Results 21 to 30 of about 46,255 (310)
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 +2 more
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Applied Sciences, 2021 Location-based recommender systems have gained a lot of attention in both commercial domains and research communities where there are various approaches that have shown great potential for further studies.
Aaron Ling Chi Yi, Dae-Ki Kang
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Application of Improved K-Means Algorithm in Collaborative Recommendation System
Journal of Applied Mathematics, 2022 With the explosive growth of information resources in the age of big data, mankind has gradually fallen into a serious “information overload” situation. In the face of massive data, collaborative filtering algorithm plays an important role in information
Hui Jing
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Structured collaborative filtering [PDF]
Proceedings of the 20th ACM international conference on Information and knowledge management, 2011 This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CIKM '11 Proceedings of the 20th ACM international conference on Information and knowledge management, http://dx.doi.org/10.1145/10.1145/2063576.2063940.
Alejandro Bellogín +2 more
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Collaborative Filtering Algorithm Combining Ontology Semantics and User Attribute [PDF]
Jisuanji gongcheng, 2019 When dealing with massive data,the traditional collaborative filtering recommendation algorithm has the data sparsity and the long tail effect of the items,resulting in low recommendation accuracy.Aiming at this problem,combining ontology semantics and ...
WANG Guang, JIANG Li, DONG Shuaihan, LI Feng
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On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets
Information, 2022 The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value.
Dionisis Margaris +2 more
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Perceptron Collaborative Filtering
International Journal for Research in Applied Science and Engineering Technology, 2023 Abstract: While multivariate logistic regression classifiers are a great way of implementing collaborative filtering - a method of making automatic predictions about the interests of a user by collecting preferences or taste information from many other users, we can also achieve similar results using neural networks.
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The intellectual system of movies recommendations based on the collaborative filtering
Journal of Education, Health and Sport, 2022 The investigation deals with designing and developing of intellectual system of movies recommendations based on the collaborative filtering using the Python software environment.
Stepan Sitkar +7 more
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Robust collaborative filtering [PDF]
Proceedings of the 2007 ACM conference on Recommender systems, 2007 The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations.
Bhaskar Mehta +2 more
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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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