Collaborative and content-based filtering for item recommendation on social bookmarking websites

Social bookmarking websites allow users to store, organize, and search bookmarks of web pages. Users of these services can annotate their bookmarks by using informal tags and other metadata, such as titles, descriptions, etc. In this paper, we focus on the task of item recommendation for social bookmarking websites, i.e. predicting which unseen bookmarks a user might like based on his or her profile. We examine how we can incorporate the tags and other metadata into a nearest-neighbor collaborative filtering (CF) algorithm, by replacing the traditional usage-based similarity metrics by tag overlap, and by fusing tag-based similarity with usage-based similarity. In addition, we perform experiments with content-based filtering by using the metadata content to recommend interesting items. We generate recommendations directly based on Kullback- Leibler divergence of the metadata language models, and we explore the use of this metadata in calculating user and item similarities. We perform our experiments on three data sets from two different domains: Delicious, CiteULike and BibSonomy.