Zero-Shot Recommender Systems
Authors: HAO DING
Yifei Ma
Anoop Deoras
Bernie Wang
Hao Wang
Venue: ICLR 2022 Submitted (2022)
Keywords: Zero Shot Learning
Recommender Systems
Neural Networks
Bayesian

Abstract: Performance of recommender systems (RecSys) relies heavily on the amount of training data available. This poses a chicken-and-egg problem for early-stage products, whose amount of data, in turn, relies on the performance of their RecSys. In this paper, we explore the possibility of zero-shot learning in RecSys, to enable generalization from an old dataset to an entirely new dataset. We develop an algorithm, dubbed ZEro-Shot Recommenders (ZESRec), that is trained on an old dataset and generalize to a new one where there are neither overlapping users nor overlapping items, a setting that contrasts typical cross-domain RecSys that has either overlapping users or items. Different from previous methods that use categorical item indices (i.e., item ID), ZESRec uses items' generic features, such as natural-language descriptions, product images, and videos, as their continuous indices, and therefore naturally generalizes to any unseen items. In terms of users, ZESRec builds upon recent advances on sequential RecSys to represent users using their interactions with items, thereby generalizing to unseen users as well. We study three pairs of real-world RecSys datasets and demonstrate that ZESRec can successfully enable recommendations in such a zero-shot setting, opening up new opportunities for resolving the chicken-and-egg problem for data-scarce startups or early-stage products.

Under review as a conference paper at ICLR 2022
ZERO-SHOT RECOMMENDER SYSTEMS
Anonymous authors
Paper under double-blind review
ABSTRACT
Performance of recommender systems (RecSys) relies heavily on the amount of training data available. This poses a chicken-and-ege problem for early-stage products, whose amount of data, in turn, relies the performance of their RecSys. In this paper, we explore the possibility of zero-shot learning in RecSys, to enable generalization from an old dataset algorithm, dubbed ZEro-Shot Recommenders (ZESREC), that is trained on an old dataset and generalize new one where there are neither overlapping users nor overlapping items, setting that contrasts typical cross gross-domain RecSys that has either overlapping users or items. Different from previous methods that use categorical item indices (i.e., item ID), ZESREC uses items' generic features, such as natural- languugg descriptions, product images, and videos, as their continuous indices, and therefore naturally generalizes to any unseen items. In terms of users, ZESREC builds upon recent advances on sequential RecSys to represent users using their interactions with items, thereby generalizing unseen users well. We study three pairs of real-world RecSys datasets and demonstrate that ZESREC successfully enable recommendations in such zero-shot setting, opening up new opportunities for resolving the chicken-and-egg problem for data-scarce startups early-stage products.
entirely new dataset. We develop an
INTRODUCTION
Many large scale e-commerce platforms (such as Etsy, Overstock, etc.) and online content plat- forms (such as Spotify, Overstock, Disney+, Netflix, etc) have such a large inventory of items that showcasing all of them front of their users is simply not practical. In particular, in the online content category of businesses, it is often seen that users of their service do not have crisp intent in mind unlike in the retail shopping experience where the users often have clear intent of purchasing something. The need for personalized recommendations therefore arises from the fact that not only it impractical to show all the items in the catalogue but often times users of such services need help discovering the next best thing be it the new and exciting movie or be it new music album or even a piece of merchandise that they may want to consider for future buying if not immediately.
Modern personalized recommendation models of users and items have often relied the idea of extrapolating preferences from similar users. Different machine learning models define the notion of similarity differently. Classical bi-linear Matrix Factorization (MF) approaches model users and items via their identifiers and represent them as vectors in the latent space [3 28 Modern deep-learning- based recommender systems 12, 25], which are also used for predicting top-k items given an item, learn the user-to-item propensities from large amounts of training data containing many (user, item) tuples, optionally with available item content information (e.g., product descriptions) and user metadata.
As machine learning models, the performance of RecSys relies heavily on the amount of training data available. This might be feasible for large e-commerce or content delivery websites such as Overstock and Netflix, but poses a serious chicken-and-eg. problem for small startups, whose amount data, in turn, relies on the performance of their RecSys. On the other hand, zero-shot learning promises some degree of generalization from an old dataset to an entirely new dataset. In this paper, we explore the possibility of zero-shot learning in RecSys. We develop algorithm, dubbed ZEro-Shot Recommenders (ZESRec), that is trained on an old dataset and generalize to a new one where there neither overlapping users nor overlapping items, setting that contrasts typical cross-domain

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Under review as a conference paper at ICLR 2022
RecSys that has either overlapping users items 44 38 to unseen users and unseen items becomes the two major challenges for developing zero-shot RecSys.
(6].
Naturally, generalization of RecSys
For the first challenge unseen users, build on a rich body of literature sequential recommen- dation models [12] 27: 25 2T). These models are built with sequential structure to encode temporal ordering of items in user's item interaction history. Representing users by the items they have consumed in the past allows the model to extrapolate the preference learning to even novel users who the model did see during training, long as the items these unseen users have interacted with have been seen during training. However, such deep learning models encode item via its categorical item index, i.e., the item ID, and therefore fall short predicting a likely relevant but brand-new item not previously seen during training.
This brings us to the second challenge of developing zero-shot recommender systems, i.c., dealing with unseen items. To address this challenge, ZESREC goes beyond traditional categorical item indices and uses items' generic features such as natural-language descriptions, product images, and videos as their continuous indices, thereby naturally generalizing to any unseen items. Take natural-language (NL) descriptions as an example. One can think of NL descriptions as system of universal identifiers that indexes items from arbitrary domains. Therefore as long one model is trained on a dataset with NL descriptions, can generalize to a completely different dataset with a similar NL vocabulary. In ZESREC we build on state-of oortth--rttttt pretrained NL embedding models such as BERT 8 to extract NI. embeddings from raw NI. descriptions, leading to an item ID system in the continuous space that is generalizable across arbitrary domains. For instance, in e-commerce platform, one could use items' description text; and similarly in the online content platforms, one could use movie synopsis music track descriptions to represent an item.
Combining the merits of sequential RecSys and the idea of universal continuous ID space, our ZESREC successfully enables recommendation in an extreme cold-start setting. i.e., the zero-shot setting where all users and items in the target domain are unseen during training. Essentially ZESREC tries to learn transferable user behavioral patterns in a universal continuous embedding space. For example, in the source domain, ZESREC can learn that if users purchase snacks or drinks (e.g.. 'Vita Coconut Water' with a lemonade flavor) that they like, they may purchase similar snacks or drinks with different flavors (e.g.. 'Vita Coconut Water' with pineapple flavor), as shown in the case study, Later the target domain, if one user purchase "V8 Splash' with a tropical flavor, ZESREC can recommend 'V8 Splash' with berry flavor to the user (see Fig. 4 of Sec. 4.6 for details). Such generalization is possible due to the use of the NL descriptions as universal identifiers, based on which ZESREC could easily identify similar products of the same brand with different flavors. To summarize our contributions:
We identify the problem of zero-shot recommender systems and propose ZESREC the first hierarchical Bayesian model for addressing this problem.
