Collaborative Generated Hashing for Market Analysis and Fast Cold-start Recommendation
Authors: Yan Zhang
Ivor W. Tsang
Lixin Duan
Guowu Yang
Venue:  (2020)
Keywords: Recommender system
generated model
market analysis
hash
cold start

Abstract: Cold-start and efficiency issues of the Top-k recommendation are critical to large-scale recommender systems. Previous hybrid recommendation methods are effective to deal with the cold-start issues by extracting real latent factors of cold-start items(users) from side information, but they still suffer low efficiency in online recommendation caused by the expensive similarity search in real latent space. This paper presents a collaborative generated hashing (CGH) to improve the efficiency by denoting users and items as binary codes, which applies to various settings: cold-start users, cold-start items and warm-start ones. Specifically, CGH is designed to learn hash functions of users and items through the Minimum Description Length (MDL) principle; thus, it can deal with various recommendation settings. In addition, CGH initiates a new marketing strategy through mining potential users by a generative step. To reconstruct effective users, the MDL principle is used to learn compact and informative binary codes from the content data. Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze the feasibility of the application in marketing.

Under review as a conference paper at ICLR 2020
COLLABORATIVE GENERATED HASHING FOR MAR- KET ANALYSIS AND FAST COLD-START RECOMMEN- DATION
Anonymous authors Paper under double-blind review
ABSTRACT
Cold-start and efficiency issues of the Top-k recommendation are critical large- scale recommender systems. Previous hybrid recommendation methods are effec- tive to deal with the cold-start issues by extracting real latent factors cold-start items(users) from side information, but they still suffer low efficiency in online recommendation caused by the expensive similarity search in real latent space. This paper presents collaborative generated hashing (CGH) to improve the ef- ficiency by denoting users and items as binary codes, which applies to various settings: cold-start users, cold-start items and warm-start ones. Specifically, CGH is designed to learn hash functions of users and items through the Minimum De- scription Length (MDL) principle; thus, it can deal with various recommendation settings. In addition, CGH initiates a new marketing strategy through mining potential users by generative step. To reconstruct effective users, the MDL prin- ciple is used to learn compact and informative binary codes from the content data. Extensive experiments on two public datasets show the advantages for recommen- dations in various settings over competing baselines and analyze the feasibility of the application in marketing.
INTRODUCTION
With the explosion of e-commerce, most customers are accustomed to receiving a variety of rec- ommendations, such as movies, books, news, or hotels they might be interested in. Traditional recommender systems just recommended items that are similar to what they liked or rated in the previous, Recommendations help users find their desirable items, and also creates new revenue op- portunities for vendors, such Amazon, Taobao, eBay, etc. Among them, one of the most popular recommendation methods, collaborative filtering is dependent on a large amount of user-item in- teractive information to provide accurate recommendation. However, most of new e-commerce vendors do not have enough interactive data, which leads to low recommendation accuracy, i.e., cold-start issues.
Previous studies on cold-start issues generally modeled as a combination of collaborative filter- ing and content filtering, known hybrid recommender systems. Specifically, they learned real latent factors by incorporating the side information into the interactive data. Such as Collabora- tive Deep Learning (CDL) (Wang et al. 2015). Visual Bayesian Personalized Ranking (VBPR) (He & McAuley 2016), Collaborative Topic modeling for Recommedation (CTR) (Wang & Bleij 2011), and the DropoutNet for addressing cold start (DropoutNet) Volkovs et al., 2017))
ABCPRec for Bridging Consumer and Producer Roles for User-Generated Content Recommenda- tion (ABCPRec) ) ('suuuud al., al, 2019). All of the above hybrid recommender systems were modeled in real latent space, which leads to low efficiency for the online recommendation with the increasing scale of datasets.
Recent studies show the promising of hashing based methods to tackle the efficiency challenge by representing users and items with binary codes (Zhang et al., 2014: Zhou & Zhal 2012 Zhang et al.) 2016 Liu al. 2019), because the preference score can be represented by the Hamming distance calculated via XOR operation efficient (Wang et al. 2014). However, the existing hashing based recommendations are learning-based frameworks, which leads to Na-hard problems of optimizing

