International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume5,February 2018,

Topic : A measure of Temporal Contextual Information on Trust based Recommender Systems

Authors:Ankur Chaturevdi || Dilip Kumar Sharma

Abstract:In an era of information age, recommender system helps users to make an effective decision. Collaborative filtering is one of the techniques to provide a personalized recommendation to users. Collaborative filtering based recommender technique provides the recommendation by aggregating ratings from similar users to predict ratings for an active user (who wants a recommendation). The similarity has a greater impact because it acts as a criterion to identify a group of similar users whose ratings will be merged to generate a recommendation for the new item for an active user. However, there are a lot of issues in Collaborative filtering for e.g. data sparsity and cold start, which can be removed by incorporating trust information. We propose a methodology to include temporal context information in providing accurate rating prediction along with Trust matrix and also propose a framework to analyze the performance of Trust-based recommender algorithms on Film Trust dataset which includes temporal context information. .

Keywords: Collaborative filtering, Recommender Systems

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DOI: 01.1617/vol5/iss2/pid43985


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Editor-in-Chief

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Dr. Allon Guez
Professor, Drexel University,
USA


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IMPACT FACTOR: 4.890

ISSN(Online):2394-2320

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
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