Content-Based Recommender Systems Taxonomy

Abstract In the era of internet access, recommender systems try to alleviate difficulty consumers face while trying find items (e.g. services, products, or information) that better match their needs. To do so, a system selects and proposes (possibly unknown) may be interest some candidate consumer, by predicting her/his preference for this item. Given diversity needs between enormous variety recommended, large set approaches have been proposed research community. This paper provides review in entire area content-based systems, not only one part it. facilitate understanding, we provide categorization each approach based on tools techniques employed, which results main contribution paper, taxonomy. way, reader acquires quick complete understanding area. Finally, comparison according ability efficiently handle well-known drawbacks.