We introduce the notion of universal continuous identifiers that makes recommendation in zero-shot setting possible.
We provide empirical results which show that ZESREC can successfully recommend items in the zero-shot setting.
We conduct case studies demonstrating that ZESREC can learn interpretable user behavioral patterns that can generalize across datasets.
RELATED WORK
Deep Learning for RecSys. Deep learning has been prevalent in modern recommender systems (4: 19 4 32] due its scalability and superior performance. As a pioneer work, (29 uses restricted Boltzmann machine (RBM) to perform collaborative filtering in recommender systems, however the system is a single-layer RBM. Later, 34 and 19 build upon Bayesian deep learning to develop hierarchical Bayesian models that tightly integrate content information and user-item rating information, thereby significantly improving recommendation performance. After that, there are also various proposed sequential (or session-basee
29
recommender systems [12 27
2
39 [4] 31 18 22
41 25,, GRU4Rec [12] was first proposed to use gated recurrent units (GRU) 6 for recommender systems. Since then. follow-up works such as hierarchical GRU (27). temporal convolutional networks (TCN) 2], and hierarchical RNN (HRNN) (5 have achieved improvement in terms of accuracy by well meta data and control
utilizing cross-session information
27 causal convolutions
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signals 225
Another line of work focusing on building self-attention based sequential models such as SASRec 14, BERT4Rec BI] and S3Rec [41] In this paper we build on such sequential RecSys and note that our ZESREC is model agnostic, that is, it is compatible with any sequential RecSys.
RecSys focusing on training a recommender system in the source domain and deploying it in the target domain where there exist either common users or items [40, 38; 3
Pross-Domain and Cold-Start RecSys. There is a rich literature on cross-
[6]. These works are also related to the problem of recommendation for cold-start users and items, i.e., users and items with few interactions (or ratings) available during training I 20 42 2
handling cold start on both user and item with meta- mreta-leaming, however they cannot generalize across domains. In summary, prior systems are either (1) not sufficient to address our zero-shot setting where there are neither common users nor common items in the target domain or (2) unable to learn user behavior patterns that are transferable across datasets/domains. Therefore, they are not applicable to our problem of zero-shot recommendations.
There are also works (24 9
3 ZERO-SHOT RECOMMENDER SYSTEMS
In this section we introduce our ZESREC which is compatible with any sequential model. Without loss of generality, here we focus NL descriptions as a possible instantiation of universal identifiers, but note that our method is general enough to use as identifiers other content information such as items' images and videos. We leave exploration for other potential modalities to future work.
Notation. We focus on the setting of zero-shot recommendation where there are neither overlapping users nor overlapping items between a source domain and a target domain. We assume set V. of Jg items and set U, of I, users in the source domain, as well as set V of Jr items and a set 24, of I, users in the target domain. We let I I, + I and J J, + Jr: we use j V, U V. to index items and i E U UU to index users. The zero-shot setting dictates that V. n.,; V. = 0 and that Us nud 0. We denote the collection of all users' interactions as a 3D tensor (with necessary where Nmaz is the maximum number of interactions among all
zero-padding) R E Ix users. We use the subscript '=' to represent the collection of all elements in a certain dimension. Specifically, each user i has sequence of N, interactions (c.g.. purchase history) with various items denoted as R,... [R 1 | where R,,, E {0, 1) is one-hot vector denoting the t-th item user i interacted with. The same user has different user embeddings at different time t, reflecting dynamics in user interests; here denote as Uy R ? the latent user vector when user interacts with the t-th item in her history, and we use U /_Nuu IR Nmas XD (with necessary pro-ppadding) to denote the collection of user latent vectors. We denote as V, E F." the item latent vector and V - [ R/ I as the collection. For simplicity and without loss of generality, in this paper we focus on using NL. descriptions (i.e., a sequence of words) to describe items. For item j we denote its NL description as xj. and the number of words as Mf; similar to V, we let X = [x,]ş=1- With slight
notation overload on f, we denote R.(s) and R (f) the sub-tensor of R that corresponds to the source and target domains, respectively, Similarly, we also have U++), UO)) v(s), (()), ((), and (!).
Problem Setup. A model is trained using all users' interaction sequences from the source domain, i... (R... Jieu, and then deployed to recommend items for any user & E Ui in the target domain, given user i's previous history R.... which can be empty. In practice we append dummy item the beginning of each user session, so that during inference we could conduct recommend even for users without any history by ingesting the dummy item as context to infer the user latent vector. In our zero-shot setting, the model is not allowed to fine-tune tune or retrain on any data from the target domain.
Definition of Zero-shot Learning in RecSys. Our zero-shot setting includes three unique properties: (1) cold users, (2) cold items, and (3) domain gap. It is fundamentally different from previous content-based cold start as the latter setting usually satisfies either (1) or (2), but not often both.
3.1 FROM CATEGORICAL DOMAIN- -PECIIFC ITEM ID TO CONTINUOUS UNIVERSAL ITEM ID
Most models in recommender systems learn item embeddings through interactions. These embeddings are indexed by categorical domain- speciffc item ID, which is transductive and cannot be generalized to unseen items.
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In this paper, propose to item generic content information such NL descriptions and image to produce item embeddings, which can be used as continuous universal item ID. Since such content information is domain agnostic, the model trained top of it can be transferable from one domain to another, therefore making zero-shot recommender systems feasible. Based on the universal item embeddings, one then build sequential models to obtain user embeddings by aggregating embeddings of items in the user history.
Here introduce the notion of universal embedding networks (UEN) that use continuous universal embeddings to index items (and therefore users) rather than categorical ID that is not transferable across domains. We call the UEN generating item and user universal embeddings item UEN and user UEN, respectively.
3.2
MODEL OVERVIEW
We propose hierarchical Bayesian model with probabilistic encoder-decoder architecture. The encoder ingests items from user history to yield the user embedding, while decoder computes recommendation scores based on similarity between user embeddings and item embeddings.
