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Context: Recent advances in contrastive SSLhave inspired therecommendation communityto leverage contrastive SSL tofuse correlations amongdifferent views of one sequence (Chenet al., 2020; Yao et al., 2020; Wuet al., 2021),following themutual information maximization (MIM) principle.Existing approaches in SRcan be seen asinstance discrimination tasksthat optimize a lower bound of MIM,such as InfoNCE (Oordet al., 2018; Heet al., 2020b; Chenet al., 2020; Liet al., 2020b).It aims to optimize theproportion of gap of positive pairs and negative pairs (Liu et al., 2021c).In such an instance discrimination task,sequence augmentations such as ‘mask’, ‘crop’, or ‘reorder’ are required tocreatedifferent views of the unlabeled data in SR (Sunet al., 2019; Zhou et al., 2020; Xieet al., 2020; Zhou et al., 2021).Formally, given a sequence S^{u},and a pre-defined data transformationfunction set \mathcal{G}, we can createtwo positive views of S^{u} as follows:(4)\tilde{S}^{u}_{1}=g_{1}^{u}(S^{u}),\tilde{S}^{u}_{2}=g_{2}^{u}(S^{u}),\text{ s.t. }g_{1}^{u},g_{2}^{u}\sim\mathcal{G},where g_{1}^{u} and g_{2}^{u} are transformation functions sampledfrom \mathcal{G} to createa different view of sequence s_{u}.Commonly, views created from the same sequenceare treated as positive pairs,and the views of any different sequencesare considered as negative pairs.The augmented views are first encoded with thesequence encoder f_{\theta}(\cdot) to\mathbf{\tilde{H}}^{u}_{1} and \mathbf{\tilde{H}}^{u}_{2},and then be fed into an ‘Aggregation’layer to get vector representationsof sequences, denoted as \mathbf{\tilde{h}}^{u}_{1} and \mathbf{\tilde{h}}^{u}_{2}. In this paper,we ‘concatenate’ users’ interest representations over time stepsfor simplicity. Note that sequences are prepossessed to have the same length (See Sec. 3.1), thustheir vector representations after concatenationhave the same length too.After that,we can optimize \theta via InfoNCE loss:(5)\mathcal{L}_{\mathrm{SeqCL}}=\mathcal{L}_{\mathrm{SeqCL}}(\mathbf{\tilde{h}}^{u}_{1},\mathbf{\tilde{h}}^{u}_{2})+\mathcal{L}_{\mathrm{SeqCL}}(\mathbf{\tilde{h}}^{u}_{2},\mathbf{\tilde{h}}^{u}_{1}),and(6)\mathcal{L}_{\mathrm{SeqCL}}(\mathbf{\tilde{h}}^{u}_{1},\mathbf{\tilde{h}}^{u}_{2})=-\log\frac{\exp(\text{sim}(\mathbf{\tilde{h}}^{u}_{1},\mathbf{\tilde{h}}^{u}_{2}))}{\sum_{neg}\exp(\text{sim}(\mathbf{\tilde{h}}^{u}_{1},\mathbf{\tilde{h}}_{neg}))},where sim(\cdot) is dot product and\mathbf{\tilde{h}}_{neg} are negativeviews’ representations of sequence S^{u}.Figure 2(a) illustrates how SeqCL works. Sequential recommendation aimsto accurately characterize users’ dynamic interestsby modeling their past behavior sequences (Rendle, 2010; Rendle et al., 2010; Kang and McAuley, 2018; Chen et al., 2021; Liet al., 2021a; Liuet al., 2021a).Early works on SR usually modelanitem-to-item transaction patternbased on MarkovChains (Rendle, 2010; He and McAuley, 2016).FPMC (Rendle et al., 2010)combines the advantages of Markov Chainsand matrix factorizationto fuse both sequential patternsand users’ general interest.With the recent advances of deep learning,many deep sequential recommendation modelsare also developed (Tang and Wang, 2018; Hidasi et al., 2015; Kang and McAuley, 2018; Sunet al., 2019). Such as Convolutional Neural Networks (CNN)-based (Tang and Wang, 2018) andRNN-based (Hidasi et al., 2015) models.The recent success of Transformer (Vaswani et al., 2017)also motivatesthe developments of pure Transformer-based SR models.SASRec (Kang and McAuley, 2018)utilizes unidirectional Transformerto assign weights to each interacted item adaptively.BERT4Rec (Sunet al., 2019) improves that byutilizing a bidirectional Transformerwith a Cloze task (Taylor, 1953)to fuse user behaviors information fromleft and right directions into each item.LSAN (Liet al., 2021a) improves SASRecon reducing model size perspective.It proposes a temporal context-aware embeddingand twin-attention network, which are light weighted.ASReP (Liuet al., 2021b) further alleviatesthe data-sparsity issueby leveraging a pre-trained Transformeron the revised user behavior sequences toaugment short sequences.In this paper, we study thepotential of addressing data sparsity issuesand improving SR via self-supervised learning. Contrastive Self-Supervised Learning (SSL)has brought much attentions bydifferent research communitiesincluding CV (Chenet al., 2020; Liet al., 2020b; Heet al., 2020b; Caron et al., 2020; Khosla et al., 2020) andNLP (Gao et al., 2021; Gunelet al., 2020; Mnih andKavukcuoglu, 2013; Zhanget al., 2020),as well asrecommendation(Yao et al., 2020; Zhou et al., 2020; Wuet al., 2021; Xieet al., 2020).The fundamental goal of contrastive SSLis to maximize mutual informationamong the positive transformationsof the data itself whileimprovingdiscrimination abilityto the negatives.In reccommendation,A two-tower DNN-basedcontrastive SSLmodel is proposed in (Yao et al., 2020).Itaimstoimproving collaborative filteringbasedrecommendation leveraging item attributes.SGL (Wuet al., 2021) adoptsa multi-task framework withcontrastive SSL to improve thegraph neural networks (GCN)-basedcollaborative filtering methods (Heet al., 2020a; Wanget al., 2019a; Liuet al., 2020b; Zhang and McAuley, 2020)with only item IDs as features.Specific to SR,S{}^{3}\text{-Rec} (Zhou et al., 2020)adopts a pre-training andfine-tuning strategy, and utilizescontrastive SSL during pre-trainingto incorporate correlationsamong items, sub-sequences, and attributes of a givenuser behavior sequence.However, the two-stage training strategyprevents the information sharing between next-item prediction and SSL tasks and restrictsthe performance improvement.CL4SRec (Xieet al., 2020) andCoSeRec (Liuet al., 2021a)insteadutilize a multi-task training frameworkwith a contrastive objectiveto enhance user representations.Different from them, our work is aware ofusers’ latent intent factor whenleveraging contrastive SSL,which we show to bebeneficial forimproving recommendationperformanceand robustness.

Question: What properties of costrastive self-supervised learning have attracted attention from researchers in the recommendation field?
Sequences of user behavior can be maximally separated or brought together by means of contrastive SSL.