---

Under review as a conference paper at ICLR 2020
discrete objectives. Thus many scholars learned binary codes by some approximate techniques, such as the two-stage hashing learning method utilized Preference Preserving Hashing(PPH) (Zhang et al. 2014) and the Iterative Quantization(ITQ) (Zhou & Zha 2012). To reduce information loss, Two learning-based hashing frameworks: bit-wise learning and block-wise learning were respec- tively proposed in hashing based recommendation frameworks (Zhang al. 2016 Wang et al.)
2019 Zhang et al. 2018 Zheng et )
However, due to the requirement of binary outputs for learning-based hashing frameworks, the train- ing procedure is expensive for large-scale recommendation, which motivates us to propose a gen- erative approach learn hash functions. In this paper, we propose the collaborative generated hashing(CGH) to learn hash functions of users and items from content data with the principle of Minimum Description Length (MDL) DDa et al. 2017).
In marketing area, mining potential customers is crucial to the e-commerce. CGH provides a strategy to discover potential users by the generative step. To reconstruct effective users, uncorrelated and balanced limits are imposed to learn compact and informative binary codes with the principle of the MDL. Especially. discovering potential customers vital to the success of adding new items for a recommendation platform (Papies et al. 2017). Specifically, for new item, we can generate new potential user by the generative step (detailed in Section 2.I). and then search the nearest potential users in the user set. By recommending in but didn't 't plan to buy, further e-commerce strategies can be developed to attract those potential users.
product to the potential users who might be interested
We organize the paper as follows: Section 2 introduce the main techniques of CGH. We first in- troduce the framework of CGH and compare with the closely related competing baselines: CDL [Wang et al. 2015) and DropoutNet (Volkovs et al. 2017); we then formulate the generative step in Section 2.1 and the inference step Section 2.2 respectively; we finally summarize the training objective and introduce the optimization in Section 2.3 Particularly, we demonstrate the process of mining potential users for the marketing application Section 2.1 Section 30 presents the experi- mental results for marketing analysis and recommendation accuracy in various settings. Section 4 concludes the paper.
The main contributions of this paper are summarized as follows:
(1) We propose the Collaborative Generated Hashing (CGH) with the principle of MDL to learn compact but informative hash codes, which applies to various settings for recommen- dation.
(2) We provides marketing strategy by discovering potential users by the generative step of CGH, which can be applied to boost the e-commence development.
(3) We evaluate the effectiveness of the proposed CGH compared with the state-of-the-art base- lines, and demonstrate its robustness and convergence properties on the public datasets.
2 COLLABORATIVE GENERATED HASHING
The framework of the proposed CGH is shown in Fig. [cc. where U, V and R are respectively observed user content, item content and rating matrix. B and D are binary codes of users and items, respectively. CGH consists of the generative step marked as dashed lines and the inference step denoted by solid lines. Once training is finished, we fix the model and make forward passes to obtain binary codes B and D through the inference step, and then conduct recommendation. For the marketing application, we create a new user via the generative step.
In comparison of CGH with the closely related baseline CDL (Wang et al., 2015). the proposed CGH aims to learn binary codes instead of real latent vectors P and Q due to the advantage of hashing for online recommendation; plus the CGH optimizes objective with the principle of MDL, while CDL optimized the joint objective of rating loss and item content reconstruction error. In comparison of CGH with DropoutNet (Volkovs et al.] 2017). CGH can be used as marketing strategy by discovering potential users; plus CGH learns hash functions by stacked denoising autoendoer, while DropoutNet obtained real latent factors by the standard neural network.
2

---

Under review as a conference paper at ICLR 2020
In the following start by first formulating the generative process and demonstrating the appli- cation marketing area; we then formulate the inference step; we finally summarize the training objective and the optimization method.
P
P
B
R
R
R
V
Q
D
(a) CDL
(b) DropoutNet
(c) CGH
Figure Differences between CDL, DropoutNet and our proposed CGH. Solid lines and dashed lines respectively represent the inference (encoding) and the generative (decoding) process. The shaded nodes U, V. R are observed user content, item content and rating, respectively. P(B), Q(D) denotes real latent factors(binary codes) of users and items.
2.1 MINING POTENTIAL USERS
Give sparse rating matrix and item content data V Rd",, where d, the dimension of content vector, and V is stacked by the bag-of- bag-of-words vectors of item content in the item set V. most previous studies were focus on modeling deterministic frameworks to learn representations of items for item recommendation, such CDL, CTR, DropoutNet, et.al. In this paper, discover new strategy from a perspective of marketing for item recommendation mining potential users.
We demonstrate the process of mining potential users by item through the generative step in Fig. 2 After the inference step, the binary code of item j is available. By maximizing the similarity function ö(b;, dl) (detailed in Section 2.1). D. the optimal binary code b, is obtained. Then we generate the new user up through the generative step. Finally we find out potential users from the user set by some nearest neighborhood algorithms, such as KNN. As marketing strategy. it can discover potential users for both warm-star start items and cold-start items. Thus, from perspective of marketing, can be regarded another kind of item recommendation.
New user
Potential users
John
Nina
Leo
2
1
0
2
max 8 8b, d
Pa
u
b
0
KNN
0
1
0
1
b
1
:
:
:
0
0
1
U
1
0
1
d
b p
1
2
1
u
Figure 2: Demonstration of mining potential users for an item j. After the inference step, dy is available, first find the most similar binary code by: then generate potential user Up by the generative process; we furtherly search the top-k nearest potential users from the user set by some nearest neighborhood algorithms (c.g.. KNN).
The generation process (also referred as decoding process) is denoted by dashed lines in Fig. (c). Fix binary codes b, and d, of the user i and the item j. the bag-of-words vector u, of the user i (v, of the item j) is generated via p(0.). The ratings Tı is generated by b, and d,. We use a simple Gaussian distribution to model the generation of u, and v, given b, and d, like Stochastic Generative Hashing (SGH) (Dai et al. 2017):
(.)) = NcC,bi,Auu11 p(u,(d;) NCC.dd,, A.)),,
(1)
3

