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Article

Scaling down candidate sets based on the temporal feature of items for improved hybrid recommendations

Authors:

Tiffany Ya Tang,

Keith C. C. ChanAuthors Info & Claims

ITWP'03: Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization

Pages 169 - 186

https://doi.org/10.1007/11577935_9

Published: 11 August 2003 Publication History

Abstract

The intensive information overload incurred by the growing interest in the Internet as a medium to conduct business has stimulated the adoption of recommender systems. However, scalability still remains an obstacle to applying recommender mechanism for large-scale web-based systems where thousands of items and transactions are readily available. To deal with this issue, data mining techniques have been applied to reduce the dimensions of candidate sets. In this chapter in the context of movie recommendations, we study a different kind of technique to scale down candidate sets by considering the temporal feature of items. In particular, we argue that movies' production year can be regarded as a “temporal context” to which the value (thus the rating) of the movie can be attached; and thus might significantly affect target users' future preferences. We call it the temporal effects of the items on the performance of the recommender systems. We perform some experiments on the MovieLens data sets. The results show that the temporal feature of items can not only be exploited to scale down the candidate sets, but also increase the accuracy of the recommender systems.

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Cited By

Song DLi ZJiang MQin LLiao L(2019)A novel temporal and topic-aware recommender modelWorld Wide Web10.1007/s11280-018-0595-922:5(2105-2127)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s11280-018-0595-9
(2018)TCARSFuture Generation Computer Systems10.1016/j.future.2017.04.00378:P1(419-429)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.future.2017.04.003
Ren YLi GZhou W(2015)A survey of recommendation techniques based on offline data processingConcurrency and Computation: Practice & Experience10.1002/cpe.337027:15(3915-3942)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1002/cpe.3370
Show More Cited By

Index Terms

Scaling down candidate sets based on the temporal feature of items for improved hybrid recommendations
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ITWP'03: Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization

August 2003

324 pages

ISBN:3540298460

Editors:
Bamshad Mobasher
Center for Web Intelligence School of Computing, DePaul University, Chicago, Illinois
,
Sarabjot Singh Anand
Department of Computer Science, University of Warwick, Coventry, Illinois, United Kingdom

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 August 2003

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Cited By

Song DLi ZJiang MQin LLiao L(2019)A novel temporal and topic-aware recommender modelWorld Wide Web10.1007/s11280-018-0595-922:5(2105-2127)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s11280-018-0595-9
(2018)TCARSFuture Generation Computer Systems10.1016/j.future.2017.04.00378:P1(419-429)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.future.2017.04.003
Ren YLi GZhou W(2015)A survey of recommendation techniques based on offline data processingConcurrency and Computation: Practice & Experience10.1002/cpe.337027:15(3915-3942)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1002/cpe.3370
Rana CJain S(2012)A recommendation model for handling dynamics in user profileProceedings of the 8th international conference on Distributed Computing and Internet Technology10.1007/978-3-642-28073-3_20(231-241)Online publication date: 2-Feb-2012
https://dl.acm.org/doi/10.1007/978-3-642-28073-3_20
Winoto PTang T(2010)The role of user mood in movie recommendationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.02.11737:8(6086-6092)Online publication date: 1-Aug-2010
https://dl.acm.org/doi/10.1016/j.eswa.2010.02.117
Lee TPark YPark Y(2009)An empirical study on effectiveness of temporal information as implicit ratingsExpert Systems with Applications: An International Journal10.1016/j.eswa.2007.11.04736:2(1315-1321)Online publication date: 1-Mar-2009
https://dl.acm.org/doi/10.1016/j.eswa.2007.11.047
Lee TPark YPark Y(2009)A Similarity Measure for Collaborative Filtering with Implicit FeedbackProceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence10.1007/978-3-540-74205-0_43(385-397)Online publication date: 17-Nov-2009
https://dl.acm.org/doi/10.1007/978-3-540-74205-0_43
Anand SMobasher B(2003)Intelligent techniques for web personalizationProceedings of the 2003 international conference on Intelligent Techniques for Web Personalization10.1007/11577935_1(1-36)Online publication date: 11-Aug-2003
https://dl.acm.org/doi/10.1007/11577935_1

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