Improving Recommendation using Forecast based approach and Re-ranking Approache
Mr. Mapari Vikas Prakash, Prof. Patil Pramod · IOSR Journal of Computer Engineering · 2014
Collaborative filtering is one of the most promising techniques in recommender systems, providing personalized recommendations to users based on their previously expressed preferences in the form of ratings and those of other similar users.A recommender system uses Collaborative Filtering or Content-Based methods to predict new items of interest for a user.Although both methods have their own and distinct advantages but individually they fail to provide good recommendations in many situations.Incorporating components from collaborative and content based methods, can overcome these challenges like data sparsity, stability, accuracy and correlation of traditional recommender systems.Inadequate ratings lot of time gives poor quality of recommendations in terms of accuracy.Various approaches are used for overcome these issues: i) Firstly, we propose to improve data sparsity and correlation.ii) Secondly, we aim to tackle the problem of rank and relevance and we improve recommender system in novelty & diversity using rank & relevance technique.