SSE-PT: Sequential Recommendation Via Personalized Transformer
Authors: Liwei Wu
Shuqing Li
Cho-Jui Hsieh
James Sharpnack
Venue:  (2020)
Keywords: sequential recommendation
personalized transformer
stochastic shared embeddings

Abstract: Temporal information is crucial for recommendation problems because user preferences are naturally dynamic in the real world. Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed for better use of the temporal ordering of items that each user has engaged with. In particular, the SASRec model, inspired by the popular Transformer model in natural languages processing, has achieved state-of-the-art results. However, SASRec, just like the original Transformer model, is inherently an un-personalized model and does not include personalized user embeddings. To overcome this limitation, we propose a Personalized Transformer (SSE-PT) model, outperforming SASRec by almost 5% in terms of NDCG@10 on 5 real-world datasets. Furthermore, after examining some random users' engagement history, we find our model not only more interpretable but also able to focus on recent engagement patterns for each user. Moreover, our SSE-PT model with a slight modification, which we call SSE-PT++, can handle extremely long sequences and outperform SASRec in ranking results with comparable training speed, striking a balance between performance and speed requirements. Our novel application of the Stochastic Shared Embeddings (SSE) regularization is essential to the success of personalization. Code and data are open-sourced at https://github.com/SSE-PT/SSE-PT.

Under review as a conference paper at ICLR 2020
SSE-PT: SEQUENTIAL RECOMMENDATION VIA PERSONALIZED TRANSFORMER
Anonymous authors Paper under double-blind review
ABSTRACT
Temporal information crucial for recommendation problems because user prefer- ences are naturally dynamic in the real world. Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed for better use of the temporal ordering of items that each user has engaged with. In particular, the SASRec model, inspired by the popular Transformer model in natural languages processing, has achieved state-of-the-art results. However, SASRec, just like the original Transformer model, is inherently un-personalized model and does not include personalized user embeddings. To overcome this limitation, we propose Personalized Transformer (SSE-PT) model, outperforming SASRec by almost 5% in terms of NDCG@ 10 on real-world datasets. Further- more, after examining some random users' engagement history, find our model not only more interpretable but also able to focus on recent engagement patterns for each user. Moreover, our SSE-PT model with a slight modification. which we call SSE-PT++, can handle extremely long sequences and outperform SASRec in rank- ing results with comparable training speed, striking a balance between performance and speed requirements. Our novel application of the Stochastic Shared Embed- dings (SSE) regularization is essential to the success of personalization. Code and data are open-sourced at lhtps:: //t/t/gitttt com SSE 2I/SSE-
SSE PT
INTRODUCTION
The sequential recommendation problem has been an important open research question, yet using tem- poral information to improve recommendation performance has proven to be challenging. SASRec, proposed by (Kang and McAuley, 2018) for sequential recommendation problems, has achieved state- of-the-art results and enjoyed more than 10x speed-up when compared to earlier CNN/RNN- based methods. However, the model used in SASRec is the standard Transformer which is inherently an un-personalized model. In practice, is important to include a personalized Transformer SASRec especially for recommender systems, but (Kang and McAuley 2018) found that adding additional personalized embeddings did not improve the performance of their Transformer model, and postulate that the failure of adding personalization is due to the fact that they already the user history and the user embeddings only contribute to overfitting. In this work, we propose a novel method, Personalized Transformer (SSE-PT), that successfully introduces personalization into self-attentive neural network architectures.
Introducing user embeddings into the standard transformer model is intrinsically difficult with existing regularization techniques, as unavoidably a large number of user parameters are introduced, which often the same scale of the number of training data. But we show that personalization can greatly improve ranking performance with a recent regularization technique called Stochastic Shared Embeddings (SSE) (Wu al. 2019). The personalized Transformer (SSE-PTT) model with SSE regularization works well for all 5 real-world datasets we consider without overfitting, outperforming previous state-of-the-art algorithm SASRec by almost 5% terms NDCG@ 10. Furthermore, after examining some random users' engagement history, we find our model is not only more interpretable but also able to focus on recent engagement patterns for each user. Moreover, our SSE-PT model with slight modification, which we call SSE-PT++, handle extremely long sequences and outperform SASRec in ranking results with comparable training speed, striking a balance between performance and speed requirements.

