Combining Collaborative Filtering and Text Similarity for Expert Profile Recommendations in Social Websites

People-to-people recommendation differ from item recommendations in a number of ways, one of which is that individuals add information to their profile which is often critical in determining a good match. The most critical information can be in the form of free text or personal tags. We explore text-mining techniques to improve classical collaborative filtering methods for a site aimed at matching people who are looking for expert advice on a specific topic. We compare results from a LSA-based text similarity analysis, a simple user-user collaborative filter, and a combination of both methods used to recommend people to meet for a knowledge-sharing website. Evaluations show that LSA similarity has a better precision at low recall rates, whereas collaborative filters have a better precision at higher recall rates. A combination of both can outperform the results of the simpler algorithms.