Social-Aware Document Similarity Computation for Recommender Systems

In recent years, huge amount of digital documents have been created in online social communities. Locating and recommending relevant documents to users in need is not a trivial task. In this paper, we introduce an approach to computing document similarity for building document recommender systems. This approach takes into account document internal content, together with social tags and associated users. These three factors are considered as three dimensions of a document in social space. The similarity computation is performed independently in each dimension, and then aggregated according to user preferences. This allows users to customize their views over the document. Experiments have been conducted with the proposed approach using data crawled from Cite Like. Experimental result has confirmed that all three dimensions have important contributions to document similarity computation.