Pre-Recommendation Clustering and Review Based Approach for Collaborative Filtering Based Movie Recommendation
Saudagar L. Jadhav, Manisha P. Mali · International Journal of Information Technology and Computer Science · 2016
The recommendation is playing an essential part in our lives.Precise reco mmendations facilitate users to swiftly locate desirable items without being inundated by irrelevant informat ion.In the last few years, the amount of customers, products and online informat ion has raised speedily and results out into the huge data analysis problem for reco mmender systems.While handling and evaluating such large-scale data, usual service recommender systems regularly undergo scalability and inefficiency problems.Nowadays, in mu ltimed ia platform such as movie, music, games, the use of Reco mmender System is increased.Co llaborative Filtering is a dominant filtering technique used by many RSs.CF utilizes the rating history of the user to find out "like minded" users and this set of like -minded user is then used to recommend the movies which are liked by these like-minded users but did not watch by the active user.Thus, in CF, to find out the "neighborhood" the rating history of a user is used, but the reason behind the rating is not considered at all.This will lead to inaccuracy in finding a neighborhood set and subsequently in recommendation also.To cope with these scalability and accuracy challenges, this paper proposes an innovative solution, Clustering and Review based Approach for Collaborative Filtering based Recommendation.This innovative approach is enacted with the two stages; in the first stage the clustering of the available movies for recommendation is clustered into the subclasses for further co mputation.In the succeeding stage, the methodology based on reviews is utilized for finding neighborhood set in User Based Collaborative Filtering.