---

Under review as a conference paper at ICLR 2020
2
RELATED WORK
2.1 SESSION- DESSION-BASED AND SEQUENTIAL RECOMMENDATION
Both session-hasse and sequential (i.e., next-basket) recommendation algorithms take advantage of additional temporal information to make better personalized recommendations The main difference between cession-based recommendations (Hidasi et al.) 2013) and sequential recommendations (Kang) and McAuley 2018) is that the former assumes that the user ids are not recorded and therefore the cession-based recommendations
length of engagement sequences are relatively short. Therefore, normally do not consider user factors. On the other hand, sequential recommendation treats each sequence as a user's engagement history (Kang and McAuley, 2018). Both settings, do not explicitly require time-stamps: only the relative temporal orderings are assumed known (in contrast to, for example, timeSVD++ (Koren 2009) using time-stamps). Initially, sequence data in temporal order usually modelled with Markov models, in which a future observation is conditioned on the last few observed items (Rendle et al. 2010). In (Rendle et al. 2010), a personalized Markov model with user latent factors is proposed for more personalized results.
In recent years, deep learning techniques, borrowed from natural language processing (NLP) literature, getting widely used in tackling sequential data. Like word sentences in NLP, item sequences in recommendations can be similarly modelled by recurrent neural networks (RNN) (Hidasi et al.
2015 Hidasi and Karatzoglou, 2018) and convolutional neural network (CNN) (Tang and Wang 2018 models. Recently, attention models are increasingly used in both NLP /Vaswani et al. 2017 Devlin et al.) 2018)) and recommender systems (Liu al. 2018 Kang and McAuley 2018). SASRec (Kang and McAuley| (018) 22188) is recent method with state-of-the of-the-art performance among the many deep learning models. Motivated by the Transformer model in neural machine translation (Vaswani et al., 2017), SASRec utilizes a similar architecture to the encoder part of the Transformer model.
Our proposed model, SSE-PT, is a personalized extension of the transformer model.
2.2 REGULARIZATION TECHNIQUES
In deep learning, models with many more parameters than data points can easily overfit to the training data. This may prevent us from adding user embeddings as additional parameters into complicated models like the Transformer model (Kang and McAuley 2018), which can easily have 20 layers with millions of parameters for a medium-sized dataset like Movielens 10M (Harper and Konstan 2016). &g regularization (Hoerl and Kennard 1970) is the most widely used approach and has been used in many matrix factorization models in recommender systems; L regularization (Tibshirani 1996) used when sparse model is preferred. For deep neural networks, it has been shown that Lp regularizations are often too weak, while dropout (Hinton et al. [012 Srivastava et al., 2014) is more effective in practice. There are many other regularization techniques. including parameter sharing (Goodfellow et al. (0166), max-norm regularization (Srebro et al., 2005), gradient clipping (Pascanu) cet al.] 2013), etc. Very recently, a new regularization technique called Stochastic Shared Embeddings (SSE) (Wu et al. 2019) is proposed
means of regularizing embedding layers. We find that the base version SSE-SE is essential to the success of our Personalized Transformer (SSE-PT) model.
3 METHODOLOGY
3.1 SEQUENTIAL RECOMMENDATION
Given users and each user engaging with a subset of m items in a temporal order, the goal of sequential recommendation is to learn a good personalized ranking of top K items out of total m
items for any given user at any given time point. We assume data in the format of item sequences:
s; Je......... jer) for Ssis
(1)
Sequences s; of length contain indices of the last T items that user has interacted with in the temporal order (from old to new). For different users, the sequence lengths can vary, but we can pad the shorter sequences so all of them have length T. We cannot simply randomly split data points into vrain/vaiiatiion/estt sets because they come in temporal orders. Instead, we need to make sure our training data is before validation data which is before test data temporally. We use last items in sequences test sets, second-to-last items as validation sets and the rest training sets. We
2

---

Under review as a conference paper at ICLR 2020
ranking metrics such as NDCG@ K and & for evaluations, which defined in the Appendix.
3.2 PERSONALIZED TRANSFORMER ARCHITECTURE
Our model, which we call SSE-PT, motivated by the Transformer model (Vaswani et al. 2017) and (Kang and McAuley 2018). It also utilizes new regularization technique called stochastic shared embeddings (Wu et al. 2019). In the following sections, we are going to examine each important component of our Personalized Transformer (SSE-PT) model, especially the embedding layer, and the novel application of stochastic shared embeddings (SSE) regularization technique.
Embedding Layer We define a learnable user embedding look-up up table U R=xd, and item embedding up table V E Rmxx, where du,, d, are the number of hidden units for user and item respectively. We also specify learnable positional encoding table R where d = d, d. So each input sequence E R² will be represented by the following embedding:
M us + P1 M
u,
+ P2
E
R
[e ud] Pr.
where (1jni u.] represents concatenating item embedding Uja Rd. and user embedding u, R into embedding E, Re for time l. Note that the main difference between our model and (Kang and McAuley 2018) is that we introduce the user embeddings ii making our model personalized.
Unitormly Sampled Negative Item
Cross Entropy Loss
Pg
4k
Positive
FullyConnected Layer
Block
Attention
Cormeedde Layer
Block
Postioona
Attintion
+PL
+Pa
+Pr
kn
9/s
V/a
Vjr
Vi
Av
E+1
time
Figure 1: Illustration of our proposed SSE-PT model
Transformer Encoder On top of the embedding layer, we have B blocks of self-attention layers and fully connected layers, where each layer extracts features for each time step based on the previous layer's outputs. Since this part identical to the Transformer encoder used the original papers Vaswaani al. 2017 Kang and McAuley 2018), we will skip the details.
Prediction Layer
At time t, the predicted probability of user i engaged item is:
Pau a (rsu),
where a is the sigmoid function and rat is the predicted score of item / by user at time point f, defined as:
Fa F [1g: u.].
where F is the output hidden units associated with the transformer encoder at the last timestamp. Although another of user and item embedding look-up tables for the u; and vt,

