Web-based Personalized Hybrid Book Recommendation System

Recommender Systems have been around for more than a decade now. Choosing what book to read next has always been a question for many. Even for students, deciding which textbook or reference book to read on a topic unknown to them is a big question. In this paper, we try to present a model for a web-based personalized hybrid book recommender system which exploits varied aspects of giving recommendations apart from the regular collaborative and content-based filtering approaches. Temporal aspects for the recommendations are incorporated. Also for users of different age, gender and country, personalized recommendations can be made on these demographic parameters. Scraping information from the web and using the information obtained from this process can be equally useful in making recommendations.