---

Under review as a conference paper at ICLR 2020
where Cuk [Cublli=1 Cuk E R is the codebook (Dai et al. 2017 with r codewords, which is similar for Cu, and d, is the dimension of the sag-offthh--word vector of users. The prior is modeled the multivariate Bernoulli distribution hash codes: p(b,) (puu). and p(d,) B(po), thus the prior probability are as follows:
p(b.) II
k=1 Pui (1 -Pp)) Pui)=-bes
p(d,) II
deh k=1 Poj (1 - Pug) 1
We formulate the rating with the similarity between binary codes of users and items like the most successful recommender systems, matrix factorization (Koren et al. 2009). Then the rating is thus drawn from the normal distribution centered the similarity value,
p(ri) bb, dj) ~ (V dz),, Cij'),
(3)
where 5(b, d,) = 1 1- Hamdis b, d,) denotes the similarity between binary codes b, and dy. Hamdis( b, d, represents Hamming distance between the two binary vectors. which has been widely applied in hashing-bas based recommender system (Wang & Blei] 2011]2001]]]0 [ et al.) 2017 2017|/Zaan Zhang al. 2018). C2 the precision parameter that serves as confidence for rúj similar to that in CTR Waang & Blei 2011) (Cij if Tij and Cij b otherwise) due to the fact that Tig means the user i is either not interested in item j or not aware of it.
With the generative model constructed, the joint probability of both observed ratings, content vectors and binary codes is given by
PR,UU, B.
II
p(ryylb,
2.2 CONSTRAINTS ON BINARY LATENT VARIABLES
The inference process (also referred as encoding process) shown the Fig. (c) with dashed lines, the binary latent variables b, (dj) depends the content vector u, (v;) and the rating R (shadowed in Fig IT. Inspired by the recent work on generative hashing (Dai et al. 2017) and DropoutNet
(Volkovs et al. (017)), use a multivariate Bernoulli distribution to model the inference process of b, and dj with linear parametrization, i.e.,
q(b, (ú;) B(o T ū.))
(S)
q(d, (,)) B[a T 5,));
where ū; Uis p.]. 83 = [0,, qil- P: and qi are the results of r-dimension matrix factorization Koren et al. 2009 of R. i.e., Tay 2 pt 9T- To = [Lu Lux E Rd.+r, To = [buk Ir 1= bos E
pdvtt are the transformation matrices of linear parametrization. From SGH (Dai et al. 2017). the MAP solution of the eq. (5) is readily given by
b. argmax q(b. ([bb)u,) sign (TTT" u.)) (TTuu,) +
b,
2
(6)
d, argmax gdd)/1;)= sign(T."'o v,) ) +
d;
2
With the linear projection followed by sign function, we can easily get hash codes of users and items. However, hashing with a simple sign function suffers from large information loss according to (Zhang et al., 2016), which motivates us to add constraints on parameters in the inference step.
To derive compact and informative hash codes for users and items, we add balanced and uncorrelated constraints in the inference step. The balanced constraint is proposed to maximize the entropy of each binary bit (Zhou Zha 2012), and the uncorrelated constraint makes each bit is independent of others. Then we can obtain compact and informative hash codes by the following constraints,
Balanced constraint: Σbi bik 0, Edi =0
Uncorrelated constraint: b,b = L, d, = Ir
4

---

Under review as a conference paper at ICLR 2020
From the eq. ( b, and d, are only dependent on parameters Tu and Tu respectively, thus we add constraints on Tu and To directly. So eq. is equivalent to the following constraints,
Balanced constraint: T 1, = 0, T. 1, =
Uncorrelated constraint: T.T
drtr T Ldytr.
By imposing the above constraints in the training step, compact and informative hash codes can be obtained through the inference process. Next we summarize the training objective and its optimiza- tion.
2.3 TRAINING OF CGH
Since our goal is to reconstruct users, items and ratings by using the least information of binary codes, train the CGH with the MDL principle, which finds the best parameters that maximally compress the training data and meanwhile keep the information carried, thus CGH aims to minimize the expected amount of informations related q-
(()) =E, llog pl R, U, V, B, D) log D)]
= -,yloog pl [R] D) + log P log Log (L(((B()))
(9)
(L(g(DVV)lI((I)
Maximizing the posterior probability is equivalent to maximizing L(g) by only considering the variational distribution of q( (B, D), the objective becomes
Cjj
Au
Ao E (0, C,d,) Σ (u: C,,b,)²
(M))(0,
Σ
(Taj 5(b, d2)),,)²
2
2
KKI gΦ. lpo.) (L (lpo. )- - ((9, b)
(10)
where -= (O.. 0.). = s ). V( 2) is the regularizer term with parameters and Q. By training the objective in eq. 10,, obtain binary codes, but some bits probably be correlated. To minimize the reconstruction error, SGH had to set up the code length as long as r = 200. Our goal in this paper is obtain compact and informative hash codes, thus we impose the balance and independent constraints on hash codes by eq. (8) Maximizing the eq. (1O) is transformed to minimizing the following constrained objective function of the proposed Collaborative Generative Hashing (CGH).
Cij (ry - 8(b,, d,))) +
Xu Σ (u: C.b,))
A +
LOGH (0, = Σ
Σ
(u, - CCdd
2
2
i.
KL KL d... (po..)) + KL g0 l|pa.) + au
|| 1,
1
2
0h
TTo 1, 1r
Su || T T I Idutr 1| Bu ||
La +r
(11)
The objective of CGH eq. straightforwardlly so in the training stage, the tanh function is utilized to replace the sign function in the inference step, and then the continuous outputs are used as a relaxation of hash codes.
[1 is a discrete optimization problem. which is difficult to optimize
With the relaxation, we train all components jointly with back sockkpropagation. After training, we fix them and make forward passes to map the concatenate vectors in U and V to binary codes B and D, respectively. The recommendation in various settings then conducted using and D by the similarity score estimated as before 8(b;, d,) 1 Hamadis(bb;.. d,).
The training settings are dependent on the recommendation settings, i.e, warm-start, cold-start item, and cold-start user. LOGH (O, +) aims to minimize the rating loss and two content reconstruction emrors with regularizers. (a.) For the warm-start recommendation, ratings for all users and items available, then the above objective is trivially optimized by setting the content weights to 0 and learning hashing function with the observed ratings R. (b.) For the cold-start item recommendation, ratings for some items missing, then the objective is optimized by setting the user content weight to 0 and learning parameters with the observed ratings R and item content V. (c.) The training setting for the cold-start recommendation is similar the cold-start item recommendation.
5