---

Under review as a conference paper at ICLR 2020
find it better use the same set of embedding look-up tables U, V in the embedding layer. But regularization for those embeddings can be different. To distinguish the i4 and By in (4) from u, by
in 2 we call embeddings in (4) output embeddings and those in 2 imput embeddings.
The binary cross entropy loss between predicted probability for the positive item = Ji(++1) and one uniformly sampled negative item As E Q is given as ([log(pH) + log(1 - Pax)], Summing over s; and I, we obtain the objective function that we want to minimize is:
[[lo((Puu) + logg(1 Pack)]
(5)
kE
At the inference time, top-K recommendations for user at time can be made by sorting scores Tast for all items E and recommending the first K items in the sorted list.
Novel Application of Stochastic Shared Embeddings The most important regularization tech- nique to SSE-PT model is the Stochastic Shared Embeddings (SSE) (Wu et al.) 2019). The main idea of SSE is to stochastically replace embeddings with another embedding with some pre-defined probability during SGD, which has the effect of regularizing the embedding layers. Without SSE, all the existing well-known regularization techniques like layer normalization, dropout and weight decay fail and cannot prevent the model from werr-fiting badly after introducing user embeddings. (Wul itt al.. 2019)) develops two versions of SSE, SSE-Graph and SSE-SE. In the simplest uniform case, SSE-SE replaces one embedding with another embedding uniformly with probability p. which is called SSE probability in (Wu et al. 2019). Since we don't 't have knowledge graphs for user or items, we simply apply the SSE-SE our SSE-PT model. We find SSE-SE makes possible training this personalized model with (Idd,) additional parameters.
There are 3 different places in our model that SSE-SE can be applied. We can apply SSE-SE to input/output user embeddings. input item embeddings, and output item embeddings with probabilities Pu. Pt and Py respectively. Note that input user embedding and output user embedding are always replaced at the same time with SSE probability Pu. Empirically, we find that SSE-SE to user embeddings and output item embeddings always helps, but SSE-SE to input item embeddings is only useful when the average sequence length is large, e.g., more than 100 in Movielens IM and Movielens 1 1M datasets.
Other Regularization Techniques Besides the SSE (Wu et al. 2019). also utilized other widely used regularization techniques, including layer normalization (Ba et al. 2016)) batch normalization (Totte and Szegedy, 2013), residual connections (He et al.) 2016). weight decay (Krogh and Hertz 1992), and dropout (Srivastava et al., 2014). Since they are used in the same way in the previous paper (Kang and McAuley 2018), we omit the details to the Appendix.
3.3 HANDLING LONG SEQUENCES: SSE-PT++
To handle extremely long sequences, slight modification can be made on the base SSE-PT model in terms of how input sequences 8,'s are fed into the SSE-PT neural network. We call the enhanced model SSE-PT++ to distinguish it from the previously discussed SSE-PT model, which cannot handle sequences longer than T.
The motivation of SSE-PT++ over SSE-PT comes from: sometimes want make use of extremely long sequences, 8; = (Ja,, ja.) for 1 < i S n, where > T, but our SSE-PT model can only handle sequences of maximum length of T. The simplest way is to sample starting index 1 < v S t uniformly and use s; = J((+1) jiz). where = min v + T - 1). Although sampling the starting index uniformly from [1. can accommodate long sequences of length > T, this does not work well in practice. Uniform sampling does not take into account the importance of recent items in a long sequence. To solve this dilemma, we introduce an additional hyper-paramete p. which we call sampling probability. It implies that with probability Ps, we sample the starting index e uniformly from [1, t 2- 27] and use sequence s, = (jies Ji(++1)) .... ... J[(++-- 1>) as input. With probability 1 Pe. we simply use the recent T items (ji(t-T++) II: Jat) as input. If the sequence s; is already shorter than T. then we always
the recent input sequence for user i.
Our proposed SSE-PT++ model can work almost as well as SSE-PT with much smaller T. One can in Table with T 100, SSE-PT++ can perform almost as well SSE-PT, The time complexity
4

---

Under review as a conference paper at ICLR 2020
of the SSE-PT model is of order O + Td?). Therefore, reducing T by one half would lead to a theoretically 4x speed-up in terms of the training and inference speeds. As to the model's space complexity, both SSE-PT and SSE-PT++ are of order Ondd, + md; Td d.).
4 EXPERIMENTS
In this section, we compare our proposed algorithms, Personalized Transformer (SSE-PT) and SSE- PT++, with other state-of-the-an the-rrr algorithms on real-world datasets. We implement our codes in Tensorflow and conduct all our experiments a server with 40-core Intel Xeon E5-2630 v4 @ 2.20GHz CPU, 256G RAM and Nvidia GTX 1080 GPUs.
Datasets We use 5 datasets. The first have exactly the same train/dev/test splits as in (Kang and McAuley 2018). The datasets are: Beauty and Gamey categories from Amazon product review datasets Steam dataset introduced in (Kang and McAuley 2018). which contains reviews crawles from large video game distribution platform; Movielens M dataset (Harper and Konstan 2016), widely used benchmark datasets containing one million user movie ratings; Movielens with ten million user ratings cleaned by us. Detailed dataset statistics are given in Table 4 One can easily see that the first 3 datasets have short sequences (average length 12) while the last 2 datasets have very long sequences 10x longer).
dataset
Evaluation Metries The evaluation metrics we use are standard ranking metrics, namely NDCG and Recall for top recommendations (See Appendix). We follow the same evaluation setting the previous paper (Kang and McAuley| 2018): predicting ratings at time point t + 1 given the previous t ratings. For a large dataset with numerous users and items, the evaluation procedure would be slow because (6) would require computing the ranking of all items based on their predicted scores for every single user. As means of speed-up evaluations, we sample fixed number C (e-g.. 100) of negative candidates while always keeping the positive item that we know the user will engage next. This way, both R,y and II; will be narrowed down small set of item candidates, and prediction scores will only be computed for those items through single forward pass of the neural network.
Ideally, we want both NDCG and Recall be as close 1 as possible, because NDCG@ K 1 means the positive item is always put on the top-1 1 position of the top-K ranking list, and Recall @ K = 1 means the positive item always contained by the top- K recommendations the model makes.
Table I: Comparing various statc-of-the-art temporal collaborative ranking algorithms on various datasets. The (A) (E) are non-deep-lcanning methods, the (F) (K) are deep-learning methods and the (L) to (O) are our variants. We did not report SSE-PT++ results for beauty, games and steam, as the input sequence lengths are very short (see Table 4
so there is no need for SSE-PT++,
DATASET
BEAUTY RECALL
GAMES
STEAM
ML-1M
METRIC
NDCG@10
RECALL@
NDCG@10
RECALL
NDCG@10
RECALL 10 NDCG@
(A) POPREC
0.4003
0.2277
0.4724
0.2779
0.7172
0.4535
0.4329
0.2377
(B) BPR
0.3775
0.2183
0.4853
0.2875
0.7061
0.4436
0.5781
0.3287
(C)
0.3771
0.6358
0.7731
0.5193
0.6983
0.4676
(D) FPMC
0.1310
0,2891
0.6802
0.4680
0.7710
0.5011
0.7599
0.5176
(E) TRANSREC
0.4607
0.3020
0.6838
0.4557
0.7624
0.4852
0.6413
0.3969
GRUAREC
0.2691
(G) STAMP
0.4607
D.3020
0.6838
0.4557
0.7624
0.4852
0.6413
0.3969
(H) GRUAREC+
0.3949
0.2556
0.6599
0.4759
0,8018
0.5595
0.7501
0,5513
CASER
0.7874
0.5381
0.5538
(3) SASREC
0.4837
0.3220
0.7434
0.5401
0.8732
0.6293
0.8233
0.59366
(K) HGN
0.4469
0,2994
0,7164
0,5209
0,7426
0.4871
0.7584
0,5241
(L) SSE-SASREC
0.4878
D.3342
0.7517
0,5535
O. 0.8699
6333
0.8230
0.5995
(M) PT
0.3954
0,2449
0.6-127
0.4434
0.7535
0.4853
0.7658
0.5241
(N) ST
0.5028
0.3370
0.7757
0,5660
0.8772
0.6378
0.8341
0.6281
(0) SSE-PT++
0.8389
0.6292
Baselines We include non-deep-leamming and 6 deep-learning algorithms in our comparisons.
http://jnncalley.uuse.....
5

