research-article

Handling data sparsity in collaborative filtering using emotion and semantic based features

Authors:

Yashar Moshfeghi,

Benjamin Piwowarski,

Joemon M. JoseAuthors Info & Claims

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 625 - 634

https://doi.org/10.1145/2009916.2010001

Published: 24 July 2011 Publication History

Abstract

Collaborative filtering (CF) aims to recommend items based on prior user interaction. Despite their success, CF techniques do not handle data sparsity well, especially in the case of the cold start problem where there is no past rating for an item. In this paper, we provide a framework, which is able to tackle such issues by considering item-related emotions and semantic data. In order to predict the rating of an item for a given user, this framework relies on an extension of Latent Dirichlet Allocation, and on gradient boosted trees for the final prediction. We apply this framework to movie recommendation and consider two emotion spaces extracted from the movie plot summary and the reviews, and three semantic spaces: actor, director, and genre. Experiments with the 100K and 1M MovieLens datasets show that including emotion and semantic information significantly improves the accuracy of prediction and improves upon the state-of-the-art CF techniques. We also analyse the importance of each feature space and describe some uncovered latent groups.

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Yao ZJi MXing TFu RLi SYin F(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
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Index Terms

Handling data sparsity in collaborative filtering using emotion and semantic based features
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

Copyright © 2011 ACM.

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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2011

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SIGIR '11

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SIGIR

SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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https://doi.org/10.1117/12.3033025
Hu YTsai CSun Y(2024)A novel hotel recommender system incorporating review sentiment and contextual informationInternational Journal of Data Science and Analytics10.1007/s41060-024-00598-7Online publication date: 23-Jul-2024
https://doi.org/10.1007/s41060-024-00598-7
Yao ZJi MXing TFu RLi SYin F(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
https://doi.org/10.1007/s00521-023-08940-z
Joshi AWong CMarinho de Oliveira DZafari FMourao FRibas SPandey S(2022)Imbalanced Data Sparsity as a Source of Unfair Bias in Collaborative FilteringProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547404(531-533)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3547404
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Fujimoto TMurakami H(2022)A Book Recommendation System Considering Contents and Emotions of User Interests2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAIAAI55812.2022.00039(154-157)Online publication date: Jul-2022
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