| Abstrakt: |
Community question answering forums allow users to find knowledge on a topic of interest by asking questions and getting answers from experts. However, it can be challenging to find experts who are knowledgeable in a particular subject, especially when there are millions of questions and thousands of new queries every day.This paper proposes a novel expert recommendation system called Semantic Similarity and Clustering-based Collaborative Filtering (SSC-CF). SSC-CF addresses two key drawbacks of collaborative filtering: scalability and sparsity. Sparsity is addressed by using matrix factorization. In matrix factorization, latent features are identified to detect similarity and generate a prediction based on both the question and the user entities. Whereas a clustering method is employed to group users and questions with shared interests to address scalability. The recommendation system’s accuracy is further improved by incorporating semantic similarity. SSC-CF is evaluated on three Stack Exchange sites: gaming, physics, and scifi. The results clearly show that the proposed technique, SSC-CF, is effective in addressing both scalability and sparsity. |
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