---

Under review as a conference paper at ICLR 2020
Table 2: Comparing SASRec, SSE-PT and SSE-PT++ Movielens M Dataset while varying the
maximum length allowed and dimension of embeddings.
METHODS
NDCG@10
RECALL @ 10
MAX LEN
USER DIM
ITEM DIM
SASREC
0.5769
0.8045
100
100
SASREC
0.5936
0,8233
200
N/A
50
SASREC
0.5919
0.8202
200
N/A
100
SSE-PT
0.6142
0.8212
100
100
SSE-PT
0.6191
0.8358
200
50
SSE-PT
0.6281
0.8341
200
50
100
SSE-PT++
0.6186
0.8318
100
100
SSE-PT++ 0.6208
0.8358
200
50
SSE-PT++ 0.6292
0.8389
200
50
100
Non-deep-learning Baselines The simplest baseline is PopRec, basically ranking items according to their popularity. More advanced methods such as matrix factorization based basclines include Bayesian personalized ranking for implicit feedback (Rendle et al.] 2209), namely BPR; Factorized Markov Chains and Personalized Factorized Markov Chains models (Rendle et al. (010) also known as FMC and PFMC; and translation based method (He et al. 2017) called TransRec.
Recent years have seen many advances in deep learning for sequential recommendations, GRU4Ree the first RNN-based method proposed for this problem (Hidasi et al.) 2015); GRU4Rec+ (Hidasi and Karatzoglou 2018) later is proposed to address some shortcomings of initial version. Caser the corresponding CNN-based method (Tang and Wang 2018). STAMP (Liu et al. 2018) utilizes the attention mechanism without using RNN CNN building blocks. Very recently, SASRec utilizes state-of-art Transformer encoder (Vaswani et al. 2017) with self-
Deep-learning Baselines
attention mechanisms. Hierarchical gating networks, also known as HGN (Ma et al. 2019) are also proposed to solve this problem.
Table 3: Comparing Different Regularizations for SSE-PT on Movielen I M Dataset. NO REG stands for no regularization. PS stands for parameter sharing across all users while PS(AGE) means PS is used within each age group. SASRec is added to last after all SSE-PT results baseline.
REGULARIZATION
NDCG@5
% GAIN
RECALL
% GAIN
NO REG (BASELINE)
0.4855
0.6500
PS
0.5065
4.3
0.6656
2.4
PS (JOB)
0.4938
0.6570
1.1
PS (GENDER)
0,5110
5.3
0.6672
2.6
PS (AGE)
0.5133
5.7
0.6743
3.7
0.5149
6.0
0.6786
4.4
DROPOUT
0,5165
6.4
0.6823
5.0
+ DROPOUT
0.5293
SSE-SE
0.5393
11.1
0.6977
7.3
1 SSE-SE + DROPOUT
0,5870
20.9
0.7442
14.5
SASREC (l2 DROPOUT)
0.5601
0.7164
Experiment Setup We use the same datasets as in (Kang and McAuley 2018) and follow the same procedure in the paper: use last items for each user as test data, second-to-last validation data and the rest as training data. We implemented our method in Tensorflow and solve it with Adam Optimizer (Kingma and Ba, 2014) with learning rate of 0.001, momentum exponential decay rates B1 = 0.9. $2 = 0.98 and batch size of 128. In Table since we use the same data, the performance of previous methods except STAMP have been reported in (Kang and McAuley 2018). We tune the dropout rate, and SSE probabilities Pa: Pi.Py for input user/item embeddings and output embeddings on validation sets and report the best NDCG and Recall for top-K recommendations on test sets. For fair comparison, For the SSE-PT and SASRec models, we use the same number of transformer encoder blocks (i.e. B 2) and set the maximum length T 200 for Movielens 1M and 10M dataset and T = 50 for
restrict all algorithms to up to 50 hidden units for item embeddings.

