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Award ID contains: 1841138

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  1. ; ; ; (, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval)
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  2. ; ; ; ; (, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval)
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  3. ; ; (, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval)
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  4. ; ; ; ; (, WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining)
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  5. ; ; (, WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining)
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  6. ; ; ; (, WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining)
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  7. ; (, CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management)
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  8. ; ; ; (, CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management)
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  9. ; ; (, The World Wide Web Conference)
    In this paper, we propose a Joint Collaborative Autoencoder framework that learns both user-user and item-item correlations simultaneously, leading to a more robust model and improved top-K recommendation performance. More specifically, we show how to model these user-item correlations and demonstrate the importance of careful normalization to alleviate the influence of feedback heterogeneity. Further, we adopt a pairwise hinge-based objective function to maximize the top-K precision and recall directly for top-K recommenders. Finally, a mini-batch optimization algorithm is proposed to train the proposed model. Extensive experiments on three public datasets show the effectiveness of the proposed framework over state-of-the-art non-neural and neural alternatives. 
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  10. ; (, Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining)
    We propose a new time-dependent predictive model of user-item ratings centered around local coherence -- that is, while both users and items are constantly in flux, within a short-term sequence, the neighborhood of a particular user or item is likely to be coherent. Three unique characteristics of the framework are: (i) it incorporates both implicit and explicit feedbacks by extracting the local coherence hidden in the feedback sequences; (ii) it uses parallel recurrent neural networks to capture the evolution of users and items, resulting in a dual factor recommendation model; and (iii) it combines both coherence-enhanced consistent latent factors and dynamic latent factors to balance short-term changes with long-term trends for improved recommendation. Through experiments on Goodreads and Amazon, we find that the proposed model can outperform state-of-the-art models in predicting users' preferences. 
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