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Listwise Collaborative Filtering

Published: 09 August 2015 Publication History

Abstract

Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do). In this paper, we propose a new ranking-oriented CF algorithm, called ListCF. Following the memory-based CF framework, ListCF directly predicts a total order of items for each user based on similar users' probability distributions over permutations of the items, and thus differs from previous ranking-oriented memory-based CF algorithms that focus on predicting the pairwise preferences between items. One important advantage of ListCF lies in its ability of reducing the computational complexity of the training and prediction procedures while achieving the same or better ranking performances as compared to previous ranking-oriented memory-based CF algorithms. Extensive experiments on three benchmark datasets against several state-of-the-art baselines demonstrate the effectiveness of our proposal.

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Cited By

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  • (2025)A Rank-Based Approach to Recommender System's Top-K Queries with Uncertain ScoresProceedings of the ACM on Management of Data10.1145/37096553:1(1-26)Online publication date: 11-Feb-2025
  • (2024)Learning-to-rank debias with popularity-weighted negative sampling and popularity regularizationNeurocomputing10.1016/j.neucom.2024.127681587(127681)Online publication date: Jun-2024
  • (2023)SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender SystemACM Transactions on Information Systems10.1145/362619442:2(1-32)Online publication date: 3-Oct-2023
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cover image ACM Conferences
SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
August 2015
1198 pages
ISBN:9781450336215
DOI:10.1145/2766462
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: 09 August 2015

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Author Tags

  1. collaborative filtering
  2. ranking-oriented collaborative filtering
  3. recommender systems

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  • Research-article

Funding Sources

  • Academy of Finland
  • Humanity and Social Science Foundation of Ministry of Education of China
  • Natural Science foundation of Shandong province
  • Natural Science Foundation of China
  • Microsoft research fund
  • Doctoral Fund of Ministry of Education of China

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SIGIR '15
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SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2025)A Rank-Based Approach to Recommender System's Top-K Queries with Uncertain ScoresProceedings of the ACM on Management of Data10.1145/37096553:1(1-26)Online publication date: 11-Feb-2025
  • (2024)Learning-to-rank debias with popularity-weighted negative sampling and popularity regularizationNeurocomputing10.1016/j.neucom.2024.127681587(127681)Online publication date: Jun-2024
  • (2023)SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender SystemACM Transactions on Information Systems10.1145/362619442:2(1-32)Online publication date: 3-Oct-2023
  • (2023)An overview of consensus models for group decision-making and group recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09380-z34:3(489-547)Online publication date: 22-Sep-2023
  • (2022)Learning Hybrid Behavior Patterns for Multimedia RecommendationProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548119(376-384)Online publication date: 10-Oct-2022
  • (2022)SQL-Rank++: A Novel Listwise Approach for Collaborative Ranking with Implicit Feedback2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9891935(1-8)Online publication date: 18-Jul-2022
  • (2021)Density-Ratio Based Personalised Ranking from Implicit FeedbackProceedings of the Web Conference 202110.1145/3442381.3450027(3221-3233)Online publication date: 19-Apr-2021
  • (2021)Differentiable Ranking Metric Using Relaxed Sorting for Top-K RecommendationIEEE Access10.1109/ACCESS.2021.31053899(114649-114658)Online publication date: 2021
  • (2020)Transfer to Rank for Top-N RecommendationIEEE Transactions on Big Data10.1109/TBDATA.2019.28924786:4(770-779)Online publication date: 1-Dec-2020
  • (2019)Listwise Collaborative Filtering with High-Rating-Based Similarity and Simple Missing Value EstimationJournal of Japan Society for Fuzzy Theory and Intelligent Informatics10.3156/jsoft.31.1_50131:1(501-507)Online publication date: 15-Feb-2019
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