---

Under review as a conference paper at ICLR 2020
other datasets. We use top-K with K = 10 and the number of negatives C' = 100 in the evaluation procedure. In practice, using a different K and C' does not affect our conclusions.
Comparisons One can easily from Table that our proposed SSE-PT has the best performance over all previous methods on all four datasets. On most datasets, our SSE-PT improves NDCG by more than 4% when compared with SASRec (Kang and McAuley 2018) and more than 20% when compared to non-deep-learning methods. SSE-SE, together with dropout and weight decay, is the best choice for regularization, which is evident from Table E] SSE-SE is a more effective way to regularize our neural networks than any existent techniques including parameter sharing, dropout, weight decay. In practice, these SSE probabilities, just like dropout rate, can be treated as tuning parameters and easily tuned. Movielens 10fiilensMMMM 10M results are left to Table 6 in the Appendix.
Temporal Collaborative Ranking Problem
feedie Markey (199)) Dhnrs, Scit
Menii Koarte (1 beti
Chicken (901y Councly
I1 tieeecco Prosieett The cORt) Comnryy Dhres Foreever
Dese Provdees (1999 Orlice Drnms
(f Drome
17-5ems Simehil (155
When hight hi mntt theer
Zohmeee (150 Childee's,
tdaas Textttory Coreel
Whew Dalla crvers Caoeely
Staesttiny thom
H. civery actesn Cranmby
29 Ban Las legin (ieiiciooa.. Semmtioo
10: Desalia Deal and anvigg pivets Caraelyy Home
Haui Sapuos, 1901 Cn craws Thillar
21 Jusse su Tina C3VO1s Devmmatian
15, Caick is and (1V0) Autias Achurasss Rucaacc
Z1: Guallan (1MeiUAcios, Deats, Thiler
5. ou de Brik Fut BII Caralyy
Valiag Eitak (iedy Conndyy Deaas
4C Rusccatton Wittc Drara
Is Calla (iets Drana, Thiiker
Di:hoggmmen Miack Deooe
Et Toe aad thok AANNroA Chilbar'
Ever Rooms cThalll
Zie Starty Acree, Comeryy Draas
Xicirad Man 117 Draat
Museal Rerence
menry cortry Dhama
Soom dichve (1fote
Wige Coange
Recommendations
Recommendations by
By Vampire Stayer (1962) :Comedyy
(1952)) Corrady
Canspracy (1507):/hhile
The Dead Ho802)HHomor
466::The Englienman Who Went Up HI Out Came Down Mountain (1885):Comedy. Rarmanc,, Drims
648: Fever Comedd Drama
129 /Rude (1980): Drama
Attention Heat Map of SASReo
Attention Heat Map of Our PT
32
23
28
58
25
37
4
IS
1
22
45
10
52
4A
12
19
19
12
15
43
18
00
16
32 23 28
38
26
57
4
48
22
45
10
52
44
42
40
19
40
20
27
39
14
51 0
21
13 29
36 33 35
Figure 2: Illustration of how SASRec (Left) and SSE-PT (Right) differs on utilizing the Engagement History of Random User in Movielens1M Dataset.
4.1 ATTENTION MECHANISM VISUALIZATION
Apart from evaluating our SSE-PT against SASRec using well-defined ranking metrics on real- world datasets, we also visualize the differences between both methods in terms of their attention mechanisms. In Figure 2 random user's engagement history in Movielens IM dataset is given in temporal order colummnwisse) We hide the last item whose index is 26 in test set and hope that temporal collaborative ranking model can figure out 1em-266 is the one this user will watch next using only previous engagement history. One can see for a typical user; they tend look at different style of movies at different times. Earlier on, they watched a variety of movies, including Sci-Fi, animation, thriller, romance, horror, action, comedy and adventure. But later on, in the last two columns of Figure drama and thriller are the two types they like to watch most, especially the drama type. In fact, they watched drama movies out of recent 10 movies. For humans, is natural to reason that the hidden movie should probably also be drama type. So what about the machine's reasoning?
For our SSE-PT, the hidden item indexed 26 is put in the first place among its top-5 recommendations. Intelligently, the SSE-PT recommends 3 drama movies. 2 thriller movies and mixing them up in positions. Interestingly, the top recommendation is 'Othello', which like the recently watched 'Richard III' is an adaptation of Shakespeare play, and this dependence is reflected in the attention
7

---

Under review as a conference paper at ICLR 2020
weight. On the contrast, SASRec cannot provide top-5 recommendations that personalized enough. recommends a variety of action, Sci-Fi, comedy, horror, and drama movies but none them match item-26. Although this user has watched all these types of movies in the past, they do not watch these anymore as one can easily tell from his recent history. Unfortunately, SASRec cannot capture this and does not provide personalized recommendations for this user by focusing thriller movies. It is easy to see that in contrast, our SSE-PT model shares with human reasoning that more emphasis should be placed on recent movies.
drama and
4.2 TRAINING SPEED
In [Kang and McAuley 2018), has been shown that SAS- Rec is about II times laster than Caser and 17 times faster than GRU4Rec* and achieves much better NDCG@ @ 10 results so we did include Caser and GRU4Ree* in our comparisons. In Figure 3 we only compare the training speeds and ranking performances among SASRec, SSE- PT and SSE-PT++ for Moviclens IM dataset. Given that we added additional user embeddings into our SSE-PT model, it is expected that it will take slightly longer to train our model than un-personalized SASRec. We find empirically that training speed of the SSE-PT and SSE- PT++ model are comparable to that of SASRec, with SSE- PT++ being the fastest and the best performing model. It is clear that our SSE-PT and SSE-PT++ achieve much better ranking performances than our baseline SASRec using the same training time.
0.925
0.00
0.555
0.50
0.535
StiRee
0.450
200
400
660
1000
(seca)
Figure 3: Illustration of the speed of SSE-PT
4.3 ABLATION STUDY
SSE probability Given the importance of SSE regularization for our SSE-PT model, we carefully examined the SSE probability for input user embedding in Table in Appendix. We find that the appropriate hyper-paramettrr SSE probability not very sensitive: anywhere between 0.4 and 1.0 gives good results, better than parameter sharing and not using SSE-SE. This is also evident based on comparison results Table
Sampling Probability Recall that sampling probability is unique to our SSE-PT++ model. show in Table Appendix using appropriate sampling probability like 0.2 -> 0.3 would allow to outperform SSE-PT when the same maximum length used.
Number of Attention Blocks preferred. One can easily see in Table in Appendix, the optimal ranking performances are achieved B or 5 for Movielens M dataset and B 6 for Movielens 10M dataset.
We find for our SSE-PT model, larger number of attention blocks
Personalization and Number of Negatives Sampled Based the results in Table Appendix, we are positive that the personalized model always outperforms the un-personalized one when we use the same regularization techniques. This holds true regardless of how many negatives sampled or what ranking metries are used during evaluation.
CONCLUSION
In this paper, we propose a novel neural network architecture called Personalized Transformer for the temporal collaborative ranking problem. It enjoys the benefits being a personalized model, therefore achieving better ranking results for individual users than the current state-of-the-ee By examining the attention mechanisms during inference, the model also more interpretable and tends pay attention to recent items in long sequences than un-personalized deep learning models.
8

