Connecting users and items with weighted tags for personalized item recommendations

Liang, Huizhi, Xu, Yue, Li, Yuefeng, & Nayak, Richi (2010) Connecting users and items with weighted tags for personalized item recommendations. In Proceedings of 21st ACM Conference on HyperText and HyperMedia, ACM, Toronto.

View at publisher


Social tags are an important information source in Web 2.0. They can be used to describe users’ topic preferences as well as the content of items to make personalized recommendations. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. To eliminate the noise of tags, in this paper we propose to use the multiple relationships among users, items and tags to find the semantic meaning of each tag for each user individually. With the proposed approach, the relevant tags of each item and the tag preferences of each user are determined. In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on real world datasets collected from and citeULike website.

Impact and interest:

31 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

377 since deposited on 06 Jun 2011
23 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 41887
Item Type: Conference Paper
Refereed: Yes
Keywords: Recommender systems, Tags, Personalization, Web 2.0
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2010 ACM
Deposited On: 06 Jun 2011 04:20
Last Modified: 11 Jun 2011 21:33

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page