Generative Process. The generative process of ZESREC (in the source domain) is follows:
1. For each item j:
Compute the item universal embedding: mj- fe(x)). Draw a latent item offset vector 6j - (0, Ip). Obtain the item latent vector: Vi = Ej + myj
2. For each user i:
For each time step t:
Compute the user universal embedding: na feeg (v.,1==))
Draw a latent user offset vector E., In)
Obtain the latent user vector: Uy = Eit + nit-
Compute recommendation score Say for each user-interaction-item tuple (i, j),
Suj Seottmax ( v2) and draw the t-th item for user è: Riz- Cat([S 1/
Here Sonfimax (-) is the softmax function: Soofmax (u v2) cxp(u,Iv,)/E) cxp(uI.v,).
Cat(-) is a categorical distribution. fe(-) is item UEN (see Sec. (.3), foeg(-) is user UEN (see Sec. 3.4p. i- vi, indexes the 7-th item that user interacts with. A and X are hyperpa- rameters. The latent item offset ej v m, provides the final latent item vector v, with the flexibility to slightly deviate from the content- based item universal embedding my. Similarly, the latent user offset &., ug na provides the final latent user vector u, with the flexi- bility to slightly deviate from the user univer- sal embedding n₃ę- Intuitively, e, and E.r pro- vide domain domain-specific information on top of the domain-agnone agnostic information from m, and nag In the target domain we will remove E, from v,, and Eu from u,z, which can be seen an attempt to remove the bias learned from the source domain.
Xi
AV
Vj
Av
T. 6-11
2kir
Av
Rici
iE 1E (E/T.
je [1..]]
Figure I: Graphical model for ZESREC. The item side (left) and the user side (right) share the same Xu and v's. The plates indicate replication.
Training. The MAP estimation in the source domain can be decomposed as following:
(( v (+), (+), A x, x
(( v(o))- P(U(() v(o), x= (( A.
Maximizing the posterior probability is equivalent to minimizing the joint Negative Log- Likelihood (NLL) of (+) and (*) given (*), x()), A and A-
Au
ua - fmyllys K=bii A
Ingmmn (u VV
IV fa(xx)
(I)
where Dz = (uv
([vi, )==)) and m, = fxz)). See the Appendix for a full Bayesian treatment of ZESREC using inference networks and generative networks [1]
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Inference and Recommendation in the Target Domain. Once the model is trained using source- domain data, can recommend unseen items j E V. (where 1/2 n V. = 0) for any unseen user E U (where U4 nu, 0) from the target domain based on the approximate MAP inference below:
p R (0) (0)
/ ( U
U V(A) x (P(P(C
v (())0) )dU
/ p(R v(a,, x())
(U(0))
(V())UO
Map
MAP
where 8()) denotes Dirac delta distribution. U MAP and v(t)
MMP the MAP estimate of U and
v(t) given ( which we approximate as:
(U v MAP
argmax p(U()) v(0 = =(aa((t(((()) U0,
MAP)
The reason for the approximation that ZESREC has no access to interactions R in the target domain, making the cross-entropy loss in the Eqn. disappear. The user and item latent matrices U vi(t) MAP in the target domain enable us to perform zero-shot recommendation by computing recommendation scores based on inner products and recommend item argmax, frotmax (u, (u, Vz).
As long the unseen items' NL descriptions are available, ZESREC could obtain both the unseen users' latent vectors and the unseen items' latent vectors based on the item universal embedding network. This is in contrast to previous methods that rely on catogorical domain-specific item ID,, which is not transferable as unseen items have completely different ID from items in the training set. Note that ZESREC is general enough to adapt to other data modalities such images and videos.
3.3 ITEM UNIVERSAL EMBEDDING NETWORK: m, fx(x)
The purpose of the item universal embedding network, denoted as f-)., is to extract item embeddings that are universal across domains. The network consists pretrained BERT network [8, denoted as fRERT, followed by single-layer neural network, denoted by fwv(-). Formally we have
m, fe fe(x,) [NN([BERT(xx))
(3)
where m, is the universal embedding for item j and x, is the NL description for item j. We use the embedding for the 'CLS' token from the last layer of BERT as the output of fRERT(X,). This UEN is jointly trained with the sequential model using the objective function in Eqn.[4] Note that Sww(() is necessary as need to adapt the pre-trained BERT for recommendation tasks.
3.4
USER UNIVERSAL EMBEDDING NETWORK: n; Sing ([v,1
The user UEN Sseg() (-) is built on top of the item UEN in Eqn. Specifically, given user i's interaction
sequence until time R... [R.r, we first replace it the corresponding NI. descriptions
((t) = [x; 1==11 and then fed into item UEN in Eqn. 3 obtain ((T)) = [m;, |==1 = (x, ) where item it is user i's 1-th interaction. Each vector in the sequence, my. is the universal embedding for item j. During training, we obtain item latent vector V, based on my (see Eqn. while during inference V, my (see Eqn. 2). We can then treat each Vj the input each time step of the sequential model, which gives the final user universal embedding nş feeg I==.) Note that this user UEN used during both training (Eqn. 4 and inference (Eqn. 2
EXPERIMENTS
In this section, we evaluate our ZESREC against various in-domain and zero-shot baselines on three source-target dataset pairs, with the major goals of addressing the following questions:
Q1 How accurate (effective) is ZESREC compared to the baselines?
Q2 If one allows training models using target-domain data, how long does it take for non zero-shot models to outperform zero-shot recommenders?
Q3 Does ZESREC yield meaningful recommendations for users with similar behavioral patterns in the source domain and target domain?
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Table 1: Zero-shot results on three dataset pairs, 'Amazon Grocery and Gourmet Food - 'Amazon Prime Pantry', 'Others' + 'NFL', and 'Others' + 'NCAA', Methods such as HRNN, HRNN-Meta. and POP are oracle methods that are trained directly using target-domain data. N@ 20 and R @ 20 represent NDCG@20 20 and Recal120, respectively. The top 3 zero-shot results are shown in bold.
