A Novel Similarity Measure to Identify Effective Similar Users in Recommender Systems
Rajeswari Nakka1, G.V.S.N.R.V.Prasad2, R.Kiran Kumar3
1Rajeswari Nakka, Research Scholar, Department of CSE, Jntuk, Kakinada, India.
2Dr G.V.S.N.R.V.Prasad, Professor of CSE, Gudlavalleru Engineering College, Gudlavalleru, India.
3R. Kiran Kumar, Department of Computer Science, Krishna University, Machilipatnam, India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 834-840 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I11720789S219/19©BEIESP DOI: 10.35940/ijitee.I1172.0789S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent years there is a drastic increase in information over the internet. Users get confused to find out best product on the internet of one’s interest. Here the recommender system helps to filter the information and gives relevant recommendations to users so that the user community can find the item(s) of their interest from huge collection of available data. But filtering information from the users reviews given for various items seems to be a challenging task for recommending the user interested things. In general similarities between the users are considered for recommendations in collaborative filtering techniques. This paper describes a new collaborative filtering technique called Adaptive Similarity Measure Model [ASMM] to identify similarity between users for the selection of unseen items. Out of all the available items most similarities would be sorted out by ASMM for recommendation which varies from user to user.
Keywords: Collaborative Filtering, CB-Filtering, ASMM and Recommendation Systems.
Scope of the Article: Community Information Systems