IEICE TRANSACTIONS on Information
Improving Accuracy of Recommender System by Item Clustering
Summary :
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches trying to apply agent technology to RS. Collaborative Filtering, one of the most widely used approach to predict user's ratings in Recommender System, predicts a user's rating towards an item by aggregating ratings given by users who have similar preference to that user. In existing approaches, user similarity is often computed on the whole set of items. However, because the number of items is often very large and so is the diversity among items, users who have similar preference in one category may have totally different judgement on items of another kind. In order to deal with this problem, we propose a method to cluster items, so that inside a cluster, similarity between users does not change significantly from item to item. After the item clustering phase, when predicting rating of a user towards an item, we only aggregate ratings of users who have similarity preference to that user inside the cluster of that item. Experiments evaluating our approach are carried out on the real dataset taken from MovieLens, a movies recommendation web site. Experiment results suggest that our approach can improve prediction accuracy compared to existing approaches.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.9 pp.1363-1373
- Publication Date
- 2007/09/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.9.1363
- Type of Manuscript
- Special Section PAPER (Special Section on Software Agent and Its Applications)
- Category
Authors
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KhanhQuan TRUONG, Fuyuki ISHIKAWA, Shinichi HONIDEN, "Improving Accuracy of Recommender System by Item Clustering" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 9, pp. 1363-1373, September 2007, doi: 10.1093/ietisy/e90-d.9.1363.
Abstract: Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches trying to apply agent technology to RS. Collaborative Filtering, one of the most widely used approach to predict user's ratings in Recommender System, predicts a user's rating towards an item by aggregating ratings given by users who have similar preference to that user. In existing approaches, user similarity is often computed on the whole set of items. However, because the number of items is often very large and so is the diversity among items, users who have similar preference in one category may have totally different judgement on items of another kind. In order to deal with this problem, we propose a method to cluster items, so that inside a cluster, similarity between users does not change significantly from item to item. After the item clustering phase, when predicting rating of a user towards an item, we only aggregate ratings of users who have similarity preference to that user inside the cluster of that item. Experiments evaluating our approach are carried out on the real dataset taken from MovieLens, a movies recommendation web site. Experiment results suggest that our approach can improve prediction accuracy compared to existing approaches.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.9.1363/_p
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@ARTICLE{e90-d_9_1363,
author={KhanhQuan TRUONG, Fuyuki ISHIKAWA, Shinichi HONIDEN, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Accuracy of Recommender System by Item Clustering},
year={2007},
volume={E90-D},
number={9},
pages={1363-1373},
abstract={Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches trying to apply agent technology to RS. Collaborative Filtering, one of the most widely used approach to predict user's ratings in Recommender System, predicts a user's rating towards an item by aggregating ratings given by users who have similar preference to that user. In existing approaches, user similarity is often computed on the whole set of items. However, because the number of items is often very large and so is the diversity among items, users who have similar preference in one category may have totally different judgement on items of another kind. In order to deal with this problem, we propose a method to cluster items, so that inside a cluster, similarity between users does not change significantly from item to item. After the item clustering phase, when predicting rating of a user towards an item, we only aggregate ratings of users who have similarity preference to that user inside the cluster of that item. Experiments evaluating our approach are carried out on the real dataset taken from MovieLens, a movies recommendation web site. Experiment results suggest that our approach can improve prediction accuracy compared to existing approaches.},
keywords={},
doi={10.1093/ietisy/e90-d.9.1363},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Improving Accuracy of Recommender System by Item Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 1363
EP - 1373
AU - KhanhQuan TRUONG
AU - Fuyuki ISHIKAWA
AU - Shinichi HONIDEN
PY - 2007
DO - 10.1093/ietisy/e90-d.9.1363
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2007
AB - Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches trying to apply agent technology to RS. Collaborative Filtering, one of the most widely used approach to predict user's ratings in Recommender System, predicts a user's rating towards an item by aggregating ratings given by users who have similar preference to that user. In existing approaches, user similarity is often computed on the whole set of items. However, because the number of items is often very large and so is the diversity among items, users who have similar preference in one category may have totally different judgement on items of another kind. In order to deal with this problem, we propose a method to cluster items, so that inside a cluster, similarity between users does not change significantly from item to item. After the item clustering phase, when predicting rating of a user towards an item, we only aggregate ratings of users who have similarity preference to that user inside the cluster of that item. Experiments evaluating our approach are carried out on the real dataset taken from MovieLens, a movies recommendation web site. Experiment results suggest that our approach can improve prediction accuracy compared to existing approaches.
ER -
English