---

Under review as a conference paper at ICLR 2020
3 EXPERIMENTS
We validate the proposed CGH on two public dataset: CiteUlike and RecSys 2017 Challenge datase from the following two aspects:
(1) Marketing analysis. To validate the effectiveness of CGH in marketing area, we fist de- fined metric evaluate the accuracy of mining potential users; we then test the perfor- mance for warm-start item and cold-start item, respectively.
(2) Recommendation performance. We test the performance of CGH for recommendation in various settings including: warm-start, cold-start item, and cold-start user in terms of Accurcy@E @k (Yin et al. 2014).
In the following, we first introduce the experimental settings, followed by the experimental results analysis from the above aspects.
3.1 EXPERIMENTAL SETTINGS
To evaluate the power of finding out potential users and the accuracy of recommendation different
settings. (1) For the CiteUlike dataset, contains 5,551 users, 16,980 articles, 204.986 observed user-article binary interaction pairs, and articles abstract content. Similar to (Wang & Blei 2011), extract bag-of-the-words item vector with dimension de 8000 by ranking the TF-IDF values. (2) For the RecSys 2017 Challenge dataset, it is the only publicly available datasets that contains both user and item content data enabling both cold-start item and cold-start user recommendation. It contains 300M user-item interactions from 1.5M users to 1.3M items and content data collected from the career oriented social network XING (Europern analog of LinkedIn). Like (Vokkovs al.) 2017), we evaluate all methods on binary rating data, item content with dimension of d,, 831 and user content with the dimension of d,, = 2738. user features and 2738 item features forming the dimensions of user and item content.
We randomly split the binary interaction (rating) R into three disjoint parts: warm start ratings R", cold-start user ratings R". and cold-start item ratings R", and Rd is furtherly split into the training dataset R and the testing dataset R Correspondingly, the user and item content datasets are split into three disjoint parts. The randomly selection is carried out times independently, and report the experimental results as the average values.
3.2 EVALUATION METRIC
The ultimate goal of recommendation is to find out the top-k items that users may be interested in. Accuracy @ & was widely adopted by many previous ranking based recommender systems (Ko- ren 2008. Chen al.) 2009). Thus we adopt the ranking- evaluation metric Accuracy @ k to evaluate the quality of the recommended item ranking list.
Metric for Marketing Application. For a new application of the recommender system, there haven't yet metric to evaluate the marketing performance. Thus, in this paper, we define an evalu- ation metric similar to the ranking- based metric Accuracy @ used for the warm-start and cold-start recommendation in this paper.
From Fig. we discover the nearest potential users for an item j. The basic idea of the metric is to test whether the user that really interested in item appears in the k potential users list. For each positive rating (rig = 1) in the testing dataset Dtest: (1) we randomly choose 1000 negative users (users & with rkj 0) and find & potential users in the 1001 user set; (2) we check if the positive user (with positive rating rıy = 1) appears in the potential users list. If the answer is 'yes' we have 'hit' and have "miss" otherwise.
The metric also denoted by Accuracy@1 @k is formulated as:
Accuracy@k- #hit@k
(12)
D
httt://wwww.cieuukk....nngagaaa httt://wwwwrrrcyccccllllm.m////

