Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study by Bahram Amini, Roliana Ibrahim, and Mohd Shahizan Othman.
Abstract:
Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.
Interesting work but I am uncertain about the need to “extract” semantic information from users. At least directly. As in linguistics, it may be enough to see where the user falls statistically and use that as a guide to the semantics. As in linguistics, it will miss the edge cases but those are likely to be missed anyway.