Method
PRIME PANTRY
NCAA
1ST DAY
NCAA
1 WEEK
NFL 1ST DAY
NFL
WEEK
N@20
R@20
N@20
R@20
N68
N
RØ20
N@#
R
HRNN (ORACLE)
0.038
0.073
0.066
0.139
0.006
0.011
0.052
0.118
0.002
0.003
HRNN-META (ORACLE)
0.045
0.089
0.054
0.120
0.004
0.010
0.044
0.112
0.001
0.003
GRU4REC (ORACLE)
0.042
0.081
0.062
0.135
0.005
0.011
0.046
0.109
0.001
0.003
GRUAREC- (ORACLE)
0.044
0.088
0.053
0.118
0.004
0.009
0.037
0.094
0.001
0.003
TCN (ORACLE)
0.038
0.073
0.068
0.141
0.006
0.012
0.049
0.114
0.001
0.003
TCN-META (ORACLE)
0.045
0.088
0.054
0.120
0.004
0.010
0.044
0.109
0.001
0.003
POP (ORACLB)
0.007
0.018
0.002
0.005
0.000
0.000
0.000
0.000
0.000
0.000
EMB-KNN (BASELINE)
0.024
0.042
0.016
0.026
0.005
0.011
0.010
0.018
0.001
0.003
RANDOM (BASFLINE)
0.001
0.002
0.002
0.006
0.002
0.006
0.001
0.002
0.001
0.002
ZESREC-H (OURS)
0.027
0.052
0.027
0.063
0.011
0.036
0.007
0.013
0.015
0.043
ZESREC-G (OURS)
0.026
0.050
0.030
0.070
0.014
0.040
0.008
0.013
0.018
0.058
ZESREC-T (OURS)
0.026
0.050
0.023
0.056
0.011
0.035
0.009
0.017
0.018
0.054
4.1
DATASETS
We use three different real-world dataset pairs, one from Amazon 26 and two from MIND 37
Amazon [26]: A publicly available dataset collection which contains a group of datasets in different categories with abundant item metadata such as item description, product images, etc. In our experiments, we consider two datasets: (1) 'Prime Pantry', which contains 300K interactions, 10K items, and 76K users, and (2) "Grocery and Gourmet Food', which contains 2.3M interactions, 213K items, and 739K users.
MIND 37 A large-scale news recommendation dataset collected from the user click logs of Microsoft News. includes 4-week user history and 5th-week interactions. In our experiments, simulate zero-shot learning by leave-one-out splitting on subcategories under the largest category in MIND: 'sports'. We consider two pairs: (1) 'Others' to 'NFL', where 'Others' contains all the other subcategories except 'NFL'. 'Others' contains 169K interactions, 8K items, and 57K users, while 'NFL' contains 1M interactions, 11K items, and 203K users. (2) 'Others' to 'NCAA', where 'Others' includes all the subcategories except 'NCAA', 'Others' contains 797K interacions, 17K items, and 206K users, while 'NCAA' contains 238K interactions, 31K items, and 56K users.
We adopted a rigorous experimental setup for zero-shot learning to ensure (1) no overlapping users and items and (2) no temporal leakage. See the Appendix for details. Datasets in Amazon pair are divided into training (80%) and test (20%) sets. For datasets in the two MIND pairs, we follow the [7 and use the four-week user history as the training set and interactions in the fifth week as the test set. Due to strong recency bias in the news recommendation
official splitting
(37), for MIND we run experiments under two settings to measure temporal invariance of the model: (1) evaluate on interactions in the first day of the week (Ist-day setting), and (2) evaluate interactions of the whole week, i.e., the full test set (whole-week setting). For all datasets, We further split 10% of the training set by user validation set.
4.2 BASELINES AND ZESREC VARIANTS
To demonstrate the effectiveness of our model, we compare ZESREC against two groups of baselines: in-domain methods and zero-shot methods.
In-Domain Methods. We compare variants of our model ZESREC against variety of state-of-the
art session-based recommendation models including GRU4Rec [12]. TCN [2], and HRNN [25]. We also consider their extensions, HRNN-Meta, GRU4Rec-Meta, and TCN-Meta, which use items' NL description embeddings to replace item ID hidden embeddings. Besides aforementioned sophisticated models, introduce POP which is simple baseline conducting recommendation only based on item global popularity. It can be a strong baseline in certain domains. All the above 7 methods are trained directly on target-domain data and therefore are considered 'oracle' methods.

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0.110
0.000
0.900
0.004
3,00
T2
3.200
0.000
1620
1,000
2.3
20
2-3
0.00
0.30
(a) NDCG@@2
(b) Recall@220
(c) NDCG@22
(d) Recall @ 20.
Figure 2: Incremental training results for baselines using target domain data compared to ZESREC using training data on MIND-NCAA (left two) and Amazon Prime Pantry (right two). To prevent clutter, we only show results for TCN-based and HRNN-based -hased models, since HRNN is an advanced version of GRU4Rec. Results show that even without using target-domain data, ZESREC still outperform models trained directly using target-domain data for substantial amount of time.
Zero-Shot Methods. Since previous work has been done this thread, consider two intuitive zero-shot models (I) EnNeddingKNN: a K-nearest-neighhoor algorithm based on inner product between the user embedding and item embedding. The item embedding is the BERT embedding from NL description, while the user embedding is an average over embeddings of the user's 's interacted items, and (2) Random: recommending items by random selection from the whole item catalogue without replacement.
ZESREC Variants. We evaluate three variants of our ZESREC, including ZESREC-G, ZESREC- T, and ZESREC-H which use GRU4Rec, TCN and HRNN as base models, respectively.
4.3 IMPLEMENTATION DETAILS
We
adopted pre-trained to process item description and generate item embedding. The dimension of BERT embedding is 768.
mmncaed_L-122111 L-12_H-768 H-768 1---6--6---222111-- BERT model from Huggingface [36]
For the model architecture, we use BERT embedding as input to a single-layer neural netwrok (i.e.,
fax(-)). The output dimension for the NN is set to D (see the Appendix for more details).
4.4 EXPERIMENT SETUP AND EVALUATION METRICS
We conducted three sets of experiments to answer questions proposed earlier in this section.
Zero-Shot Experiments. We trained in-domain baselines on target domain training set, while our ZESREC is trained on source domain. All models are tested on the testing set of the target domain for an apples to apples comparison.
Incremental Training Experiments. To measure how long it takes for non-zero-shot models to outperform zero-shot recommenders, we conducted incremental training experiments on in-domain base models GRU4Rec, TCN, HRNN as well as GRUaRec-Meta, TCN-Meta, HRNN-Meta. Note that the variants of our ZESREC are NOT retrained on target domain. It is also inevitable that non-zero-shot models eventually outperform ZESREC because ZESREC does not have access to target-domain data. Due to space constraint, we only reported results for incremental training experiments on Amazon pair and MIND NCAA pair under the lst-day setting. Importantly, under the whole-week setting for the MIND dataset, the ZESRec variants already significantly outperform in-domain competitors which are trained on the full target training set; therefore is meaningless to compare incremental training results under this setting, as in-domain competitors will never beat ZESRec variants. For all the source-tare target dataset pairs, we group the interactions by user and sort interactions within each user based on interaction timestamps. We randomly select users until we get enough interactions and build three datasets containing 2.5K, 5K, and 10K interactions, respectively.
Case Studies. To gain more insight what ZESREC learns, we perform several case studies. Specifi- cally, we randomly select users from the test set of the target domain Amazon Prime Pantry (where evaluate ZESREC) and only keep users for whom ZESREC correctly predicts the 6-th items in the sequence given the first 5 items as context, as we want to focus on sequences where ZESREC works. We use these users queries to find users with similar behavioral patterns from the source domain Amazon Grocery and Gourmet Food (where we train ZESREC) based on user embeddings from ZESREC. User embeddings generated based the first 5 items of the sequence.