---

Under review as a conference paper at ICLR 2020
REFERENCES
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint 167.00660700445.0 2016.
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks sequence modeling. arXiv preprint arXiv:1412.3355, 2014.
Jacob Devlin, Ming- Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810004805. 2018.
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. Deep learning, volume 1. MIT press Cambridge, 2016.
F Maxwell Harper and Joseph A Konstan. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5(4):19, 2016.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference computer vision and pattern recognition, pages 770-778, 2016.
Ruining He, Wang-Chene Kang, and Julian McAuley. Translation- recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems, pages 161-169. ACM, 2017.
Balázs Hidasi and Alexandros Karatzoglou. Recurrent neural networks with top-k gains for session- based recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 843-852, ACM, 2018.
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.0690 2015.
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, llya Sutskever, and Ruslan R Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0588. 2012.
Arthur E Hoerl and Robert W Kennard, Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1):55-67, 1970.
Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.
Wang-Cheng Kang and Julian McAuley. Self-attentive sequential recommendation. arXiv preprint arXiv:1808.09781 2018
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint 1412,690,,, 2014.
Ychuda Koren. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 447-456. ACM, 2009.
Anders Krogh and John A Hertz. A simple weight decay can improve generalization. In Advances in neural information processing systems, pages 950 950,957,-1 1992.
Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. Stamp: short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1831-1839. ACM, 2018.
Chen Ma, Peng Kang, and Xue Liu. Hierarchical gating networks for sequential recommendation. arXiv preprint arXiv:
2019.
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. On the difficulty of training recurrent neural networks. In International Conference Machine Learning, pages 1310-1318, 2013.

---

Under review as a conference paper at ICLR 2020
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Sehmidt-Thiema Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pages 452-461. AUAI Press, 2009.
Steften Rendle, Christoph Freudenthaler, and Lars Schmidt-Thiem. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web, pages 811-820. ACM, 2010.
Badrul Munir Sarwar, George Karypis, Joseph A Konstan, John Riedl, et Item-based collaborative filtering recommendation algorithms. Www, 1:285-295, 2001.
Nathan Srebro, Jason Rennie, and Tommi S Jaakkola. Maximum- Maximum-margin margin matrix factorization. In Advances in neural information processing systems, pages 1329-1336, 2005.
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15()):1929-195, 2014.
Jiaxi Tang and Ke Wang. Personalized top-n sequential recommendation via convolutional sequence embedding. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pages 565-573. ACM, 2018.
Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological)) pages 267-288, 1996.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, pages 5998 6008, 2017.
Liwei Wu, Cho-Jui Hsieh, and James Sharpnack. Sql-rank: A listwise approach to collaborative ranking. In Proceedings of Machine Learning Research (35th International Conference on Machine Learning), volume 80, 2018.
Liwei Wu, Shuqing Li, Cho-Jui Hsieh, and James Sharpnack. Stochastic shared embeddings: Data- driven regularization of embedding layers. arXiv preprint 1905 2019.
10

---

Under review as a conference paper at ICLR 2020
APPENDIX
NDCG@K: defined as:
(1, (T.)
NDCG@K=
(6)
DCG@K(i,I,,
where represents i-th user and
K
2am
DCG@K(i,II, II,)
Σ 1=1
log2
In the DCG definition, II represents the index of the I-th ranked item for user in test data based on the learned score matrix X. R is the rating matrix and R, is the rating given to item by user i. II; is the ordering provided by the ground truth rating.
Recall K: defined fraction of positive items retrieved by the top K recommendations the model makes:
Recall@K Ei= 1(31 < K Ran, =1}
here we already assume there is only single positive item that will engage next and the indicator function 1 1131 < S k RaI., 1 is defined to indicate whether the positive item falls into the top K position our obtained ranked list using scores predicted in (4.
Layer Normalization Layer normalization (Ba al.) 2016) normalizes neurons within a layer. Previous studies (Ba et al. 2016) show is more effective than batch normalization for training recurrent neural networks (RNNs). One alternative is the batch normalization (Toffe and Szegedy 2015) but we find does not work as well as the layer normalization in practice even for reasonable large batch size of 128. Therefore, our SSE-PT model adopts layer normalization.
Residual Connections Residual connections firstly proposed in ResNet for image classification problems (He al. 2016). Recent research finds that residual connections can help training very deep neural networks even if they are convolutional neural networks (Vaswani al. (017). Using residual connections allows us to train very deep neural networks here. For example, the best performing model for Movielens IOM dataset in Table is the SSE-PT with attention blocks, in which 1 + 6 3 = 20 layers are trained end-to-end.
Weight Decay Weight decay (Krogh and Hertz 1992). also known as l2 regularization (Hoerl and Kennard 1970,, is applied to all embeddings, including both user and item embeddings.
Dropout (Srivastava et al.) 2014) is applied to the embedding layer E, self-attention layer and pointwise feed-forward layer by stochastically dropping some percentage of hidden units to prevent co-adaption of neurons. Dropout has been shown to be an effective way of regularizing deep learning models.
Dropout
In summary, layer normalization and dropout are used in all layers except prediction layer. Residual connections are used in both self-attention layer and pointwise feed-forward layer. SSE-SE used in embedding layer and prediction layer.
Table 4: Description of Datasets Used in Evaluations.
DATASET
#USERS
#ITEMS
AVG SEQUENCE LEN
MAX SEQUENCE LEN
BEAUTY
52,024
57,289
7.6
291
GAMES
31,013
23,715
858
STEAM
334,730
13,047
11.0
1,229
ML- 1-IM
6.040
3,416
163.5
2,275
ML-10M 10M
69.878
65,133
141.1
7,357
11