---

Under review as a conference paper at ICLR 2020
where [Diest is the size of the test set, and #hit@k denotes the number of hits in the test set.
3.3 ACCURACY FOR MINING POTENTIAL USERS
The experiments evaluate the performance of the marketing application in mining potential users for warm-start items on the test dataset R and cold-start items on R". Specifically, we first train the model on the training dataset R and the corresponding user and item content data. When the training is completed, we fix parameters and obtain hash codes b, and dj by making forward passes. Then we generate & potential users for items in the test dataset by the procedure demonstrated in Fig. 2 and evaluate the quality of the potential users list by Accuracy @k defined in Section 3 The
Accuracy with different numer of polantial users
different settings
1.2
Reconstruction error
strt
0.5
0.5
Jwin lpos
Accumcy
Actracve 20
0.8
0.3
0.6
0.3
0.4
0.24
0.2
25
30
10
30
40
20
60
60
100
The numer of potential users
The size test for each user less than
number of samples visted
Figure 3: Left:
The accuracy variation for warm-start and cold-start with the number of potential users. Center: The accuracy variation of mining 20 potential users with the number of users (size of the test set) who really interested in the target item. Right: The average reconstruction error with the number of visited samples. The total error is the sum of the rating reconstruction error, the user content reconstruction error, and the item reconstruction error.
marketing analysis for warm start item and cold-start item are reported in Fig. (Left.), which shows the accuracy values varies with the numbers of potential users. It indicates the accuracy increases with the number of potential users for both cold-start and warm start settings. It's reasonable because mining more potential users will have greater accuracy value defined in Section 3.2 Especially, the proposed CGH effective for cold-start item, which indicates further e-commerce strategies can be developed for new items to attract those potential users. Besides, from the perspective of marketing, warm-star recommendation and cold-start recommendation has less gap than traditional recommendation.
Robust Testing, We evaluate the performance varies with the number of users who really interested in the target item in test set. The experimental results shown in Fig. (Center.) indicates the accuracy grows steadily with the size of test set, which reveals the CGH for marketing application is robust. Thus, it is practical to be used in the sparse and cold-start settings.
Convergence of CGH. Fig. 3 (Right.) demonstrates the convergence of the proposed CGH, which reveals the reconstruction errors of ratings, users content, items content and the total error with the number of samples seen by CGH are converged, which furtherly validate the correction and effectiveness of the proposed CGH.
3.4 ACCURACY FOR RECOMMENDATION
Accuracy for warm-start Recommendation. Fig. (Left.) shows the accuracy comparison of warm-start recommendation on CiteUlike dataset. In which collaborative generated embedding (CGE) denotes the real version of CGH. The figure shows the proposed CGH (CGE) has a com- parable performance with other hybrid recommender systems. The proposed CGH is hashing- based recommendation, hence binary vectors apply to recommendation which has the advantage in online recommendation introduced in Section D while the baselines are real-valued recommendations which conducts recommendation on real latent space. Due to real latent vectors intuitively carried more information than hash codes. Thus it is acceptable have small gaps between the real-valued hybrid recommendation and the hashing-bart based recommendation. In addition, there still small gap of the real version CGE in comparison with DropoutNet, because the reconstruction error is consid-
7

---

Under review as a conference paper at ICLR 2020
ered in CGH(CGE), while DropoutNet didn't consider it. However, the reconstruction is significant in the generative step of CGH, which makes it feasible to mining effective potential users, thus CGH(CGE) has the advantage in marketing application.
Accuracy: & on Citau (Warm-start)
Accuracy
Accuracyåk cold-start)
Orsaxanet
CDL
0.3
Drapsutrat
0.25
CSH
Dispectrat
023
Arruscys
CGE
Accuracy)
Accuacy&
0.2
0.0
0:15
0:15
0.2
0.06
0.05
5
10
15
20
30 Positonk
25
35
40
45
50
15
70
10
10
35
40
50
Postion K
Positio
Figure 4: Left: The Accuracy variation with the number of recommended items for warm-start recommendation on CiteUlike. Center: The Accuracy variation with the number of recommended items for cold-start item recommendation CiteUlike Right: The Accuracy variation with the number recommended items for cold-start user recommendation on RecSys
Accuracy for cold-start item recommendation. This experiment studies the accuracy comparison between competing hybrid recommender systems and CGH under the same cold-start item setting. We test the performance item (cold-start item) has less than 5 positive ratings. Then we select users with at least one positive rating test users. For each test user, first choose his/her ratings related cold-start items as the test set, and the remaining ratings as the training set. Our goal is to test whether the marked-off cold-start items can be accurately recommended the right user.
the test dataset R, introduced in Section 3.1 Specifically, in Rg each
The experimental results for cold-start item recommendation are shown in Fig. (Center.). We conclude that CGH has comparable performance with competing baselines and achieves better performance than CTR. The results evaluated by another metric MRR (detailed in Appendix.A) are similar.
Accuracy for cold-start user recommendation. We also test the performance on the cold-start user setting on the test dataset R. introduced in Section 3.1 Specifically, Ra, each user (cold-start user) has less than 5 positive ratings. Then we select items with at least one positive rating test items. For each test item, we first choose ratings related cold-start users as the test set, and the remaining ratings as the training set. Our goal is to test whether the test item can be can be accurately recommended to marked-off user.
Due to the fact that only Dropout can be applied to cold-start user recommendation, so we only compare the performance of CGH with Dropout. The experimental results for cold-start user rec- ommendation shown in Fig, 4 (Right.) indicates our proposed CGH has similar performance with DropoutNet. Besides, CGH has the advantage of the application in marketing area.
4 CONCLUSION
In this paper, generated recommendation framework called collaborative generated hashing (CGH) proposed address the cold-start and efficiency issues for recommendation. The two main con- tributions are put forward in this paper: (1) we develop collaborative generated hashing framework with the principle Minimum Description Length together(MML) with uncorrelated and balanced constraints on the inference process to derive compact and informative hash codes, which is signifi- cant for the accuracy of recommendation and marketing; (2) we propose a marketing strategy by the proposed CGH, specifically. we design framework to discover the k potential users by the generate step: (3) we evaluate the proposed scheme on two the public datasets, the experimental results show the effectiveness of the proposed CGH for both warm-start and cold-start recommendation.
8

