Abstract
This study examines the collaborative filtering in recommender system by categorising users according to their choices of place, food, local item purchase, etc. The proposed system will store the opinions of the local users about the sites, foods and products for purchase available in those sites. It uses collaborative filtering technique to find the similar users to a given querying user. The system recommends the best sites along with good foods and products available on those sites according to the recent data. Two hundred (male = 110, female = 90) married individuals from Bhubaneswar, Odisha (India) participated in this survey. Cosine similarity is used in the proposed system to find the similar users of a given input query user. The results revealed that collaborative filtering is the more reliable technique for personalised recommender systems. Experimental results show performance of the proposed system in terms of precision, recall and F-measure values.
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