Document recommendation in social tagging services
Ziyu Guan, Can Wang, Jiajun Bu, Chun Chen, Kun Yang, Deng Cai, Xiaofei He · 2010
Social tagging services allow users to annotate various on-line resources with freely chosen keywords (tags). They not only facilitate the users in finding and organizing online re-sources, but also provide meaningful collaborative semantic data which can potentially be exploited by recommender systems. Traditional studies on recommender systems fo-cused on user rating data, while recently social tagging data is becoming more and more prevalent. How to perform re-source recommendation based on tagging data is an emerg-ing research topic. In this paper we consider the problem of document (e.g. Web pages, research papers) recommen-dation using purely tagging data. That is, we only have data containing users, tags, documents and the relation-ships among them. We propose a novel graph-based rep-resentation learning algorithm for this purpose. The users, tags and documents are represented in the same semantic space in which two related objects are close to each other. For a given user, we recommend those documents that are sufficiently close to him/her. Experimental results on two data sets crawled from Del.icio.us and CiteULike show that our algorithm can generate promising recommendations and outperforms traditional recommendation algorithms.