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Learning to rank for hybrid recommendation

Published: 29 October 2012 Publication History

Abstract

Most existing recommender systems can be classified into two categories: collaborative filtering and content-based filtering. Hybrid recommender systems combine the advantages of the two for improved recommendation performance. Traditional recommender systems are rating-based. However, predicting ratings is an intermediate step towards their ultimate goal of generating rankings or recommendation lists. Learning to rank is an established means of predicting rankings and has recently demonstrated high promise in improving quality of recommendations. In this paper, we propose LRHR, the first attempt that adapts learning to rank to hybrid recommender systems. LRHR first defines novel representations for both users and items so that they can be content-comparable. Then, LRHR identifies a set of novel meta-level features for learning purposes. Finally, LRHR adopts RankSVM, a pairwise learning to rank algorithm, to generate recommendation lists of items for users. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms demonstrate the performance gain 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
  • (2022)Recommendation Ranking Method Combining Graph Convolutional Network and Factorization Machine2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)10.1109/ICWOC55996.2022.9809884(55-62)Online publication date: 10-Jun-2022
  • (2020)Inferring Implicit Rules by Learning Explicit and Hidden Item DependencyIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2017.276854750:3(935-946)Online publication date: Mar-2020
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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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Publication History

Published: 29 October 2012

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Author Tags

  1. collaborative filtering
  2. features
  3. learning to rank
  4. recommender systems

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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
  • (2022)Recommendation Ranking Method Combining Graph Convolutional Network and Factorization Machine2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)10.1109/ICWOC55996.2022.9809884(55-62)Online publication date: 10-Jun-2022
  • (2020)Inferring Implicit Rules by Learning Explicit and Hidden Item DependencyIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2017.276854750:3(935-946)Online publication date: Mar-2020
  • (2019)Learning to Refine Expansion Terms for Biomedical Information Retrieval Using Semantic ResourcesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2018.280130316:3(954-966)Online publication date: 1-May-2019
  • (2018)Improve Biomedical Information Retrieval Using Modified Learning to Rank MethodsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.257833715:6(1797-1809)Online publication date: 1-Nov-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)A novel learning-to-rank based hybrid method for book recommendationProceedings of the International Conference on Web Intelligence10.1145/3106426.3106547(837-842)Online publication date: 23-Aug-2017
  • (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)Probabilistic Models for Ad Viewability Prediction on the WebIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.270568829:9(2012-2025)Online publication date: 1-Sep-2017
  • (2016)SPrankACM Transactions on Intelligent Systems and Technology10.1145/28990058:1(1-34)Online publication date: 20-Sep-2016
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