---

Under review as a conference paper at ICLR 2020
Table 5: Comparing our SSE-PT, SSE-PT++ with SASRec on MM dataset. We use number of negatives C 100, dropout probability 0.2 and learning rate of le- for all experiments while varying others. Pa, Pi. Pv. are SSE probabilities for user embedding, input item embedding and output item embedding respectively.
MovielensIm
Dimensions
Number of Blocks
Sampling Probability
SSE-SE Parameters
Model
NDCG@10
Recall<r1 de
d,
P4
Pu
P
Py
SASRec
0.5961
0.8195
50
SASRec
0.5941
0.8182
100
SASRee
0.5996
0.8272
100
SSE-PT
0,6101
0.8343
50
50
0.92
0.1
0
SSE-PT
0.6164
0.8336
50
50
0.92
0.1
SSE-PT
0.5832
0.8091
50
0.1
0.1
SSE-PT
0.6174
0.8351
50
0.92
0.1
0.1
SSE-PT
0,5949
0.8205
25
0,92
0.1
0.1
SSE-PT
0,6214
0.8359
25
75
0.92
0.1
0.1
SSF-PT 0.6281
0.8341
50
100
0.92
0.1
0.1
SSE-PT++ 0.6292
0.8389
50
100
0.3
0.92
0.1
0.1
Table 6: Comparing our SSE-PT with SASRec on Movielens 10M dataset. Unlike Table we use the number of negatives C' 500 instead of 100 C 100 easy for this dataset and gets difficult to tell the differences between different methods: Hit Ratio 10 approaches 1.
MoviclensIm
Dimensions
Number of Blocks
SSH-SE Parameters
Model
NDCG@10
Ratio@10
du
d
Pu
Pi
Pv
SASRec
0.7268
0,9429
50
SASRec
0.7413
0.9474
100
SSE-PT
0.7199
0.9331
50
PS
0.01
0.01
SSE-PT
0.7169
0,9296
50
100
0.0
0.01
0.01
SSE-PT
0.7398
0,9418
50
100
0.2
0.01
0.01
SSE-PT
0.7500
0,9500
50
100
0.4
0.01
0.01
SSE-PT 0.7484
0,9480
50
100
0.6
0.01
0.01
SSE-PT 0.7529
0,9485
50
100
0.8
0.01
0.01
SSE-PT
0.7503
0.9505
50
100
2
1.0
0.01
0.01
PopRec: ranking items according to their popularity.
BPR:
Bayesian personalized ranking for implicit feedback setting (Rendle al., 2009).
It is a low-rank matrix factorization model with pairwise loss function. But does not utilize the temporal information. Therefore, it serves as strong bascline for non-temporal methods.
FMC: Factorized Markov Chains: a first-order Markov Chain method, in which predictions are made only based on previously engaged item.
PFMC: personalized Markov chain model (Rendle et al. 2010) that combines matrix factorization and first-order Markov Chain to take advantage of both users' latent long-tent term preferences as well as short-term item transitions.
TransRec: a first-order sequential recommendation method (He et al. 2017) in which items embedded into transition space and users modelled translation vectors operating on item sequences.
SQL-Rank (Wu al. 2018) and item-based recommendations (Sarwar al. 2001) omitted because the former similar to BPR (Rendle et al. (209) except using the listwise loss function instead of the pairwise loss function and the latter has been shown inferior to TransRec (He et al.) 2017).
6.0.1 DEEP-LEARNING BASELINES
GRU4Rec: the first RNN-bai based method proposed for the session-based recommendation problem (Hidasi et al. 2013). It utilizes the GRU structures (Chung et al., 2014) initially proposed for speech modelling.
12

---

Under review as a conference paper at ICLR 2020
GRU4Rec*: follow-up work of GRU4Rec by the same authors: the model has very similar architecture to GRU4Rec but has a more complicated loss function (Hidasi and Karatzoglou 2018).
Caser: a CNN-based method (Tang and Wang 2018) which embeds sequence of recent items both time and latent spaces forming an 'mmage" before learning local features through horizontal and vertical convolutional filters. In (Tng and Wang 2018), user embeddings included in the prediction layer only. On the contrast, in our Personalized Transformer, user embeddings are also introduced in the lowest embedding layer they can play important role in self-attention mechanisms as well as in prediction stages.
STAMP: session-based recommendation algorithm (Liu et al. 2018) using attention mechanism. (Liu et al. 2018) only uses fully connected layers with one attention block that is not self-attentive.
SASRec: self-attentive sequential recommendation method (Kang and McAuley 2018) motivated by Transformer in NLP (Vaswani et al., 2017). Unlike method SSE-PT, SASRec does not incorporate user embedding and therefore is not personalized method. SASRec paper (Kang and McAuley 2018) also does not utilize SSE (Wu et al. 2019) for further regularization: only dropout and weight decay are used.
HGN: hierarchical gating networks method solve the sequential recommendation problem (a et al., 2019), which incorporates the user embeddings and gating networks for better personalization than the SASRec model.
Table 7: Comparing Different SSE probability for user embeddings for SSE-PT Moviclens LM Dataset. Embedding hidden units of 50 for users and 100 for items, attention blocks of 2, SSE probability of 0.01 for item embeddings, dropout probability of 0.2 and max length of 200 used.
USER-SIDE SSE-SE PROBABILITY
NDCG @ 10
RECALL @ 10
PARAMETER SHARING
0.6188
0.8294
1.0
0.6258
0.8346
0.9
0.6275
0.8321
0.8
0.6244
0.8359
0.6
0.6256
0.8341
0.4
0.6237
0.8369
0.2
0.6163
0.8281
0.0
0.5908
0.8048
Table 8: Comparing Different Sampling Probability, p.. of SSE-PT++ Movielens IM Dataset. prrer-pprameters the same as Table 7 except that the max length T allowed is set 100 instead 200 to show effects of sampling sequences.
SAMPLING PROBABILITY
NDCG@10
RECALL @10 10
SASREC (T 100)
0.5769
0.8045
SSE-PT (T 100)
0,6142
0.8212
0.5697
0.7977
0.8
0.5735
0.7801
0.6
0,6062
0.8242
0.6113
0.8273
0.3
0.6186
0.8318
0.2
0.6193
0.8233
0.0
0.6142
0.8212
13

---

Under review as a conference paper at ICLR 2020
Table 9: Comparing Different Number of Blocks for SSE-PT while Keeping The Rest Fixed on Movielens M and Movielens10M Datasets.
BLOCKS
NDCG@
SASREC (6 DLOCKS)
0.5984
0.8207
0.6162
0.8301
0.6280
0.8365
MOVIELENSIM
0.6293
0.8376
0.6270
0.8401
0,6270
0.8397
SASREC (6 BLOCKS)
0,7531
0,9490
0.7454
0,9478
0.7512
0,9522
1OVIELENS
0.7543
0.9491
0.7608
0,9485
0.7619
0.9524
0.7683
0.9537
Table 10: Varying number of negatives C' in evaluation on Movielens M dataset. Other hyper- parameters are fixed for fair comparison.
METRIC
NDCG@10
RECALL
UN-PEESONALIIEED
0.3787
0.6119
S00
PERSONALIZED
0.3846
0.6171
UN- PERSONSONALDE
0.2791
0.4781
1000
PERSONALIZED
0.2860
0.4929
1000
DI-PERSONALIZED
0.1939
0.3515
2000
PERSONALIZED
0.1993
0.3667
2000
14


