Cited By
View all- Feng YWen WHao ZCai R(2024)Time-aware tensor factorization for temporal recommendationApplied Intelligence10.1007/s10489-024-05851-x55:1Online publication date: 27-Nov-2024
Hierarchical Category-Enhanced Prototype Learning for Imbalanced Temporal Recommendation
Pages 6181 - 6189
Abstract
Temporal recommendation systems aim to suggest items to users at the optimal time. However, the significant imbalance of items in the training data poses a major challenge to predictive accuracy. Existing approaches attempt to alleviate this issue by modifying the loss function or utilizing resampling techniques, but such approaches may inadvertently amplify the specificity of certain behaviors.
To address this problem, we propose a novel temporal recommendation algorithm called HCRec. The algorithm adopts a transformer-like architecture, focuses on decoupling variant and invariant information according to hierarchical categories, and then uses invariant prototypes to mitigate the impact of special behaviors. Although category-enhanced recommendation is not a new concept, current approaches have considerable limitations. They usually incorporate the category information into a black-box model as another input sequence, which can easily lead to overfitting. In contrast, HCRec creates a hierarchical relationship tree of items and categories, and establishes mapping rules to classify nodes as variant or invariant. Then, an attention model is used to extract potential variant node features from historical behaviors and enhance them with external knowledge. The invariant node features are fused based on the relationship tree, and finally used for recommendation. On top of this, two time kernel functions are designed to simulate short-term and long-period time effects. Experiments on real-world datasets show that hierarchical category-enhanced prototype learning helps alleviate the imbalance problem, enabling HCRec to outperform all baseline methods.
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Index Terms
- Hierarchical Category-Enhanced Prototype Learning for Imbalanced Temporal Recommendation
Recommendations
Temporal recommendation on graphs via long- and short-term preference fusion
KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data miningAccurately capturing user preferences over time is a great practical challenge in recommender systems. Simple correlation over time is typically not meaningful, since users change their preferences due to different external events. User behavior can ...
Learning hierarchical category influence on both users and items for effective recommendation
SAC '17: Proceedings of the Symposium on Applied ComputingItem category has proven to be useful additional information to address the data sparsity and cold start problems in recommender systems. Although categories have been well studied in which they are independent and structured in a flat form, in many ...
Time-aware tensor factorization for temporal recommendation: Time-aware tensor factorization for temporal recommendation
AbstractIn recent years, temporal recommendation, which recommends items to users with considering temporal information has attracted widespread attention. How to capture and combine the time-varying user behavior distributions and the time-varying user ...
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October 2023
9913 pages
ISBN:9798400701085
DOI:10.1145/3581783
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Published: 27 October 2023
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View all- Feng YWen WHao ZCai R(2024)Time-aware tensor factorization for temporal recommendationApplied Intelligence10.1007/s10489-024-05851-x55:1Online publication date: 27-Nov-2024
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