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Connecting users and items with weighted tags for personalized item recommendations

Published: 13 June 2010 Publication History

Abstract

Tags are an important information source in Web 2.0. They can be used to describe users' topic preferences as well as the content of items to make personalized recommendations. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. To eliminate the noise of tags, in this paper we propose to use the multiple relationships among users, items and tags to find the semantic meaning of each tag for each user individually. With the proposed approach, the relevant tags of each item and the tag preferences of each user are determined. In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on real world datasets collected from Amazon.com and citeULike website.

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      cover image ACM Conferences
      HT '10: Proceedings of the 21st ACM conference on Hypertext and hypermedia
      June 2010
      328 pages
      ISBN:9781450300414
      DOI:10.1145/1810617
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 13 June 2010

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      Author Tags

      1. personalization
      2. recommender systems
      3. tags
      4. web 2.0

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      HT '10: 21st ACM Conference on Hypertext and Hypermedia
      June 13 - 16, 2010
      Ontario, Toronto, Canada

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      Cited By

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      • (2023)Hashtag recommendation for enhancing the popularity of social media postsSocial Network Analysis and Mining10.1007/s13278-023-01024-913:1Online publication date: 11-Jan-2023
      • (2023)Multi-component graph collaborative filtering using auxiliary information for TV program recommendationNeural Computing and Applications10.1007/s00521-023-08940-z35:30(22737-22754)Online publication date: 17-Aug-2023
      • (2021)A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous ModelingApplied Sciences10.3390/app1116741811:16(7418)Online publication date: 12-Aug-2021
      • (2021)EnPSO: An AutoML Technique for Generating Ensemble Recommender SystemArabian Journal for Science and Engineering10.1007/s13369-021-05670-z46:9(8677-8695)Online publication date: 23-Apr-2021
      • (2020)Using Social Tag Embedding in a Collaborative Filtering Approach for Recommender Systems2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00075(502-507)Online publication date: Dec-2020
      • (2020)DRprofiling: Deep Reinforcement User Profiling for Recommendations in Heterogenous Information NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.2998695(1-1)Online publication date: 2020
      • (2020)IoT Service Recommendation Scheme Based on Matter DiffusionIEEE Access10.1109/ACCESS.2020.2979777(1-1)Online publication date: 2020
      • (2020)A hybrid neural network approach to combine textual information and rating information for item recommendationKnowledge and Information Systems10.1007/s10115-020-01528-2Online publication date: 23-Nov-2020
      • (2020)Hashtag recommendation for short social media texts using word-embeddings and external knowledgeKnowledge and Information Systems10.1007/s10115-020-01515-7Online publication date: 14-Oct-2020
      • (2019)Regularizing Knowledge Transfer in Recommendation With Tag-Inferred CorrelationIEEE Transactions on Cybernetics10.1109/TCYB.2017.276491849:1(83-96)Online publication date: Jan-2019
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