Header menu link for other important links
Introducing hybrid technique for optimization of book recommender system
M. Chandak, , D. Mukhopadhyay
Published in Elsevier B.V.
Volume: 45
Issue: C
Pages: 23 - 31
E-Commerce has already entered into the Indian market for online shopping. People are more inclined towards online shopping which has changed the complete market scenario. There are several online shopping portals offered by organizations such as, Amazon, Flipkart, Snapdeal, Junglee, Jabong, and others, which are enjoying their online market share. As the number of online buyers and traders are increasing, effective business techniques need to be adopted to handle the large amount of data generated every day. Recommendation Systems play an important role in filtering this data and providing adequate information to the users. Various techniques like Collaborative Filtering, Content-based, and Demographic have been adopted for recommendation but there are several drawbacks causing these techniques to fail in providing effective recommendations. Therefore, it is necessary to identify more distinguishing features for optimizing these techniques. This can be achieved through utilizing the strengths of various techniques in a hybrid manner. This paper describes an effective hybrid technique for book recommendation with the use of Ontology for user profiling to increase system efficiency. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
About the journal
JournalData powered by TypesetProcedia Computer Science
PublisherData powered by TypesetElsevier B.V.