---

Under review as a conference paper at ICLR 2020
REFERENCES
Iman Barjasteh, Rana Forsati, Farzan Masrour, Abdol-Hossein Esfahanian, and Hayder Radha. Cold-start item and user recommendation with decoupled completion and transduction. In Pro- ceedings of the 9th ACM Conference on Recommender Systems, pp. 91-98. ACM, 2015.
Wen-Yes Yen Chen, Jon-Chyuan Chu, Junyi Luan, Hongjie Bai, Yi Wang, and Edward Y Chang. Col- laborative filtering for orkut communities: discovery of user latent behavior. In Proceedings of the 18th international conference on World wide web, pp. 681-690. ACM, 2009.
Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, and Le Song. Stochastic generative hashing. In Proceedings of the 34th International Conference Machine Learning-Voiie Volume 70, pp. 913-922. JMLR. org, 2017.
Ruining He and Julian McAuley. Vbpr:
visual bayesian personalized ranking from implicit feed- back. In Thirtieth AAAI Conference on Artificial Intelligence, 2016.
Yehuda Koren. Factorization meets the neighborhood: multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426-434. ACM, 2008.
Ychuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, (8):30-37, 2009.
Defu Lian, Rui Liu, Yong Ge, Kai Zheng, Xing Xie, and Longbing Cao. Discrete content-aware matrix factorization. In Proceedings of KDD' 17, pp. 325-334. ACM, 2017.
Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, and Steven CH Hoi. Discrete social recommendation. Thirty-Thinn -hird AAAI Conference on Artificial Intelligence, 2019.
Dominik Papies, Peter Ebbes, and Harald J Van Heerde. Addressing endogeneity in marketing models. In Advanced methods for modeling markets, pp. 581-627. Springer, 2017.
Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, and Alan Han- jalic. Climf: learning maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the sixth ACM conference on Recommender systems, pp. 139-146. ACM, 2012.
Kosetsu Tsukuda, Satoru Fukayama, and Masataka Goto. Abeprec: Adaptively bridging consumer and producer roles for user-generated content recommendation. In Proceedings of the 42nd In- ternational ACM SIGIR Conference on Research and Development Information Retrieval, pp. 1197-1200. ACM, 2019.
Maksims Volkovs, Guangwei Yu, and Tomi Poutanen. Dropoutnet: Addressing cold start in recom- mender systems. In Advances in Newral Information Processing Systems, pp. 4957-4966, 2017.
Ellen M Voorhees et al. The trec-8 question answering track report. In Trec, volume 99, pp. 77-82, 1999.
Chong Wang and David M Blei. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 448-456 456. ACM, 2011.
Hao Wang, Naiyan Wang, and Dit-Yan Yeung. Collaborative deep learning for recommender sys- tems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discov- ery and Data Mining, pp. 2235-1244, ACM. 2015.
Haoyu Wang, Nan Shao, and Defu Lian. Adversarial binary collaborative filtering for implicit feedback. In Proceedings of the AAAI Conference Artificial Intelligence, volume 33, pp. 5248- 5255, 2019.
Qifan Wang, Bin Shen, Zhiwei Zhang, and Luo Si. Sparse semantic hashing for efficient large scale similarity search. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1899-19009 ACM, 2014.

---

Under review as a conference paper at ICLR 2020
Jian Wei, Jianhua He, Kai Chen, Yi Zhou, and Zuoyin Tang. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69:29-39, 2017.
Hongzhi Yin, Bin Cui, Ling Chen, Zhiting Hu, and Zi Huang. A temporal context-aware model for user behavior modeling in social media systems. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pp. 1543-1554. ACM, 2014.
Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, and Tat-Seng Chua. Discrete collaborative filtering. In Proceedings of SIGIR 16, volume 16, 2016.
Yan Zhang, Haoyu Wang, Defu Lian, Ivor W Tsang, Hongzhi Yin, and Guowu Yang. Discrete ranking-based matrix factorization with self-paced learning. In Proceedings of the 24th ACM SIGKDD International Conference Knowledge Discovery & Data Mining, pp. 2758-2767. ACM, 2018.
Zhiwei Zhang, Qifan Wang, Lingyun Ruan, and Luo Si. Preference preserving hashing for efficient recommendation. Proceedings of SIGIR' 14, pp. 183-192. ACM, 2014.
Feng Zheng, Cheng Deng, and Heng Huang. Binarized neural networks for resource-efficient hash- ing with minimizing quantization loss.
Ke Zhou and Hongyuan Zha. Learning binary codes for collaborative filtering. In Proceedings of KDD' 12, pp. 498-506. ACM, 2012.
APPENDIX
A. MRR RESULTS ON FOR RECOMMENDATION
We evaluate the accuracy in terms of the MRR Yin et al. 2014 metric shown in Table [ warm- start recommendation. Our proposed CGH performs almost as well as the best result of the real- valued competing baselines. Table [ result is marked as ** and the second best is marked as 'oq. We find that the performance CGH is very close to the best result, that is consistent with the outcome of Accuracy @ k reported in Fig.
summarizes MRR results for the four algorithms, the best
Table I: MRR CiteUlike
Method
CTR
CDL
Dropoutnet
CGH
Waarm-starty
0.0324
0.0667*
0.0580
0.0595
Cold-startq
0.0101
0.0150
0.0179*
0.0165
10


