document: Document recommendation in social tagging services Social tagging services allow users to annotate various online resources with freely chosen keywords ( tags ) . They not only facilitate the users in finding and organizing online resources , but also provide meaningful collaborative semantic data which can potentially be exploited by recommender systems . Traditional studies on recommender systems focused on user rating data , while recently social tagging data is becoming more and more prevalent . How to perform resource recommendation based on tagging data is an emerging research topic . In this paper we consider the problem of document ( e.g. Web pages , research papers ) recommendation using purely tagging data . That is , we only have data containing users , tags , documents and the relationships among them . We propose a novel graph-based representation 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 .
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