Long- and short-term self-attention network for sequential recommendation
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DOI: 10.1016/j.neucom.2020.10.066
Abstract: Abstract With great value in real applications, sequential recommendation aims to recommend users the personalized sequential actions. To achieve better performance, it is essential to consider both long-term preferences and sequential patterns ( i .… read more here.
Keywords: network; term; sequential recommendation; self attention ... See more keywords
ARERec: Attentive Local Interaction Model for Sequential Recommendation
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DOI: 10.1109/access.2022.3160466
Abstract: Previous sequential-recommendation methods have been able to capture patterns of item characteristics that interact with the user. However, they modeled user behavior using a whole interaction sequence, despite possible changes in a user’s behavior over… read more here.
Keywords: user item; sequence; sequential recommendation; interaction ... See more keywords
Enhanced Attention Framework for Multi-Interest Sequential Recommendation
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DOI: 10.1109/access.2022.3185063
Abstract: Sequential recommendation tasks predict items to be interacted at the next moment according to users’ historical behavior sequences. A large number of studies have shown that accuracy is not the only evaluation metric in the… read more here.
Keywords: sequential recommendation; enhanced attention; recommendation; interest sequential ... See more keywords
Fusing User Preferences and Spatiotemporal Information for Sequential Recommendation
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DOI: 10.1109/access.2022.3201339
Abstract: At present, the research on sequence recommendation mainly focuses on using the historical interaction data between users and items to mine their relationship, so as to predict the next interaction between users and items, then… read more here.
Keywords: information; spatiotemporal information; sequential recommendation; recommendation ... See more keywords
Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention.
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DOI: 10.1109/tcyb.2021.3077361
Abstract: Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval… read more here.
Keywords: attention; multivariate hawkes; sequential recommendation; recommendation ... See more keywords
Learning From the Future: Light Cone Modeling for Sequential Recommendation.
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DOI: 10.1109/tcyb.2022.3222259
Abstract: Modeling sequential behaviors is the core of sequential recommendation. As users visit items in chronological order, existing methods typically capture a user's present interests from his/her past-to-present behaviors, i.e. making recommendations with only the unidirectional… read more here.
Keywords: sequential recommendation; modeling sequential; future light; learning future ... See more keywords
Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation
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DOI: 10.1109/tkde.2021.3050407
Abstract: Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms – including both shallow and deep ones – often model… read more here.
Keywords: sequential recommendation; recommendation; dictionary learning; dynamic preferences ... See more keywords
Self-Attention Based Time-Rating-Aware Context Recommender System
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DOI: 10.1155/2022/9288902
Abstract: The sequential recommendation can predict the user's next behavior according to the user's historical interaction sequence. To better capture users' preferences, some sequential recommendation models propose time-aware attention networks to capture users' long-term and short-term… read more here.
Keywords: sequential recommendation; time; self attention; rating ... See more keywords
Attenuated and normalized item-item product network for sequential recommendation
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DOI: 10.7717/peerj-cs.867
Abstract: Sequential recommendation has become a research trending that exploits user’s recent behaviors for recommendation. The user-item interactions contain a sequential dependency that we need to capture to better recommend. Item-item Product (IIP), which models item… read more here.
Keywords: sequential recommendation; item; item item; item product ... See more keywords