research-article

Identifying representative ratings for a new item in recommendation system

Authors:

Sang-Min Choi,

Yo-Sub HanAuthors Info & Claims

ICUIMC '13: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication

Article No.: 64, Pages 1 - 6

https://doi.org/10.1145/2448556.2448620

Published: 17 January 2013 Publication History

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Abstract

With the development of the Internet, the users share information using Web applications. Because of this reason, there is lots of information on the Web. The information includes not only high quality information, but also useless one. With the phenomena, the recommendation system appears on the Web. Existing information recommendation systems on the Web have known problems. One famous problem is cold-start. We tackle the cold-start problem for a new item in recommendation system. To alleviate cold-start for a new item, we use method for identifying representative reviewers in raters group and recommendation algorithm based on category correlations. The representative reviewers mean the users who represent their raters group. Namely, the ratings of the reviewers can represent the average ratings of other users. If there are the ratings for new items rated by the representative reviewers, then we can consider the ratings rated by many other users. We predict the ratings of these reviewers for a new item. To predict ratings, we use the recommendation algorithm based on the category correlations. This algorithm can draw the prediction results without ratings since the algorithm uses category information. We propose the prediction results of the representative reviewers as the representative ratings for a new item. We propose the algorithm to alleviate cold-start for a new item and show the reliability of our approach through tests.

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Cited By

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Kim YChoi SLee DSeo YLee S(2023)A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart ContractsMathematics10.3390/math1113296211:13(2962)Online publication date: 3-Jul-2023
https://doi.org/10.3390/math11132962
Choi SLee DJang KPark CLee S(2023)Improving Data Sparsity in Recommender Systems Using Matrix Regeneration with Item FeaturesMathematics10.3390/math1102029211:2(292)Online publication date: 5-Jan-2023
https://doi.org/10.3390/math11020292
Choi SJang KLee TKhreishah ANoh W(2020)Alleviating Item-Side Cold-Start Problems in Recommender Systems Using Weak SupervisionIEEE Access10.1109/ACCESS.2020.30194648(167747-167756)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3019464
Show More Cited By

Index Terms

Identifying representative ratings for a new item in recommendation system
1. Information systems
  1. Information systems applications

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Published In

ICUIMC '13: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication

January 2013

772 pages

ISBN:9781450319584

DOI:10.1145/2448556

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 January 2013

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National Research Foundation of Korea

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ICUIMC '13

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SIGAPP
SKKU

ICUIMC '13: The 7th International Conference on Ubiquitous Information Management and Communication

January 17 - 19, 2013

Kota Kinabalu, Malaysia

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Overall Acceptance Rate 251 of 941 submissions, 27%

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Cited By

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Kim YChoi SLee DSeo YLee S(2023)A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart ContractsMathematics10.3390/math1113296211:13(2962)Online publication date: 3-Jul-2023
https://doi.org/10.3390/math11132962
Choi SLee DJang KPark CLee S(2023)Improving Data Sparsity in Recommender Systems Using Matrix Regeneration with Item FeaturesMathematics10.3390/math1102029211:2(292)Online publication date: 5-Jan-2023
https://doi.org/10.3390/math11020292
Choi SJang KLee TKhreishah ANoh W(2020)Alleviating Item-Side Cold-Start Problems in Recommender Systems Using Weak SupervisionIEEE Access10.1109/ACCESS.2020.30194648(167747-167756)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3019464
de C.A. Ziesemer AMüller LSilveira M(2014)Just Rate It! Gamification as Part of RecommendationHuman-Computer Interaction. Applications and Services10.1007/978-3-319-07227-2_75(786-796)Online publication date: 2014
https://doi.org/10.1007/978-3-319-07227-2_75

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A Genre-Based Item-Item Collaborative Filtering: Facing the Cold-Start Problem

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