Making Item Predictions through Tag Recommendations
- DOI
- 10.2991/icebi.2010.5How to use a DOI?
- Keywords
- social tagging, tag-based recommendation, tag recommendation, item recommendation, collaborative filtering
- Abstract
As opposed to the search engine, social tagging can be considered an alternative technique tapping into the wisdom of the crowd for organizing and discovering information on the Web. Effective tagbased recommendation of information items is a critical aspect of this social information discovery mechanism. While most existing work in the tagging domain makes item recommendations directly after constructing or learning the user profiles, items are not particularly recommendable indeed due to the limiting descriptive ability of the binary values they were assigned on interacting with users. In response to this problem, we propose to recommend the more recommendable tags, which have numerical interactions with users, to refine users¡¯ tag preference first, and then deliver quality item recommendations based on the global relationship between tags and items. Experiments on three realworld social tagging datasets demonstrate the effectiveness of our approach.
- Copyright
- © 2010, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jing Peng AU - Daniel Zeng PY - 2010/12 DA - 2010/12 TI - Making Item Predictions through Tag Recommendations BT - Proceedings of the 1st International Conference on E-Business Intelligence (ICEBI 2010) PB - Atlantis Press SP - 30 EP - 37 SN - 1951-6851 UR - https://doi.org/10.2991/icebi.2010.5 DO - 10.2991/icebi.2010.5 ID - Peng2010/12 ER -