GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation
Jie Zhang, Ke-Jia Chen, Jingqiang Chen

TL;DR
GIMIRec is a novel sequential recommendation model that leverages global co-occurrence information and time intervals to better capture diverse user interests, significantly improving recommendation accuracy.
Contribution
The paper introduces a global context extraction module using co-occurrence and graph convolution, enhancing multi-interest modeling without external data.
Findings
Outperforms state-of-the-art methods on three real-world datasets.
Global context extraction improves interest diversity representation.
Module can be integrated into existing models easily.
Abstract
Sequential recommendation based on multi-interest framework models the user's recent interaction sequence into multiple different interest vectors, since a single low-dimensional vector cannot fully represent the diversity of user interests. However, most existing models only intercept users' recent interaction behaviors as training data, discarding a large amount of historical interaction sequences. This may raise two issues. On the one hand, data reflecting multiple interests of users is missing; on the other hand, the co-occurrence between items in historical user-item interactions is not fully explored. To tackle the two issues, this paper proposes a novel sequential recommendation model called "Global Interaction Aware Multi-Interest Framework for Sequential Recommendation (GIMIRec)". Specifically, a global context extraction module is firstly proposed without introducing any…
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Taxonomy
TopicsRecommender Systems and Techniques · Advanced Graph Neural Networks · Sentiment Analysis and Opinion Mining
MethodsAttentive Walk-Aggregating Graph Neural Network
