Results 21 to 30 of about 208,309 (312)
Context-Similarity Collaborative Filtering Recommendation
IEEE Access, 2020 This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In
Hiep Xuan Huynh +6 more
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Proceedings of the 12th ACM Conference on Recommender Systems, 2018 Automatic solutions which enable the selection of the best algorithms for a new problem are commonly found in the literature. One research area which has recently received considerable efforts is Collaborative Filtering. Existing work includes several approaches using Metalearning, which relate the characteristics of datasets with the performance of ...
Cunha, Tiago +2 more
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Classification and Comparison of the Hybrid Collaborative Filtering Systems [PDF]
International Journal of Research in Industrial Engineering, 2017 Recommender systems have become fundamental applications in overloaded information domains like e-commerce. These systems aim to provide users with suggestions about items that are likely to be of their interest.
F. S. Gohari, M.J. Tarokh
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Content-boosted Matrix Factorization Techniques for Recommender Systems [PDF]
, 2013 Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering.
Nguyen, Jennifer, Zhu, Mu
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Interactive collaborative filtering [PDF]
Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013 In this paper, we study collaborative filtering (CF) in an interactive setting, in which a recommender system continuously recommends items to individual users and receives interactive feedback. Whilst users enjoy sequential recommendations, the recommendation predictions are constantly refined using up-to-date feedback on the recommended items ...
Xiaoxue Zhao, Weinan Zhang, Jun Wang
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Collaborative Filtering Based on Gaussian Mixture Model and Improved Jaccard Similarity
IEEE Access, 2019 The recommender systems play an important role in our lives, since it can quickly help users find what they are interested in. Collaborative filtering has become one of the most widely used algorithms in recommender systems due to its simplicity and ...
Hangyu Yan, Yan Tang
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Causal Collaborative Filtering
Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, 2023 Accepted by the 2023 ACM SIGIR International Conference on Theory of Information ...
Shuyuan Xu +5 more
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QoS Prediction of Web Service Based on Community Detection [PDF]
Jisuanji gongcheng, 2019 Traditional collaborative filtering methods face many problems such as data sparsity,cold start and noise influences when predicting unknown Quality of Service(QoS) values.Therefore a new QoS prediction method based on community discovery is proposed ...
LU Beini,DU Yugen
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Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets
Big Data and Cognitive Computing, 2023 Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values ...
Dionisis Margaris +3 more
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Collaborative Filtering Recommendation Algorithm Based on Representation Learning of Knowledge Graph [PDF]
Jisuanji gongcheng, 2018 To solve the problem that collaborative filtering algorithm only uses the items-users rating matrix and does not consider semantic,a collaborative filtering recommendation algorithm is presented.Using the knowledge map to represent the learning method ...
WU Xiyu,CHEN Qimai,LIU Hai,HE Chaobo
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