Reviews:
[{"review_id": "Bygqiv-3Fr", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper introduces a collaborative generated hashing (CGH) method to learn hash funcations of users and items from content data. The approach first provides a strategy to discover potential users by the generative step and inference through adding balanced and uncorrelated constraints. The experiments demonstrates some effectiveness on improving accuracy for both warm-start and cold-start recommendations.\n\nThis paper should be rejected because (1) this method only combines existing techs, such as Stochastic Generative Hashing (Eq.1 and Eq. 6), and lacks novelty; (2) lack of introduction to related work and baselines, (3) the experiments results can not support the claim, i.e. the effectiveness of CGH in marketing area, and (4) paper writing is awful and very hard to follow.\n\nMain argument\n\nAlmost every essential parts of the proposed method are from existing methods:\n(I) Eq. 1 and Eq. 6 are proposed by Stochastic Generative Hashing [1];\n(II) Eq. 2 and Eq. 5 are a multivariate Bernoulli distribution;\n(III) Eq. 3 is a normal distribution;\n(IV) Eq. 7 is proposed by [2];\n(V) loss function Eq. 9 is follow the Minimum Description Length principle [1];\nThe proposed method CGH is a combination of these techs and compared with these methods, there are few novel aspects.\n\nThis paper omits the related work part and does a rough introduction to two baselines (CDL and DropoutNet) in a confusing way in Section 2. A concise and precise introduction to other methods will help the reader to better understand the related works and the advantages and disadvantages of the proposed method.\n\nThe experiments do not provide convincing evidence of the corretness of the proposed method, especially in Section 3.3. In Section 3.3, Figure 3 shows the performance on Accyracy@k without any baseline. The results do not demonstrate the validity of the method and therefore cannot support the author's claim.\n\nThings to improve the paper that did not impact the score:\n1) page 1, 4th line in the 3rd paragraph, 'efficient' -> efficiently\n2) page 3, 1st sentence in Section 2.1\n3) page 3, hard to find the definition of the similarity function \n4) page 4, 2nd line 'similar for' -> 'similar to'\n5) page 6, 3rd paragraph 'From Fig. 2, we discosver the k nearest potential users for an item j'. What do you mean?\n\n\nReference\n[1] Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, and Le Song. Stochastic generative hashing. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 913–922. JMLR. org, 2017.\n[2] Ke Zhou and Hongyuan Zha. Learning binary codes for collaborative filtering. In Proceedings of KDD’12, pp. 498–506. ACM, 2012.", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly."}, "extra_text": {}}, {"review_id": "BygXZbAD_S", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper proposes new hashing schemes to learn hash codes to describe users/items for the purpose of recommendation.\n\nIt is claimed that doing so leads to three contributions\n(1) the new hash codes themselves, which can apply to various recommendation settings\n(2) the ability to better discover potential users in e-commerce settings\n(3) state-of-the-art performance on several datasets\n\nIn terms of (1), the paper contrasts with Collaborative Deep Learning and dropoutnet. The main difference compared to CDL is that the hashing-based method learns binary codes. Compared to DropoutNet the main difference is the use of a stacked autoencoder, rather than a different neural network architecture. This latter contribution is perhaps a little thin (really it's just a technical detail); the additional contribution of being able to be used as a marketing strategy I didn't really follow.\n\nThe \"mining potential users\" contribution (contribution 2) seemed a little ad-hoc to me. It ultimately seems like a variant of KNN, and seems like something similar could be attempted for other methods.\n\nTraining etc. looks fine, though I didn't fully check the details.\n\nThe experiments seem not totally convincing. Most critically, the method does not seem to exhibit state-of-the-art performance as claimed, but is somewhat lower (in terms of accuracy) than other baselines. It might be better in terms of speed but this doesn't seem to be thoroughly evaluated. The choice of accuracy as the only evaluation metric also seems unusual.\n\nThe selection of datasets is also quite limited. It's claimed that these are the only datasets with user and item content, but why are both needed to run an experiment? Can't this method work with either (in which case many other datasets would be appropriate)?\n\nOverall the actual results seem mixed, and thus the paper hinges on its statement that it has the \"advantage of applications in marketing area\". However this latter contribution seems handwavy.\n\nIn order to be accepted, I'd need to see\n-- More clearly stated and demonstrable contributions\n-- More compelling experiments, in terms of datasets, evaluation measures, and actual performance", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I did not assess the derivations or theory.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I made a quick assessment of this paper."}, "extra_text": {}}, {"review_id": "BklWz3InFS", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The work considers the problem of efficient user and item recommendations in the warm- and cold-start settings. It aims at improving computational efficiency of the best candidate selection in these settings by utilizing binary codes representation. The transformation from an actual to a binary code representation is learned in a hybrid manner using both collaborative and content information. In order to keep such representations compact yet expressive enough, the authors impose a set of constraints that ensure balanced and uncorrelated transformations. Once binary codes are learned, the inference can be made by virtue of efficient Hamming distance computations. Moreover, the search for candidate entities can be performed via the generative step that projects binary codes onto actual feature space, where kNN-based techniques can be further utilized.