A Review of Tag-aware Recommender Systems for Future Applications in Research and Development

Due to the recent growth in online data about customers and the growing social web content due to the ever-increasing popularity of social media services, tag-aware recommendation systems are attracting more attention. Tag-aware recommendation systems(TRS) effectively reveal user preferences and extract latent semantic information of items through social tag information. Therefore, a review of the present status of the literature on tag-aware recommendation systems is necessary to identify future research possibilities and directions. This article reviews the research direction in terms of approaches used, application domains, challenges and problems related to developing a system of recommendations, and evaluation metrics used to evaluate performance. It also, presents the insights gained and potential directions for further research. We evaluated 33 scientific papers thorough quantitative evaluation. Although TRS is a flexible approach to managing information, we found that the number of publications are few over the years. Also, scientific publications are limited to specific datasets and types of publications and focus on a specific field more than others. 73% of the papers were published as a journal, and 29% of papers used collaborative filtering approach. The most covered domin was the music domain with 26%, and the most used dataset was Last.FM with 20%.