Harnessing the power of Social Bookmarking for improving tag-based Recommendations
This work addresses the need for more effective personalized recommendations in social bookmarking systems, but it is incremental as it builds on existing tag-based methods.
The paper tackles the problem of improving tag-based product recommendations by proposing an algorithm that clusters user-provided tags based on semantic similarity and incorporates user annotation competency and similarity metrics. The result is that the approach outperforms baseline and state-of-the-art methods, predicting user liking more accurately on real-world data from citeUlike.
Social bookmarking and tagging has emerged a new era in user collaboration. Collaborative Tagging allows users to annotate content of their liking, which via the appropriate algorithms can render useful for the provision of product recommendations. It is the case today for tag-based algorithms to work complementary to rating-based recommendation mechanisms to predict the user liking to various products. In this paper we propose an alternative algorithm for computing personalized recommendations of products, that uses exclusively the tags provided by the users. Our approach is based on the idea of using the semantic similarity of the user-provided tags for clustering them into groups of similar meaning. Afterwards, some measurable characteristics of users' Annotation Competency are combined with other metrics, such as user similarity, for computing predictions. The evaluation on data used from a real-world collaborative tagging system, citeUlike, confirmed that our approach outperforms the baseline Vector Space model, as well as other state of the art algorithms, predicting the user liking more accurately.