Reviews:
[{"review_id": "SJenAyg6tH", "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": "The manuscript proposes SSE-PT, a sequential recommendation model based on transformer and stochastic shared embedding (SST). Experiments on several datasets show that SSE-PT outperforms a number of baseline methods. Some analytical results are also provided. Overall, I think this work is not suitable for ICLR due to following reasons. \n\nThe novelty of this work is limited. This work is based on SASREC [W Kang, ICDM2018] and uses transformer to encode user-item interactions in sequential manner. The difference is that this work adds user embedding in bottom layer and utilizes SSE for regularization as well as designs SSE-PT++ by sampling. To me, there is little extension or novelty. \n\nThe experiment results are not convincing. Most of results are copied from [W Kang, ICDM2018] except HGN in Table 1. Table 1 shows SASREC is much better than HGN [C Ma, KDD2019]. However, I checked the results in HGN paper and found HGN is much better than SASREC. Even though datasets are different, most of them are from Amazon data. I was not convinced by this result due to the large difference. In addition, I did not understand why the authors change evaluation metrics in Table 3, i.e., from NDCG/Recall@10 to NDCG/Recall@5. I found SSE-PT without regularization and with different regularizations are much worse than the best result, which makes me concern about the effectiveness of personalized transformer. I did not see ablation study or discussion about this. \n\nUpdate: I have considered author rebuttal. I appreciate the extensive hyper-parameter sensitivity and ablation study in the paper, while these cannot be a key factor in evaluating paper as most of them can be done easily. I main concerns still lie in the novelty and experimental results. I still think this work is not suitable for ICLR and I keep my score. ", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_derivations_and_theory": "N/A"}, "extra_text": {}}, {"review_id": "Hyg-LKcJcS", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 6, "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 SSE-PT for sequential recommendation, which is an extension of previous work SASRec by adding user embedding with SSE  regularization [Wu et al. 2019] . They further extend SSE-PT to SSE-PT++ to handle longer sequence. Experiments on five datasets show that the SSE-PT and SSE-PT++ outperform several baseline approaches.\n\nDetailed comments:\n\n1)\tThe technical contribution seems to be scattered: user embedding is introduced, effect of different types of regularization is studied and sampling based approach is added to address long sequence. It could be better if the author could make clear what the major contribution of this paper is. Also, SSE [Wu et al. 2019] is existing technique and simply applying it to sequential recommendation is a bit incremental.\n\n2)    In addition to SASRec, there are some other transformer based model (e.g., [1]) for sequential recommendation and the paper discuss how the proposed method differ from them.\n\n3)\tIn SSE-PT++, would sampling start index v based on the recency (e.g., with exponential decay) make more sense than uniform probability?\n\n4） Overall, experiments look comprehensive: The baseline methods include both non-deep-learning methods and recent deep learning based methods for sequential recommendation; ablation study is conducted; case study is performed on MovieLens to show how the attention weights differ from SASRec; running time is compared against baselines and sensitivity analysis on hyper-parameters are also provided. \n\n\nTo summarize, the paper is a bit incremental/scattered in terms of technical contribution but the execution of this paper looks solid. I would give a “weak accept” to this paper given the reasons listed above.\n\n\n[1] F. Sun et. al. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer\n", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "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": {}}, {"review_id": "B1xkMwxJYB", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "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": "In this paper, the authors study an important recommendation problem, i.e., sequential recommendation, and design a novel and improved model called SSE-PT (Stochastic Shared Embedding - Personalized Transformer). Specifically, the authors mainly follow the previous works of the Transformer model and the stochastic shared embedding (SSE) regularization technique. For the part of the personalized transfer (PT), the authors introduce the user embedding for each user $i$, i.e., $u_i$, shown in Eq.(2) and illustrated in Figure 1. For the part of regularization, the authors find that the SSE technique works well in terms of avoid overfiting in context of other regularization techniques.\n\nExtensive empirical studies on five datasets show the effectiveness of the proposed approach compared with other related methods.\n\nOverall, the paper is very well presented, in particular of the introduction and discussion about the related works, and the analysis of the experimental results. \n\nMy major concern is that the technical novelty is somehow limited in terms of the two closely related works of Transformer and stochastic shared embedding (SSE). I thus recommend weak acceptance.\n\nSome suggestion: Some important baseline methods may be included to make the results more convincing, e.g., Fossil, MARank, and/or BERT4Rec.\n\nSome minors:\nTypo: in the paragraph below Eq.(3): user $l$ -> user $i$\nTypo: FPMF, PFMC in different places\n", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "N/A", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "N/A"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes to improve sequential recommendation by extending SASRec (from prior work) by adding user embedding with SSE regularization.  The authors show that the proposed method outperforms several baselines on five datasets.

The paper received two weak accepts and one reject.  Reviewers expressed concerns about the limited/scattered technical contribution.  Reviewers were also concerned about the quality of the experiment results and need to compare against more baselines.  After examining some related work, the AC agrees with the reviewers that there is also many recent relevant work such as BERT4Rec that should be cited and discussed.  It would make the paper stronger if the authors can demonstrate that adding the user embedding to another method such as BERT4Rec can improve the performance of that model.  Regarding R3's concerns about the comparison against HGN, the authors indicates there are differences in the length of sequences considered and that some method may work better for shorter sequences while their method works better for longer sequences.  These details seems important to include in the paper. 

In the AC's opinion, the paper quality is borderline and the work is of limited interest to the ICLR community.  Such would would be more appreciated in the recommender systems community.  The authors are encouraged to improve the paper with improved discussion of more recent work such as BERT4Rec, add comparisons against these more recent work, incorporate various suggestions from the reviewers, and resubmit to an appropriate venue.
Decision: Reject