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TagiCoFi: tag informed collaborative filtering

Published: 23 October 2009 Publication History

Abstract

Besides the rating information, an increasing number of modern recommender systems also allow the users to add personalized tags to the items. Such tagging information may provide very useful information for item recommendation, because the users' interests in items can be implicitly reflected by the tags that they often use. Although some content-based recommender systems have made preliminary attempts recently to utilize tagging information to improve the recommendation performance, few recommender systems based on collaborative filtering (CF) have employed tagging information to help the item recommendation procedure. In this paper, we propose a novel framework, called tag informed collaborative filtering (TagiCoFi), to seamlessly integrate tagging information into the CF procedure. Experimental results demonstrate that TagiCoFi outperforms its counterpart which discards the tagging information even when it is available, and achieves state-of-the-art performance.

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  • (2024)Enhancing group recommender systems: A fusion of social tagging and collaborative filtering for cohesive recommendationsSystems Research and Behavioral Science10.1002/sres.300041:4(665-680)Online publication date: 14-Feb-2024
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cover image ACM Conferences
RecSys '09: Proceedings of the third ACM conference on Recommender systems
October 2009
442 pages
ISBN:9781605584355
DOI:10.1145/1639714
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: 23 October 2009

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Author Tags

  1. collaborative filtering
  2. recommender systems
  3. tag

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RecSys '09
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RecSys '09: Third ACM Conference on Recommender Systems
October 23 - 25, 2009
New York, New York, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2024)DEKGCI: A double-ended recommendation model for integrating knowledge graph and user–item interaction graphThe Journal of Supercomputing10.1007/s11227-024-06344-xOnline publication date: 8-Jul-2024
  • (2024)HyperSegRec: enhanced hypergraph-based recommendation system with user segmentation and item similarity learningCluster Computing10.1007/s10586-024-04560-x27:8(11727-11745)Online publication date: 3-Jun-2024
  • (2024)Enhancing group recommender systems: A fusion of social tagging and collaborative filtering for cohesive recommendationsSystems Research and Behavioral Science10.1002/sres.300041:4(665-680)Online publication date: 14-Feb-2024
  • (2023)TRAL: A Tag-Aware Recommendation Algorithm Based on Attention LearningApplied Sciences10.3390/app1302081413:2(814)Online publication date: 6-Jan-2023
  • (2023)Enriching Recommendation Models with Logic ConditionsProceedings of the ACM on Management of Data10.1145/36173301:3(1-28)Online publication date: 13-Nov-2023
  • (2023)Enhanced Multi-Task Learning and Knowledge Graph-Based Recommender SystemIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325189735:10(10281-10294)Online publication date: 1-Oct-2023
  • (2023)Recommender Performance for Users with Interest in Several Fields on Collaborative Topic Regression through Vocabulary Completion2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM)10.1109/IMCOM56909.2023.10035578(1-8)Online publication date: 3-Jan-2023
  • (2022)A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for RecommendationInformation10.3390/info1305022913:5(229)Online publication date: 29-Apr-2022
  • (2022)Performance Evaluation on Collaborative Filtering using Latent Topics under Several Sparsity Levels潜在トピックを利用した協調フィルタリングにおけるスパース度合いに応じた性能評価IEEJ Transactions on Electronics, Information and Systems10.1541/ieejeiss.142.1048142:9(1048-1059)Online publication date: 1-Sep-2022
  • (2022)A Survey on Knowledge Graph-Based Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.302870534:8(3549-3568)Online publication date: 1-Aug-2022
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