Context-aware short-term interest first model for session-based recommendation

TL;DR

This paper introduces CASIF, a session-based recommendation model that combines context-aware graph neural networks with a short-term interest focus to improve recommendation accuracy without user profiles.

Contribution

The paper proposes a novel model that dynamically constructs session graphs and integrates short-term and long-term interests for enhanced session-based recommendations.

Findings

CASIF outperforms existing methods on real-world datasets.

The model effectively captures context dependencies and user interests.

Experimental results show significant accuracy improvements.

Abstract

In the case that user profiles are not available, the recommendation based on anonymous session is particularly important, which aims to predict the items that the user may click at the next moment based on the user's access sequence over a while. In recent years, with the development of recurrent neural network, attention mechanism, and graph neural network, the performance of session-based recommendation has been greatly improved. However, the previous methods did not comprehensively consider the context dependencies and short-term interest first of the session. Therefore, we propose a context-aware short-term interest first model (CASIF).The aim of this paper is improve the accuracy of recommendations by combining context and short-term interest. In CASIF, we dynamically construct a graph structure for session sequences and capture rich context dependencies via graph neural network (GNN), latent feature vectors are captured as inputs of the next step. Then we build the short-term interest first module, which can to capture the user's general interest from the session in the context of long-term memory, at the same time get the user's current interest from the item of the last click. In the end, the short-term and long-term interest are combined as the final interest and multiplied by the candidate vector to obtain the recommendation probability. Finally, a large number of experiments on two real-world datasets demonstrate the effectiveness of our proposed method.