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Adapting vector space model to ranking-based collaborative filtering

Published: 29 October 2012 Publication History

Abstract

Collaborative filtering (CF) is an effective technique addressing the information overload problem. Recently ranking-based CF methods have shown advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we seek accuracy improvement of ranking-based CF through adaptation of the vector space model, where we consider each user as a document and her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Then we use cosine similarity to select a neighborhood of users for the target user to make recommendations. Experiments on benchmarks in comparison with the state-of-the-art methods demonstrate the promise of our approach.

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Cited By

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  • (2023)DGRec: Graph Neural Network for Recommendation with Diversified Embedding GenerationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570472(661-669)Online publication date: 27-Feb-2023
  • (2019)A Multi-criteria Collaborative Filtering Recommender System Using Learning-to-Rank and Rank AggregationArabian Journal for Science and Engineering10.1007/s13369-019-04180-3Online publication date: 27-Sep-2019
  • (2018)Collaborative Recommender Systems Based on User-Generated Reviews: A Concise Survey2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT)10.1109/ISAECT.2018.8618822(1-6)Online publication date: Nov-2018
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      cover image ACM Conferences
      CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management
      October 2012
      2840 pages
      ISBN:9781450311564
      DOI:10.1145/2396761
      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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      Published: 29 October 2012

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      Author Tags

      1. collaborative filtering
      2. ranking-based collaborative filtering
      3. recommender systems
      4. term weighting
      5. vector space model

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      View all
      • (2023)DGRec: Graph Neural Network for Recommendation with Diversified Embedding GenerationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570472(661-669)Online publication date: 27-Feb-2023
      • (2019)A Multi-criteria Collaborative Filtering Recommender System Using Learning-to-Rank and Rank AggregationArabian Journal for Science and Engineering10.1007/s13369-019-04180-3Online publication date: 27-Sep-2019
      • (2018)Collaborative Recommender Systems Based on User-Generated Reviews: A Concise Survey2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT)10.1109/ISAECT.2018.8618822(1-6)Online publication date: Nov-2018
      • (2018)A language model-based framework for multi-publisher content-based recommender systemsInformation Retrieval Journal10.1007/s10791-018-9327-021:5(369-409)Online publication date: 6-Feb-2018
      • (2018)ColdRoute: effective routing of cold questions in stack exchange sitesData Mining and Knowledge Discovery10.1007/s10618-018-0577-732:5(1339-1367)Online publication date: 29-Jun-2018
      • (2017)Breaking Cycles In Noisy HierarchiesProceedings of the 2017 ACM on Web Science Conference10.1145/3091478.3091495(151-160)Online publication date: 25-Jun-2017
      • (2017)Learning to Recommend Accurate and Diverse ItemsProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052585(183-192)Online publication date: 3-Apr-2017
      • (2016)Ranking-Oriented Collaborative FilteringACM Transactions on Information Systems10.1145/296040835:2(1-28)Online publication date: 21-Sep-2016
      • (2016)Combination of improved cosine similarity and patent attribution probability method to judge the attribution of related patents of hydrolysis substrate fabrication processAdvanced Engineering Informatics10.1016/j.aei.2015.11.00330:1(26-38)Online publication date: 1-Jan-2016
      • (2015)Listwise Collaborative FilteringProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767693(343-352)Online publication date: 9-Aug-2015
      • Show More Cited By

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