Session-aware Information Embedding for E-commerce Product Recommendation

Abstract

Most of the existing recommender systems assume that user's visiting history can be constantly recorded. However, in recent online services, the user identification may be usually unknown and only limited online user behaviors can be used. It is of great importance to model the temporal online user behaviors and conduct recommendation for the anonymous users. In this paper, we propose a list-wise deep neural network based architecture to model the limited user behaviors within each session. To train the model efficiently, we first design a session embedding method to pre-train a session representation, which incorporates different kinds of user search behaviors such as clicks and views. Based on the learnt session representation, we further propose a list-wise ranking model to generate the recommendation result for each anonymous user session. We conduct quantitative experiments on a recently published dataset from an e-commerce company. The evaluation results validate the effectiveness of the proposed method, which can outperform the state-of-the-art significantly.

References in corpus (2)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Session-based Recommendations with Recurrent Neural Networks

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• A Survey on Session-based Recommender Systems
• Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
• Towards Neural Mixture Recommender for Long Range Dependent User Sequences
• Exploiting Session Information in BERT-based Session-aware Sequential Recommendation
• Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels
• Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
• Time-weighted Attentional Session-Aware Recommender System
• Inferring Substitutable and Complementary Products with Knowledge-Aware Path Reasoning based on Dynamic Policy Network