Simulated Online Scenarios. In the experiments, ZESRec only accesses target domain data during inference simulate online scenarios, where new businesses just open and the customers using
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Source Domalnn: Amazon Grocery and Gourmet Food
ZE5Rec Recommends
ECAIF
bounce
Monning
Lemoo Sag
CHERIIA
Energy Bars
Bounse Fabdic Softener Sheets
Blakk Tea Morning Thurder
Zingor
Flworr
Chooohaz Ship hearsl Duacc
CCosic to oundoor woer
Item form
Teetap
One 20oount to Zagerr Hertol hogs
Iegrddients
Osgars Pullcd Outa...
Organiz Brmwn Rin Sampp
Helps Racuos Vetritie
Contro static cinngt fatanca
Brand Vodeery
Coleata Sensoning:
Caffiise free
ddended with becus for tart flover
frnd
Steaa not wiler for 4.6 ranvris for the perfect oup
Target Domain: Amazon Prime Pantry
Figure 3: Case Study 1, The purchase history of user in the source domain (sop) and the purchase history of an unseen user in the target domain, where all items are unseen during training (bottom). We select two users with similar universal embeddings according to Sec. 4.4 This case study demonstrates ZESREC can learn the user behavioral pattern that 'users who bought sugary snacks and tea tend to buy caffeine-fre herbal tea later'.
service in real-time. This real-time online access setting is substantially different from batch access ahead of time as it prevents us from training recommender in the target domain before serving.
Evaluation Protocol. For evaluation, we adopted Recall (R (R@20) and the ranking metric Normalized Discounted Cumulative Gain (NDCG) (30] (N @ 20). We removed all the repetitive interactions (e.g.. user A clicked item B two times in a row) to only focus on evaluating the model's capability of capturing the transition between user history to the next item.
4.5 EXPERIMENTAL RESULTS
The experimental results three dataset pairs are in Table [ Overall, our ZESREC outperforms zero-shot baselines Embedding-Kel KNN and Random by a large margin in most cases; also achieve performance comparable to in-domain baselines. On the Amazon pair, ZESREC beats POP and achieves comparable performance with HRNN-Meta and HRNN- Interactions, suggesting the existence of shared recommendation patterns two domains. For the two MIND pairs, we made the following observations: (1) Under the whole-week setting, ZESREC consistently outperforms TRNNN-Meta and first-day results and the whole-week results, it is obvious that the in-domain models overfit the history and fail to generalize well on latest user-item interactions. This reflects strong recency bias in news recommendation. In contrast, model the transition from user history to the next item. (2) Under the first-day setting, ZESREC still achieves reasonable performance comparing with in-domain models on both pairs. (3) Surprisingly in the MIND NFL pair, the ZESRec variants perform well under the whole-weck setting even when the source domain contains much fewer interactions than the target domain (169K VS 1M).
Zero-Shot Experiments.
by a large margin. Comparing the
zero-shot learning naturally comes with an inductive bias to only
Incremental Training Experiments. Incremental training results are in Fig. 2 Overall, almost all the in-domain baselines are not able outperform ZESREC by retraining at most 10K interactions in target domain, which shows the vast importance of conducting zero-shot learning in recommender domain. For new business operating an early-stage RecSys, it's hard to train good RecSys with limited interactions. This is a chicken-and-egg problem, as training good RecSys requires sufficient interactions, while in turn, collecting sufficient interactions requires satisfactory RecSys to attract users. Therefore the first 10K interactions are crucial to get the RecSys started.
4.6
CASE STUDIES
To gain more insight, we randomly select pairs of user purchase histories from the source and target domains in the Amazon pair case studies according to the procedure in Sec. 4.4 The goal is
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MIA
VTTA
COC
12
Vita Coco Coconut Water, Lemonade
Vita Coco Coconut Water with
Vita Coco Coconut Water, Tongerine
Vita Coco Coconut Watter, Pineopple
Acai Pomigranate
Taste the tropics
Pock 17
204
ing redients. Purified Water
Natural hydiation
Conveni sealable socks
Convenient sealible
SSiletyy warning product labelled to
Refr yourself
tumpen-endent
Unite States standards and may difice from
Fat free, Glvten and SM10
Ensrronment fiiandly pocks Serve chilled or mind a cocktai
Erddronmen friendly tetra
simillr products sold checwhere
12 pack 26.9 cunce bottles
-Snvee chilled mixad cocktall
ingred
allergen wornings
Source Domain: Amazon Grocery and Gourmet Food
Cornation
ZE5Rec Recommends
Nestle Carnation Vitamin Added Eveporated Milk
Mug Root Beer Cans (12 Count, Fl Each)
VS Splash Tropical Bland,
V8 Splash Berry Blend, 64 oz. Bottle
oz. Bottle
Ingredients
Mi
Haver
Root Beer
rondan E. mb
Arriooldan skamins & calones per serding
Brund
Carnation
Ingredients
Carbonoted Water...
caloris per serving
Weight
15.3 Guroes
Brand
Free
flavors
Cluten Free
DDlicious fruit flavors whole farilly can enjry
Description
(50m)
Serving Description
1 cam[( (1
Contains juloes
Proteis
2 Grams
whole fumily onjoy
Target Domain:
Amazon Prime Pantry
Figure 4: Case Study 2. The purchase history of a user in the source domain (top) and the purchase history of an unseen user in the target domain, where all items are unseen during training (bottom). We select two users with similar universal embeddings according to Sec. 4.4 This case study demonstrates ZESREC can learn the user behavioral pattern that 'if users bought snacks or drinks that they like, they may later purchase similar snacks or drinks with different flavors'.
to demonstrate our ZSR could learn relevant dynamics of users' purchase history from the source domain and successfully recommend unseen products to unseen user in the target domain.
Fig. shows the purchase history of a user in the source domain (top). 'Amazon Grocery and Gourmet Food', and the purchase history of unseen user in the target domain (bottom), 'Amazon Prime Pantry', where all items are unseen during training. The user in the source domain bought "Tension Tamer Tea', which is type of herbal tea, after buying some sugary snacks (KitKat) and other tea. Such a pattern is captured by ZESREC, which then recommended 'Lemon Zinger Herbal Tea' to an unseen user after she bought some sugary snacks ( ('Energy Bars from Clif Bar') and some black tea. This case study demonstrates ZESREC can learn the user behavioral pattern that 'users who bought sugary snacks and tea tend to buy caffeine- free herbal tea later'. More interestingly, another case study in Fig. deemonstraees that ZESREC can learn the user behavioral pattern that 'if users bought snacks or drinks that they like, they may later purchase similar snacks or drinks with different flavors'. Specifically, in the source domain, the user purchased "Vita Coconut Water' with four different flavors; such a pattern is captured by ZESREC. Later in the target domain, an unseen user purchase 'V8 Splash' with a tropical flavor, ZESREC then successfully recommends *V8 Splash with a berry flavor to the user.
5 CONCLUSION
In this paper, we identify the problem of zero-shot recommender systems where a model is trained in the source domain and deployed in a target domain where all the users and items are unseen during training. We propose ZESREC as the first general framework for addressing this problem. We introduce the notion of universal continuous identifiers leveraging the fact that item ID can be grounded in natural-language descriptions. We provide empirical results, both quantitatively and qualitatively, to demonstrate the effectiveness of our proposed ZESREC and verify that ZESREG can successfully learn user behavioral patterns that generalize across datasets (domains). The main limitation of this work is that commonality between domains is crucial for ZESRec, as is the case for most other works in transfer learning. Future work includes quantification of such commonality and exploring other modalities, e.g., images and videos, as alternative universal identifiers. It would also be interesting to investigate the interpretability provided by the pretrained BERT model and to incorporate additional auxiliary information to further improve the zero-shot performance.
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A APPENDIX
A.1 DATA USAGE AND PRIVACY DISCUSSIONS
Data Usage: For real-world scenarios, new business owners who hope to do zero-shot recommenda- tion need to check the dataset policy before usage. On the other hand, if multinational corporations hope to establish branch in new region they could use their own data from other existing regions.
Privacy: To protect the privacy of user data, we encourage people who want to adopt our methods to train the model using source-domain data with differential privacy embedded. Some related references include 5 35
A.2 BAYESIAN TREATMENT WITH INFERENCE NETWORKS
Training. Using Jensen's inequality we have the following evidence lower bound (ELBO):
log pl (R|X, Au, Ao) 2 Fgy P(o, U, VJ, Au, Aw)] - Flglog (U, V[X)),
where the expectation is over (U, V(X). We have
L N.
A. J. fec (ev
log(t Frommnn ( (1, v.,))
A.
Σ
Iue
2 Σ llv,
fx;
1=1
(4)
For the variational distribution qUU, VIX) we have the following factorization:
QUU V X)= = q [V[X)] x) (UV),,
where = =[u;]/.), V = [vy] and x = [x,1
a(v, X) <(vs/2g)) N N(v3 Se, Se,0z (xz)) quu V. X) .V N(u:: Saeg, ( (( yau
( frng ( (LV }
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Overall,
( R|x, , Aus An) 2 Fg [ooog(RR, U, V X, Xu, (o)) Fiy g( V[x)]
= Egg
log (og(Ssun Ssopomax ( (u/3 ve.))l
= k=1
+ E,y Egllog(([[X)) - Eg (U|V) - Egllog (U, v[x)] + +C
A Σ (x)))=
Egg
log(Ssofummn (u vi,)))
2
+ Eyllog [.[log((U[V]] - (, V|X] +C,
where the expectation is over QU, V))). Below we discuss several important terms including - -Fy (U, V] and V (log pl ([]) in the ELBO.
The term llog (U, v[x)] is the entropy of (U, V(X).
(xx (oogi (UU V[X)] = E ( X + log [(U]))
PoV ol VXX Buyyy log V)]
= (OIVI))(oB/(V/X)) ) [
2 En log 2x,) Σ B
1 log I Sug (1vi, [==1) +
NC ΣΣ
1 En log 2x
Σ
log I oz ((vi, }イ= + C.
2N.
where V is sampled for N₂ times get Monte Carlo estimate of ( Dog o2 Vi, ) )} In practice, is found that one sample is usually sufficient due to the use of SGD-based optimization process.
Similarly the term
EgeV x) (lgg(
Au Ex(Vix)
I
N 1a } + +c
2
N
An
1s ((vi, |=21 + C,
2N,
where V is sampled for N, times from (V(X) to get a Monte Carlo estimate of Eg(Vix)) x x oeg. )> In practice, it is found that one sample usually sufficient due to the use of SGD-based optimization process.
Inference. Inference be done via Monte Carlo estimates of p(R|X, Au, Av). Specifically,
p(R An A,) = E, V)) N.N. Σ Σ ((R[U(() v("),,
V(v) U(a)
where (n) V X) and U q (()) X).
One could also use MAP inference to trade accuracy for speed.
X(R[X)= = f (R]U V, X)P(U, V[X)dUdV ~ / p(R]U, V. (U dV)dUdV
where 5(.) denotes Dirac delta distribution. UMAP and VMAP the MAP estimate of U and V given X:
(UMAP, V MAP) argmax (U, v/x) (f
U.V
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A.3 IMPLEMENTATION DETAILS
We use pre-trained ggogle/ber__uccsed_L--- process item description and generate item embedding. The dimension of BERT embedding 768. We use BERT embedding as input to single-layer neural netwrok (NN) and the ouiput dimension for the NN is set to D, which equals to the hidden dimension of the sequential model.
BERT model from Huggingface [36] to
For ZESREC variants we use the default optimal setting: we set the hidden dimension D as 300, the dropout rate 0.2, and the number of training epochs as 20. We use Adagrad 7 as the optimizer with learning rate of 0.1, and train ZESREC variants in the source domain with early stopping based on validation loss. We set the hyperparameter X, as a relatively large value 100 to restrain variance of the item offset vector Ej.
For base models (HRNN, TCN, GRU4Rec) and corresponding base-meta models (HRNN- Meeta TCN-Meta, GRUARec-Meta) we set the dropout rate as 0.2 and the number of training epochs 20; we choose Adagrad 7 as the optimizer. We train base models and base-meta models in the target domain with early stopping and perform hyperparameter tuning, both based on the validation loss. We tried the hidden dimension in [128, 300} and the learning rate v in {0.01, 0.1, 1}, and choose the configurations {D 128, rj }} for base models and {D 128, 1 0.13 for base-meta models.
For all datasets, treat the rating implicit feedback (intcractions between user and item). Since we are considering session-based recom- mendation and using sequential model, we filter out users with only interaction as the sequen- tial model need ingest at least one item from user history as context to perform next-step pre- diction.
XL+
Av
Vj
Vir
A
s. 1R, 6-1)
An
No Temporal Leakage. We adopted rigor- experimental setup for zero-shot learning: Firstly, we makes sure that there are no overlap- ping users and items between the source domain and the target domain. Secondly, we temporally split the domains, meaning that all the train- ing interactions in the source domain must be happened before all the testing interactions in the target domain.
