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Intent-Guided Bilateral Long and Short-Term Information Mining With Contrastive Learning for Sequential Recommendation | IEEE Journals & Magazine | IEEE Xplore

Intent-Guided Bilateral Long and Short-Term Information Mining With Contrastive Learning for Sequential Recommendation


Abstract:

The current sequential recommendation systems mainly focus on mining information related to users to make personalized recommendations. However, there are two subjects in...Show More

Abstract:

The current sequential recommendation systems mainly focus on mining information related to users to make personalized recommendations. However, there are two subjects in the user historical interaction sequence: users and items. We believe that mining sequence information only from the users’ perspective is limited, ignoring effective information from the perspective of items, which is not conducive to alleviating the data sparsity problem. To explore potential links between items and use them for recommendation, we propose Intent-guided Bilateral Long and Short-Term Information Mining with Contrastive Learning for Sequential Recommendation (IBLSRec), which interpretively integrates three kinds of information mined from the sequence: user preferences, user intentions, and potential relationships between items. Specifically, we model the potential relationships between interactive items from a long-term and short-term perspective. The short-term relationship between items is regarded as noise; the long-term relationship between items is regarded as a stable common relationship and integrated with the user's personalized preferences. In addition, user intent is used to guide the modeling of user preferences to refine the representation of user preferences further. A large number of experiments on four real data sets validate the superiority of our model.
Published in: IEEE Transactions on Services Computing ( Volume: 18, Issue: 1, Jan.-Feb. 2025)
Page(s): 212 - 225
Date of Publication: 03 January 2025

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I. Introduction

Recommendation systems [1], [2], [3] are a kind of expert systems that assist users in filtering information and finding items matching their needs and personal preferences. With this role, recommendation systems can effectively alleviate the information overload issue for the end user [4], [5]. In the last decades, recommendation systems have been practiced in a variety of domains such as electronic commerce [6] and social networks [7].

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References

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