research-article

Refining User and Item Profiles based on Multidimensional Data for Top-N Item Recommendation

Authors:

Shlomo GevaAuthors Info & Claims

iiWAS '14: Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services

Pages 310 - 319

https://doi.org/10.1145/2684200.2684284

Published: 04 December 2014 Publication History

Abstract

In recommender systems based on multidimensional data, additional metadata provides algorithms with more information for better understanding the interaction between users and items. However, most of the profiling approaches in neighbourhood-based recommendation approaches for multidimensional data merely split or project the dimensional data and lack the consideration of latent interaction between the dimensions of the data. In this paper, we propose a novel user/item profiling approach for Collaborative Filtering (CF) item recommendation on multidimensional data. We further present incremental profiling method for updating the profiles. For item recommendation, we seek to delve into different types of relations in data to understand the interaction between users and items more fully, and propose three multidimensional CF recommendation approaches for top-N item recommendations based on the proposed user/item profiles. The proposed multidimensional CF approaches are capable of incorporating not only localized relations of user-user and/or item-item neighbourhoods but also latent interaction between all dimensions of the data. Experimental results show significant improvements in terms of recommendation accuracy.

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Cited By

Choi YRhee IHoang PChoi H(2023)Simple desired manipulability ellipsoid with velocity and force for control of redundant manipulatorJournal of Mechanical Science and Technology10.1007/s12206-023-0339-337:4(2033-2041)Online publication date: 1-Apr-2023
https://doi.org/10.1007/s12206-023-0339-3
Tang XXu YGeva S(2018)Factorization-based primary dimension modelling for multidimensional data in recommender systemsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-018-0816-7Online publication date: 20-Apr-2018
https://doi.org/10.1007/s13042-018-0816-7
Tang XXu YGeva S(2015)Integrating Tensor Factorization with Neighborhood for Item Recommendation in Multidimensional ContextProceedings of the 2015 IEEE / WIC / ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) - Volume 0110.1109/WI-IAT.2015.117(377-384)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.1109/WI-IAT.2015.117

Index Terms

Refining User and Item Profiles based on Multidimensional Data for Top-N Item Recommendation
1. Information systems
  1. Information retrieval

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iiWAS '14: Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services

December 2014

587 pages

ISBN:9781450330015

DOI:10.1145/2684200

Copyright © 2014 ACM.

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@WAS: International Organization of Information Integration and Web-based Applications and Services
Johannes Kepler Univ Linz: Johannes Kepler Universität Linz

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Association for Computing Machinery

New York, NY, United States

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Published: 04 December 2014

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iiWAS '14

iiWAS '14: The 16th International Conference on Information Integration and Web-based Applications & Services

December 4 - 6, 2014

Hanoi, Viet Nam

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Cited By

Choi YRhee IHoang PChoi H(2023)Simple desired manipulability ellipsoid with velocity and force for control of redundant manipulatorJournal of Mechanical Science and Technology10.1007/s12206-023-0339-337:4(2033-2041)Online publication date: 1-Apr-2023
https://doi.org/10.1007/s12206-023-0339-3
Tang XXu YGeva S(2018)Factorization-based primary dimension modelling for multidimensional data in recommender systemsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-018-0816-7Online publication date: 20-Apr-2018
https://doi.org/10.1007/s13042-018-0816-7
Tang XXu YGeva S(2015)Integrating Tensor Factorization with Neighborhood for Item Recommendation in Multidimensional ContextProceedings of the 2015 IEEE / WIC / ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) - Volume 0110.1109/WI-IAT.2015.117(377-384)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.1109/WI-IAT.2015.117

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