Results 11 to 20 of about 31,699 (263)
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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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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Sinkhorn Collaborative Filtering
Proceedings of the Web Conference 2021, 2021 Recommender systems play a vital role in modern web services. In a typical recommender system, we are given a set of observed user-item interaction records and seek to uncover the hidden behavioral patterns of users from these historical interactions. By exploiting these hidden patterns, we aim to discover users’ personalized tastes and recommend them ...
Xiucheng Li +3 more
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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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Deep social collaborative filtering [PDF]
Proceedings of the 13th ACM Conference on Recommender Systems, 2019 Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative filtering techniques. In addition to the user-item interactions, social networks can also provide useful information to understand ...
Fan, Wenqi +5 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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An Improved Dynamic Collaborative Filtering Algorithm Based on LDA
IEEE Access, 2021 Currently, available collaborative filtering (CF) algorithms often utilize user behavior data to generate recommendations. The similarity calculation between users is mostly based on the scores, without considering the explicit attributes of the users ...
Meng Di-Fei +3 more
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Delay-tolerant collaborative filtering [PDF]
Proceedings of the 7th ACM international symposium on Mobility management and wireless access, 2009 Recommender systems using collaborative filtering are a well-established technique to overcome information overload in today's digital society. Currently, predominant collaborative filtering systems mostly depend on huge centralized databases to store user preferences and furthermore are only available when connected to Internet.
Gratz, Patrick, Leclerc, Tom
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Online Collaborative-Filtering on Graphs [PDF]
SSRN Electronic Journal, 2013 Existing approaches to designing recommendation systems with user feedback focus on settings where the number of items is small and/or admit some underlying structure. It is unclear, however, if these approaches extend to applications like social network news feeds and content-curation platforms, which have large and unstructured content pools and ...
Banerjee, Siddhartha +2 more
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