Collaborative Filtering (CF)-based recommender systems are indispensable tools to find items of interest from the unmanageable number of available items. Moreover, companies who deploy a CF-based recommender system may be able to increase revenue by drawing customers’ attention to items that they are likely to buy. However, the sheer number of customers and items typical in e-commerce systems d...

Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few approaches that avoid this have rather unsatisfyingly lacked theoretical foundations, or do not scale. We present a learning-torank formulation that optimizes the fraction of satisfied users, with a scalable algorith...

In this paper, we describe and compare two distinct algorithms aiming at the low-rank approximation of a user-item ratings matrix in the context of Collaborative Filtering (CF). The first one implements standard Principal Component Analysis (PCA) of an association matrix formed from the original data. The second algorithm is based on h-NLPCA, a nonlinear generalization of standard PCA, which ut...

This paper presents our work on the 2016 CLEF eHealth Task 3.We used Indri to conduct our experiments. We used CHV to expand query and proposed a learning-to-rank algorithm to re-rank the result.

LambdaMART is the boosted tree version of LambdaRank, which is based on RankNet. RankNet, LambdaRank, and LambdaMART have proven to be very successful algorithms for solving real world ranking problems: for example an ensemble of LambdaMART rankers won Track 1 of the 2010 Yahoo! Learning To Rank Challenge. The details of these algorithms are spread across several papers and reports, and so here...

Collaborative Filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems in recent years. However, most existing CF based recommender systems worked in a centralized way and suffered from its shortage in scalability as their calculation complexity increased quickly both in time and space when the record in user database increases. In this article...

In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). “NSN-AP-CF” processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the Apriori algorithm. Finally, it groups dynamically the users base...

This paper describes the approach proposed by UNIFESP for the MediaEval 2016 Predicting Media Interestingness Task and for its video subtask only. The proposed approach is based on combining learning-to-rank algorithms for predicting the interestingness of videos by their visual content.

Matroid theory is in the center of Combinatorics, Finite Geometry, Lattice theory and Combinatorial Optimization. During the last decades, extensive search was done in order to find a good degree of generality which still preserves the validity of deep results known for matroids. One of such generalizations is the concept of bouquet of matroids introduced in 1983 by Deza, Frank1 and Laurent and...