Asami Okuda
ABSTRACT
Portal sites supporting online purchases provide commercial items and reviews for them. In the case of purchasing cosmetic items, in particular, reviews have important roles in purchasing decisions, allowing purchasers to avoid becoming annoyed with unsuitable items. Thus, we are trying to develop a recommender system for cosmetic items and analyzing reviews. General recommender systems basically identify similar users based on their preferences against common items. However, owing to the huge number of cosmetic items, it is not easy to use preferences for common items because of the data sparsity problem. Therefore, we propose a method for finding similar users based on their preferences against cosmetic item clusters. Moreover, we evaluate and discuss the proposed method for finding similar users based on experimental evaluations.
- @cosme, http://www.cosme.net/Google Scholar
- Y. Matsunami, et al., "Mining Attribute-Specific Ratings from Reviews of Cosmetic Products", Transactions on Engineering Technologies (IMECS2016), pp.101--114, Springer, 2017.Google Scholar
- Amazon.com, http://www.amazon.com/Google Scholar
- Priceprice.com, http://ph.priceprice.com/Google Scholar
- tabelog, https://tabelog.com/Google Scholar
- B. Kang, et al., "Inspection Mechanisms for Community-based Content Discovery in Microblogs" IntRS'15at ACM Recommender Systems 2015.Google Scholar
- The site data of @cosme (May.2017), istyle Inc., http://www.istyle.co.jp/business/uploads/sitedata.pdfGoogle Scholar
- H. Kanayama, et al., "Textual demand analysis: detection of users' wants and needs from opinions.", Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, 2008. Google ScholarDigital Library
- J. O'Donovan, et al., "Extracting and Visualizing Trust Relationships from Online Auction Feedback Comments.," IJCAI'07, 2007. Google ScholarDigital Library
- I. Titov, et al., "A Joint Model of Text and Aspect Ratings for Sentiment Summarization," 46th Meeting of Association for Computational Linguistics (ACL-08), Columbus, USA, pp.308--316, 2008.Google Scholar
Index Terms
- Finding similar users based on their preferences against cosmetic item clusters
Recommendations
Tag recommendation method for a cosmetics review recommender system
iiWAS '17: Proceedings of the 19th International Conference on Information Integration and Web-based Applications & ServicesIn recent years, although cosmetics review-sharing sites have been helpful in decision making by users, it is not easy for users to find reviews that are suitable for them because the quality of skin and taste vary among individuals. We aim to develop a ...
A Genre-Based Item-Item Collaborative Filtering: Facing the Cold-Start Problem
ICSCA '19: Proceedings of the 2019 8th International Conference on Software and Computer ApplicationsRecommender System is a technique which is used to recommend an item or product to a user based on the user's preference'. Collaborative filtering is an approach that is vastly used in recommender systems. Item-item-based collaborative filtering is a ...
Improving Accuracy of Recommender System by Item Clustering
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 ...
Comments