MovieReco: A Recommendation System

Recommender Systems act as personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with no emphasis on the trustworthiness of the user. RS depends on information provided by different users to gather its knowledge. We believe, if a large group of users provide wrong information it will not be possible for the RS to arrive in an accurate conclusion. The system described in this paper introduce the concept of Testing the knowledge of user to filter out these “bad users". This paper emphasizes on the mechanism used to provide robust and effective recommendation.

Authors:

• Dipankaj G Medhi
• Juri Dakua

Keywords:

• Collaborative Filtering
• Content Based Filtering
• Intelligent Agent
• Level of Interest
• Recommendation System.

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