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Graph-Augmented Co-Attention Model for Socio-Sequential Recommendation | IEEE Journals & Magazine | IEEE Xplore

Graph-Augmented Co-Attention Model for Socio-Sequential Recommendation


Abstract:

A sequential recommendation has become a hot research topic, which seeks to predict the next interesting item for each user based on his action sequence. While previous m...Show More

Abstract:

A sequential recommendation has become a hot research topic, which seeks to predict the next interesting item for each user based on his action sequence. While previous methods have made many efforts to capture the dynamics of sequential patterns, we contend that they still suffer from two inherent limitations: 1) they fail to model item transition patterns in an efficient and time-sensitive manner and 2) they are unaware of the importance of dynamically capturing social influence, resulting in suboptimal performance. We introduce a new concept dubbed socio-sequential recommendation, where the challenge mainly lies in dynamically modeling social influences and capturing item-to-item transition patterns in a time-sensitive manner. In light of this, we contribute a novel solution named GCARec (short for graph-augmented co-attention model), which takes into account the joint effect of dynamic sequential patterns and dynamic social influences. GCARec decomposes socio-sequential recommendation workflow into two steps. First, we adopt a light graph embedding module to model long-term user preference. Then, we propose a time-sensitive attention mechanism and a social-aware attention mechanism to capture dynamic patterns at sequential-level and social-level, respectively. Extensive experiments have been conducted on eight real-world datasets from different scenarios, demonstrating the superiority of GCARec against several state-of-the-art methods. The codes and datasets have been released at: https://github.com/wubinzzu/GCARec.
Page(s): 4039 - 4051
Date of Publication: 21 February 2023

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I. Introduction

In the era of information overload, the personalized recommendation has been an essential component in various commercial applications, which could push personalized information for customers and increase great profits for content providers. Learning users’ preferences toward items from historical interaction records is the core of a personalized recommendation. Traditional recommender systems usually model and understand user behaviors in a static manner, ignoring the fact that user interests generally shift over time. In recent years, due to the high practicability, sequential recommendation greatly attracts academia and industry concerns. Many efforts toward this task have been devoted to developing various sequential models, such as Markov chains (MCs) techniques [1], [2], recurrent neural networks (RNNs) [3], [4], convolution neural networks (CNNs) [5], [6], and self-attention mechanisms [7], [8]. Nonetheless, the existing sequential recommenders mainly capture item transition patterns within individual sequence, which is not expressive enough to model short-term user interest especially for cold-start users.

References

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