Ruj
Oset vev!
(E tc [C.T.N.)
|E [E
Figure 5: Graphical model for ZESREC. The item side (left) and the user side (right) share the same Xu and v's. The plates indicate replication.
All experiments were ran on GPU machine with Nvidia Tesla V100 16G memory GPU.
A.4 MODEL ARCHITECTURE
Prediction Score
Latont Item Cition Vector
Lutert Ciffee Vedta
Lakert Vectio
n.29
Labert Frim
0.09
(fffuet
Oritue Mentioe
0L00
Temm Rnberren Embeorinng
Embbolin
Embbaldngg
Enbodding
Einboldin
1-4 14 104
Tol NN
T-laper NN
Layor KN
Pictroined BERT Model
Pr Protalned BERT Model
Fresmined BERT Model
Freiniree
Figure 6: Model Architecture
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The graphical model for ZESREC is shown in Fig. 5 In the MAP estimation version of ZES- REC, we set A - 3o to remove latent user offset vector, thereby preventing ZESREC from over-parameteriiation. Fig. showss simplified deterministic model architecture from the neural network point view. Below we elaborate on the process in terms of two stages: training in source domain and inference in target domain.
Training in Source Domain, During training, on the encoder side, we generate BERT embeddings from items' NL descriptions as item universal embeddings and then add the learnable item offset vectors to them, which yield the final item embeddings (item latent vectors). The sequential model will aggregate item embeddings of items in user history generate user embeddings. On the decoder side, we compute item latent vectors the same way we do on the encoder side. Here we share item latent vectors on both encoder and decoder sides to reduce number of parameters. Empirically we find this prevent overfitting and improve performance.
Inference in Target Domain. In this phase, we will remove the item offset vectors on both the encoder and decoder sides. We use item universal embeddings directly instead as the final item latent vectors since we have no access to interactions in the target domain, and therefore the model is unable to estimate the item offset vectors.
15


Reviews:
[{"review_id": "fOvHt2ZAQw", "reviewer": "Reviewer_cG8S", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Pros: \n1. This paper is well motivated. The problem of zero-shot recommendation is interesting yet very challenging to solve.\n2. The proposed approach with universal item embedding combined with sequential recommendation methods for user embedding levering the items that user have interacted with is a novel and reasonable approach for solving this problem. \n3. The paper demonstrate the effectiveness of the proposed approach using real-world dataset and experiments. The questions in Section 4 that the experiment tries to answer are informative. The experiment set up is solid with following both the non-overlapping as well as temporal aspect to it.  \n4. It is good that the paper demonstrates the proposed framework using multiple base sequential models. \n\nCons:\n1. The literature survey does not cover a lot of more recent approaches from sequential recommendations. Although the proposed approach is model agnostic, it would still be good a provide a thorough literature review. Same for the baselines compared are not as strong. Some examples papers (there are more) are below:\nSun, Fei, et al. \"BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer.\" Proceedings of the 28th ACM international conference on information and knowledge management. 2019.\nKang, Wang-Cheng, and Julian McAuley. \"Self-attentive sequential recommendation.\" 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018.\nZhou, Kun, et al. \"S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization.\" Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020.\n2. It would be good to add some discussions in terms of how easy/hard the proposed approach can generalize to other modalities such as video, image, attributes. Does these modality also share the commonalities that language use?\n3. What is the sequential model used for 3.4 user universal embedding network after getting each time steps embedding for each item?\n4. In figure 2, when saying the ZSESRec do not directly use the target-domain data, do you use the interaction histories of users. If so, it will be consider to be as using the target data. If not, how is user represented, aren’t all uses will get the same results if no item is recommended?\n5. The limitations in terms how close the source and target domain needs to be are not discussed in the paper. \n\n", "summary_of_the_paper": "A great recommender system relies on great training set. However, at the beginning, there is no such data availability. This paper tries to solve the zero-shot recommendation problem where there is no user or item overlaps. The two challenges are generalize to unseen users and to unseen items. For unseens users, sequential recommendation represents users as a sequence of their interacted items. As long as the items are seen before, the users can be represented. As for items, the unique ids are not useful. However, the attributes such as natural language description can be universal. This paper proposed an approach based on hierarchical Bayesian model. The item universal embedding is using pre-trained BERT network with a single layer neural network. Extensive experiments are carried out to demonstrate the effectiveness of the proposed approach. \n", "summary_of_the_review": "Overall, the problem being studied is interesting and challenging. The approach proposed in this paper make sense. The experiments questions and design are good. Many major concern is intermittent question 4 in Main Review. Please clarify more for the authors. There are some areas for improvement like baselines, literature review, lack of discussion about . \n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "MssmJmJRqf", "reviewer": "Reviewer_hJB8", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "This paper studies \"zero shot recommendation\", where there is no overlap between the source domain and target domain in terms of user id and item ids. This is different from the recent cross-domain recommendations where overlapping items are leveraged. The main idea is to use item content features that can be generalizable, such as using BERT over item descriptions. Users are represented based on the items that were interacted by them. Experiments are conducted on two offline datasets, where variants of the proposed methods outperform some in-domain only methods and simple cross-domain baselines (such as random and direct embedding matching). Some case studies are provided.\n\nStrength\n- The paper is clearly written and the authors clearly communicates what has been done\n\nWeakness\n- The major issue of this paper is novelty. The reviewer agrees that the \"zero shot recommendation\" is probably an interesting future direction, but the way that this problem is tackled in this paper makes it not different from known problem settings. More specifically, the paper uses content-based methods instead of ids. Content-based recommendation is as old as id based recommendations, if not older. Content-based recommendations is the default solution for cold-start problems and is commonly used together with ids in practice. The method proposed in this paper is not different from existing content-based recommendation paradigm:\n1. The model is learned on source domain and directly applied to target domain. There are no tuning on target domain (e.g., by using some unsupervised methods). The important assumption here is, the source and target domain are actually still *in-domain*, otherwise the model will just fail since nothing is done in the target domain. Then it is not really different from content-based methods for cold start problem, which has been studied for decades. The reviewer understands for real-world applications that are different problems, but in terms of machine learning (the focus of this conference), the reviewer does not see novelty. Actually, in terms of applications:\n2. One motivation to do \"zero shot recommendations\" is that small new websites do not have enough data. However, the paper assumes that users in the target domain has interactions available (otherwise there will be no user representation). This is confusing since the reviewer does not understand if this is really zero-shot or not. Many recommendation papers use such datasets so it is a standard data setting. Furthermore, the experiments are conducted on two offline datasets and the authors need to do a bunch of data massaging to make it \"zero shot\". This makes the reviewer wonder how valuable this setting is - if user interactions are already available in the target domain, why \"zero shot\" is needed? Also, ideally there should be real-world use cases to show this setting can really benefit new websites.