Global Consistency Augmented Multi-preference Session-Based Recommendation Model
Session-based recommendation aims to predict the next item which a user is likely to interact with based on an anonymous session.However,existing session-based recommendation methods based on graph neural networks underutilize the global information.To address this issue,a global consistency augmented multi-preference session-based recommendation model(GCAM)is proposed.Firstly,a consistent global graph is constructed through the shortest path routing algorithm.The consistency of global information is ensured by capturing reliable item relationships and filtering out unreliable item relationships.Secondly,a multi-preference label smoothing strategy is applied to mine collaborative information from historical sessions to soften labels,and thereby the label can fit the true user preferences.Extensive experiments on three different datasets demonstrate the superiority of GCAM.
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