\n\nMajor drawback of the work is that it does not provide any quantitative evidence to support the main claim – that the proposed approach is at least more computationally efficient, since it underperforms competing methods in terms of accuracy. Essentially, the work answers the question whether it is possible to utilize hashing techniques based on binary codes; however, the question on the practicality and efficiency of this approach remains open. I would therefore suggest rejecting the work.\n\nOne of the weak points of other methods noted by the authors is “the expensive similarity search in real latent space”. The authors aim to resolve that problem by learning hashing functions based on “compact and informative” binary codes representation. However, while an overall problem formulation is clearly described and the learning objective is well explained, no further evidence supporting initial claims is provided. Moreover, an overall logic seems contradictory:\n1.\tBinary codes allow efficient preference estimation via XOR operation.\n2.\tLearning binary codes is a difficult discrete optimization task.\n3.\tHence, we employ special MDL principle for solving a constraint optimization problem and employ relaxation of hash codes to move away from discrete optimization.\nAfter relaxation, the hash codes are no longer binary. Do you still enforce binary representation by some thresholding or other method? If yes, more words explaining this should be added to the text. If no, then how is it different from classical learning of latent variables? Essentially, relaxed non-binary hash codes are similar to latent vectors.\nThe lack of description raises concerns in an overall efficiency and the authors, unfortunately, provide no evidence of improved computational performance. Given that the proposed approach underperforms competing methods in terms of accuracy of recommendations, more efforts should be made to demonstrate its competitive advantages in terms of time required for training and generating predictions.\n\nAnother contradictory part is the generative step for candidate selection. The idea of using inference to generate the most pertinent user vector for a selected item hash code is novel and interesting. However, it requires searching neighbors in the real feature space, which can be very inefficient, depending on the structure of features. I’m not convinced that it is better than searching neighbors directly in the latent space, which can be done in the majority of hybrid models. Moreover, there exist various approximate nearest-neighbors search methods, e.g. Annoy, NMSLib, Faiss, etc., which allow trading-off accuracy and efficiency. Considering that hash codes also lose some information (which is observed in the results of experiments), it seems necessary to have a comparison with these approximate methods as well.\nIt should be also noted that in some cases you don’t even have to run the similarity search. Many hybrid models learn latent representation of features directly and cold-start entities are straightforwardly described via combination of the corresponding latent vectors of their features (e.g, Factorization Machines [Rendle 2009]). Hence, affinity between a cold item and some user can be quickly estimated via inner product of their latent vectors. \n\nSuggestions on improving the work:\nWorth mentioning, that recent studies raise certain concerns about the superiority of modern neural network-based approaches over simpler (and properly tuned) linear baselines, see the work by [Dacrema, Cremonesi, Jannach 2019] on “A Worrying Analysis of Recent Neural Recommendation Approaches”. The DropoutNet method in your experiments is very similar to CDL in terms of accuracy. The latter, however, underperforms even simple knn models, as shown by the work mentioned above. The haven’t tested it in the cold-start regime, though. Still, I’d strongly recommend adding to your experiments comparison with simpler hybrid models, e.g., Factorization Machines. Also note that there are even stronger baselines published recently, e.g. HybridSVD by [Frolov, Oseledets 2019].\n\nAdditional remarks:\n1)\tFigure 3 and the related text seem to focus on too obvious things. Indeed, by increasing the number of entities to compare against, you increase chances to have a hit. This part of the text, basically, states that the method works, which can already be seen from other results.\n2)\tA lot of attention is given to the “marketing application”. It’s ok to have it in introduction and make a connection to the real-world problem; however, further mentions of it in the text feel excessive. In the experiment section you describe a standard evaluation procedure for the cold start, there is no need to refer to marketing application again as you do not provide any new metric. It would feel much more organic if you would have the results of A/B testing on real users. Otherwise, I’d suggest to focus more on the problem that you’re solving, not on possible application.\n3)\tThe text is in a very unpolished state. It reads more like a draft version. There are many typos and error both in text and in derivations.\n\nReferences:\nRendle, Steffen. \"Factorization machines.\" In 2010 IEEE International Conference on Data Mining, pp. 995-1000. IEEE, 2010.\nFrolov, Evgeny, and Ivan Oseledets. \"HybridSVD: when collaborative information is not enough.\" In Proceedings of the 13th ACM Conference on Recommender Systems, pp. 331-339. ACM, 2019.\nDacrema, Maurizio Ferrari, Paolo Cremonesi, and Dietmar Jannach. \"Are we really making much progress? A worrying analysis of recent neural recommendation approaches.\" In Proceedings of the 13th ACM Conference on Recommender Systems, pp. 101-109. ACM, 2019.", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The presented work has worse accuracy than existing (and not all the baselines are given correctly) and does not provide the running time comparison. All reviewers recommend rejection, and I am with them.
Decision: Reject