\n\nOverall, the reviewer recommends the authors to think carefully what \"zero shot recommendation\" is a novel meaningful setting and why the proposed methods in this paper has any meaningful novelties. Currently the problem setting and proposed method do not seem to really show novelty, regardless of the bar of ICLR.\n\nReply to rebuttal:\nThe reviewer acknowledge the response but not convinced.\n\n1. The authors argue that \"Our zero-shot setting is fundamentally different from content-based cold start\". \n\n- The reviewer understands the difference of the setting and mentioned in the original review that this setting can have values (if tackled concretely). The reviewer's point is, the proposed method makes assumptions that make the \"new setting\" work only under scenarios that is virtually the same as cold start or cross-domain recommendation. More specifically, only inference is done on the target domain without any adjustment. This will clearly fail in many cases. The authors keep using claims such as \"completely different\", \"extreme cold-start\" - but what would happen, for example, if the model learned on MIND dataset is applied to Amazon datasets? How would the owner of a new website decide which dataset to use? How would they get such datasets? \n\n2. The authors argue that \"Our setting is zero-shot because the target domain data is not used during training, but only during inference to simulate online scenarios, where new businesses just open and the customers are using service in real-time. What the reviewer refers to is batch access ahead of time, which is distinct from our online access case.\" The authors also argue that \"Previous initial-phase recommenders can only collect low-quality interactions\" and \"Initial-phase recommenders take much longer time to collect data because inaccurate recommendations are not appealing to users.\" The authors referred to Figure 2, the \"incremental training\", to argue for effectiveness.\n- The reviewer finds some arguments and the incremental training experiments confusing. First, data used to train recommender systems are not necessarily from recommendation UI. It is common practice to just use \"interaction\" data, regardless of where it comes from. In fact, one can argue that interactions not from the UI do not possess many kinds of biases and are of higher quality. Second, Figure 2 is not really measuring what the authors are arguing about. Noticeably, the proposed methods have constant performance over the time period (while other methods get better as more training data is available). How could the proposed method work well when there are no interactions (as its inference depends on user's past interactions)? The reviewer notices that the test set is on *later date* (i.e., week 5 for MIND) and the proposed method *already leverages previous interactions* in first 4 weeks during inference. Again, I understand it is not trained to update any parameters, but think what would happen in practice - such interaction data is available anyway (for the proposed model to do inference). a) The performance in day1 or week1 is not measured, which is what the authors are really arguing about. On the real day1 without any interactions, the proposed method will generate nothing (or the same set of items to all users). Does it really help bootstrapping a new website? 2) If the proposed method has first 4 weeks data for inference, then other methods should use them for training - it is just the proposed method can't train with them. So the reviewer feels the experiments are not fair under the current setting. Please let me know if I misunderstood anything in terms of the experimental setting.\n\n\n", "summary_of_the_paper": "This paper studies \"zero shot recommendation\" where source and target domain have no overlap in terms of user and items. The paper proposes to use item content features, such as leveraging BERT on descriptions, instead of IDs. Experiments are conducted on two offline datasets.", "summary_of_the_review": "Though the paper is well written, the reviewer finds it difficult to believe the \"zero shot recommendation\" is a novel setting and the proposed methods have differ from content traditional content-based recommender systems in meaningful ways. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "H0Wj-8i9iY", "reviewer": "Reviewer_zQRZ", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n\n1 The authors identify a new and interesting problem called zero-shot recommendation.\n\n2 The proposed zero-shot recommendation method is simple and generic.\n\nWeakness:\n\n1 The idea is a bit too straightforward, i.e., using the attributes of the items/users and their embeddings to bridge any two domains.\n\n2 The technical contribution is limited, i.e., there is no significant technical contribution and extension based on a typical model for the cross-domain recommendation setting.\n\n", "summary_of_the_paper": "In this paper, the authors identify a new and interesting problem called zero-shot recommendation, where there is no overlap of users or items between a source domain and a target domain. The main idea is to bridge two domains via the attributes of the item and the users, which is called continuous universal item ID and user ID.", "summary_of_the_review": "In this paper, the authors identify a new and interesting problem called zero-shot recommendation, where there is no overlap of user or items between a source domain and a target domain. The main idea is to bridge two domains via the attributes of the item/users, which is called continuous universal item/user ID.\n\nThe authors conduct experiments and show that the proposed zero-shot recommendation framework work well. \n\nOverall, the paper is well presented. The studied problem (zero-shot recommendation) is interesting.\n\nHowever, my main concern is that the idea to bridge two non-overlap domains via attributes is too straightforward, and the technical contribution is limited.\n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The authors propose zero-shot recommendations, a scenario in which knowledge from a recommender system enables a second recommender system to provide recommendations in a new domain (i.e. new users & new items). The idea developed by the authors is to transfer knowledge through the item content information and the user behaviors.

The initial assessment of the reviewers indicated that this paper was likely not yet ready for publication. The reviewers all recognized the potential usefulness of zero-shot recommendations but argued that the implications of the proposed setup were somewhat unclear. Most notably, the reviewers raised the issue of how widely applicable this was in terms of distance between source and target domains (presumably the quality of the zero-shot recommendations depends on the distance). 

The reviewers also noted that this was an application paper. This is of course within the CFP, and recommender systems papers have been published at ICLR in the past (for example one of the initial Session-based RecSys paper w. RNNs) but the potential audience for this work is somewhat lower at ICLR. I should also add that I agree with the authors that their model is novel, but it's very much tailored to this application and it was unclear to me how it might be impactful on its own. All in all, this did not play a significant role in my recommendation.

During the discussion, there were significant, yet respectful, disagreements between the authors and the reviewers. It also seems like perhaps the authors missed an important reply from reviewer hJB8 made available through their updated review (see "Reply to rebuttal"). So the discussion between reviewers and authors did not converge. Having said that, even the two most positive reviewers have scores that would make this paper a very borderline one (a 6 and a 5). 

Further, I do find that reviewer's hJB8 arguments have merit and require another round of review. In particular, I think the role and effect of your simulated online scenario should be further discussed (note that I did read the new paragraph on it from your latest manuscript). For example, comparing to a baseline that can train with the data from this new domain would be useful even if at some point it ends up being an upper bound on the performance of your approach. I also found the question raised by the reviewer around the MIND results to be pertinent. Further characterizing pairs of domains in which the approach/works fails (even if empirically) would add depth to this paper. 

All in all, this paper has interesting ideas and I strongly encourage the authors to provide a more thorough experimental setup that fully explores the benefits and limitations of their zero-shot approach.
Decision: Reject