Collaborative Co-Attention Network for Session-Based Recommendation

Wanyu Chen,Honghui Chen

doi:10.3390/math9121392

Abstract

Session-based recommendation aims to model a user’s intent and predict an item that the user may interact with in the next step based on an ongoing session. Existing session-based recommender systems mainly aim to model the sequential signals based on Recurrent Neural Network (RNN) structures or the item transition relations between items with Graph Neural Network (GNN) based frameworks to identify a user’s intent for recommendation. However, in real scenarios, there may be strong sequential signals existing in users’ adjacent behaviors or multi-step transition relations among different items. Thus, either RNN- or GNN-based methods can only capture limited information for modeling complex user behavior patterns. RNNs pay attention to the sequential relations among consecutive items, while GNNs focus on structural information, i.e., how to enrich the item embedding with its adjacent items. In this paper, we propose a Collaborative Co-attention Network for Session-based Recommendation (CCN-SR) to incorporate both sequential and structural information, as well as capture the co-relations between them for obtaining an accurate session representation. To be specific, we first model the ongoing session with an RNN structure to capture the sequential information among items. Meanwhile, we also construct a session graph to learn the item representations with a GNN structure. Then, we design a co-attention network upon these two structures to capture the mutual information between them. The designed co-attention network can enrich the representation of each node in the session with both sequential and structural information, and thus generate a more comprehensive representation for each session. Extensive experiments are conducted on two public e-commerce datasets, and the results demonstrate that our proposed model outperforms state-of-the-art baseline model for session based recommendation in terms of both Recall and MRR. We also investigate different combination strategies and the experimental results verify the effectiveness of our proposed co-attention mechanism. Besides, our CCN-SR model achieves better performance than baseline models with different session lengths.

Highlights

Introduction iationsWith the information increasing at a rapid speed on the Internet, recommender systems have been proposed to provide users with their required information in an efficient way [1,2,3,4]
When comparing among the neural-based approaches, for the Recurrent Neural Network (RNN)-based models, i.e., GRU4Rec and Neural Attentive Recommendation Machine (NARM), we can see that NARM generally outperforms GRU4Rec in terms of Recall@20 and MRR@20 on both of the two datasets
As for the Graph Neural Network (GNN)-based model, i.e., SR-GNN, it achieves the best performance among all of the baseline models, which indicates that modeling the transition relations between adjacent items can help to improve the recommendation accuracy

Summary

Introduction

With the information increasing at a rapid speed on the Internet, recommender systems have been proposed to provide users with their required information in an efficient way [1,2,3,4]. Many general recommendation approaches rely on users’ historical behaviors to make personalized recommendations. Collaborative filtering (CF) [5] builds the user-item interaction matrix and learns user as well as item representations so as to fill in the matrix and make recommendations. Personal information, e.g., user IDs, is not available. It is a challenging task to recommend users satisfying items only based on limited behaviors in a session in those cases. Session-based recommendation (SBRS) is proposed to deal with the task and make recommendations based on an ongoing session [3]

Objectives

Methods

Results

Conclusion