A Context-Aware User-Item Representation Learning for Item Recommendation
نویسندگان
چکیده
منابع مشابه
A Context-Aware User-Item Representation Learning for Item Recommendation
Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That is, a single static feature vector is derived to encode her preference without considering the particular characteristics of each candidate ite...
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class, 107accuracy, 40AdaBoost, 77adaptivity, 41age, 81ALS, see alternating least squaresalternating least squares, 36, 86Apache Mahout, 86area under the ROC curve, 41, 61, 82aspect model, 58association rules, 87attribute-based kNN, 81attribute-to-factor mapping, 45 59AUC, see area under the ROC curve bagging, 77, 87Bayesian Context-Aware ...
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Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not at all. However, those ignored products are exactly the products that businesses need to find customers for and their recommendations would be more beneficial. In this paper, we examine an item weighting approach to im...
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Recommender systems have always faced the problem of sparse data. In the current era, however, with its demand for highly personalized, real-time, context-aware recommendation, the sparse data problem only threatens to grow worse. Crowdsourcing, specifically, outsourcing micro-requests for information to the crowd, opens new possibilities to fight the sparse data challenge. In this paper, we la...
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ژورنال
عنوان ژورنال: ACM Transactions on Information Systems
سال: 2019
ISSN: 1046-8188,1558-2868
DOI: 10.1145/3298988