Exploiting Positional Information for Session-based Recommendation

Abstract

For present e-commerce platforms, session-based recommender systems are developed to predict users' preference for next-item recommendation. Although a session can usually reflect a user's current preference, a local shift of the user's intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user's initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability to capture the significance of interactions at different positions. To thoroughly exploit the positional information within a session, a theoretical framework is developed in this paper to provide an in-depth analysis of the positional information. We formally define the properties of forward-awareness and backward-awareness to evaluate the ability of positional encoding schemes in capturing the initial and the latest intention. According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session. To enhance the positional encoding scheme for the session-based recommendation, a dual positional encoding (DPE) is proposed to account for both forward-awareness and backward-awareness. Based on DPE, we propose a novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module to fully exploit the positional information for session-based recommendation tasks. Extensive experiments are conducted on two e-commerce benchmark datasets, Yoochoose and Diginetica and the experimental results show the superiority of the PosRec by comparing it with the state-of-the-art session-based recommender models.

References in corpus (14)

• Adam: A Method for Stochastic Optimization
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Session-based Recommendation with Graph Neural Networks
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
• Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
• Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks
• Using Temporal Data for Making Recommendations
• GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
• General factorization framework for context-aware recommendations
• Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
• Context-Aware Attention-Based Data Augmentation for POI Recommendation
• Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Cited by in corpus (8)

• Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
• Lightweight Self-Attentive Sequential Recommendation
• Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation
• Exploiting Session Information in BERT-based Session-aware Sequential Recommendation
• Classification of Edge-dependent Labels of Nodes in Hypergraphs
• Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis
• Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection
• Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation