A collaborative filtering similarity measure based on singularities.

Recommender systems play an important role in reducing the negative impact of informa- tion overload on those websites where users have the possibility of voting for their prefer- ences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to cal- culate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed

A framework for collaborative filtering recommender systems

As the use of recommender systems becomes more consolidated on the Net, an increasing need arises to develop some kind of evaluation framework for collaborative filtering measures and methods which is capable of not only testing the prediction and recommendation results, but also of other purposes which until now were considered secondary, such as novelty in the recommendations and the users? trust in these. This paper provides: (a) measures to evaluate the novelty of the users? recommendations and trust in their neighborhoods, (b) equations that formalize and unify the collaborative filtering process and its evaluation, (c) a framework based on the above-mentioned elements that enables the evaluation of the quality results of any collaborative filtering applied to the desired recommender systems, using four graphs: quality of the predictions, the recommendations, the novelty and the trust

Incorporating reliability measurements into the predictions of a recommender system

In this paper we introduce the idea of using a reliability measure associated to the predic- tions made by recommender systems based on collaborative filtering. This reliability mea- sure is based on the usual notion that the more reliable a prediction, the less liable to be wrong. Here we will define a general reliability measure suitable for any arbitrary recom- mender system. We will also show a method for obtaining specific reliability measures specially fitting the needs of different specific recommender systems

Movie Recommendation Using OLAP and Multidimensional Data Model

Part 4: Data Analysis and Information RetrievalInternational audienceThis research proposes an adoption of data warehousing concepts to create a movie recommender system. The data warehouse is generated using ETL process in a desired star schema. The profiles of users and movies are created using multidimensional data model. The data are analyzed using OLAP, and the reports are generated using data mining and analysis tools. The recommended movies are selected using multi-criteria candidate selection. The movies which present the genres that match individual preference are recommended to the particular user. The multidimensional data model and OLAP provide high performance to discover the new knowledge in the big data