About Performance Evaluation of the Movie Recommendation Systems

About Performance Evaluation of the Movie Recommendation Systems

Movie recommendation systems are now becoming very popular both commercially and also in the research community, where many approaches have been proposed for providing recommendations. For more and more usage of any system, it is necessary to know about the efficiency of the system and for this reason performance evaluation of a Recommendation system is done. By doing the performance evaluation...

Improving Performance of Movie Recommendation in Collaborative Filtering Systems

Collaborative filtering has been most widely used in commercial sites to recommend items based on the history of user preferences for items. The idea behind this method is to find similar users whose ratings for items are incorporated to make recommendation. Hence, similarity calculation is most critical in recommendation performance. For movie recommendation, this paper enhances performance of...

Movie Recommendation with DBpedia

In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic ver...

Movie Recommendation Systems Using An Artificial Immune System

Explainable Movie Recommendation Systems by using Story-based Similarity

The goal of this paper is to provide a story-based explanation for movie recommendation systems, achieved by a multiaspect explanation and narrative analysis methods. We explain how and why particular movies are similar based on following two aspects: (i) composition of movie characters and (ii) interactions among the characters. These aspects correspond to story-based features of the movies th...