Item recommendation in collaborative tagging systems via heuristic data fusion.

Collaborative tagging systems have been popular on the Web. However, information overload results in the increasing need for recommender services from users, and thus item recommendation has been one of the key issues in such systems. In this paper, we examine if data fusion can be helpful for improving effectiveness of item recommendation in these systems. For this, we first summarize the state-of-the-art recommendation methods which are classified into several categories according to their algorithmic principles. Then, we experiment with about 40 recommending components against the datasets from three social tagging systems-Delicious, Lastfm and CiteULike. Based on these, several heuristic data fusion models including rank-based and score-based are used to combine selected components. We also put forward a hybrid linear combination (HLC) model for fusing item recommendation. We use four kinds of evaluation metrics, which respectively consider accuracy, inner-diversity, inter-diversity and novelty, to systematically assess quality of recommendations obtained by various components or fusion models. Depending on experimental results, combining evidence from separate components can lead to performance improvement in the accuracy of recommendations, with a little or without loss of recommendation diversity and novelty, if separate components can suggest similar sets of relevant items but recommend different sets of non-relevant items. Particularly, fusing recommendation sets formed from different combinations of profile representations and similarity functions in user-based and item-based collaborative filtering can significantly improve recommendation accuracy. In addition, some other useful findings are also drawn: (i) Using the tag to represent users profiles or items profiles maybe not as good as profiling users with the item or profiling items with the user, however, exploiting tags in the topic models and random walks can notably improve the accuracy, diversity and novelty of recommendations; (ii) Generally, user-based collaborative filtering, item-based collaborative filtering and random walks methods are robust for the task of item recommendation in social tagging systems, thus can be chosen as the basic components of data fusion process; and (iii) The proposed method (HLC) is more flexible and robust than traditional data fusion models.