Volume 16 - Issue 1
Filtering and Summarization Architecture for News Pages
Abstract
To tackle the issue that collaborative filtering algorithm just uses the client thing rating lattice and doesn't think about semantic data, we proposed a novel collaborative filtering recommendation algorithm dependent on information chart. Utilizing the information chart portrayal learning technique, this strategy inserts the cur-rent semantic information into a low-dimensional vector space. It coordinates the semantic data of things into the collaborative filtering recommendation by computing the semantic similitude between things. The weakness of collaborative filtering algorithm which doesn't consider the semantic data of things is survived, and subsequently the impact of collaborative filtering recommendation is enhanced the semantic level. Test results show that the proposed algorithm can get higher qualities on exactness, review, and F-measure for collaborative filtering recommendation.
Paper Details
PaperID: 201006
Author's Name: K. Ajay Kumar, K. Praveen Kumar and L.V. Kiran
Volume: Volume 16
Issues: Issue 1
Keywords: Collaborative Filtering, Hubs, Precision, F-measure.
Year: 2020
Month: February
Pages: 33-38