Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation

Abstract

Session-based recommendation (SR) has become an important and popular component of various e-commerce platforms, which aims to predict the next interacted item based on a given session. Most of existing SR models only focus on exploiting the consecutive items in a session interacted by a certain user, to capture the transition pattern among the items. Although some of them have been proven effective, the following two insights are often neglected. First, a user's micro-behaviors, such as the manner in which the user locates an item, the activities that the user commits on an item (e.g., reading comments, adding to cart), offer fine-grained and deep understanding of the user's preference. Second, the item attributes, also known as item knowledge, provide side information to model the transition pattern among interacted items and alleviate the data sparsity problem. These insights motivate us to propose a novel SR model MKM-SR in this paper, which incorporates user Micro-behaviors and item Knowledge into Multi-task learning for Session-based Recommendation. Specifically, a given session is modeled on micro-behavior level in MKM-SR, i.e., with a sequence of item-operation pairs rather than a sequence of items, to capture the transition pattern in the session sufficiently. Furthermore, we propose a multi-task learning paradigm to involve learning knowledge embeddings which plays a role as an auxiliary task to promote the major task of SR. It enables our model to obtain better session representations, resulting in more precise SR recommendation results. The extensive evaluations on two benchmark datasets demonstrate MKM-SR's superiority over the state-of-the-art SR models, justifying the strategy of incorporating knowledge learning.

References in corpus (8)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• An Overview of Multi-Task Learning in Deep Neural Networks
• Session-based Recommendation with Graph Neural Networks
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
• Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
• A Survey on Session-based Recommender Systems

Cited by in corpus (11)

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• Multi-Behavior Sequential Recommendation with Temporal Graph Transformer
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• Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
• Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
• M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations
• Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data
• Exploring Global Information for Session-based Recommendation
• Multi-behavior Graph Contextual Aware Network for Session-based Recommendation
• Exploiting Group-level Behavior Pattern forSession-based Recommendation
• Matryoshka Model Learning for Improved Elastic Student Models