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Recommendations help humans in making decisions and hence contribute in increase of user satisfaction. For good recommendations; the recommender should be more precise. From past decades, Collaborative Filtering (CF) has been explored by researchers because of its efficiency and effectiveness. The main objective of CF is to find most similar items using various similarity measures. This research paper proposes improvised mobile recommender that significantly increases accuracy for recommended right plans for mobile users using similarity fusion. Experimental results show that the proposed recommender using similarity fusions provide better recommendation quality.

An Improvised Recommendation System For Mobile Plans Using Similarity Fusion