Arthur F. da Costa

Multimodal Interactions in Recommender Systems: An Ensembling Approach

In this paper, we present a technique that uses multimodal interactions of users to generate a more accurate list of recommendations optimized for the user. Our approach is a response to the actual scenario on the Web which allows users to interact with the content in different ways, and thus, more information about his preferences can be obtained to improve recommendation. The proposal consists of an ensemble technique that combines rankings generated by unimodal recommenders based on particular interaction types. By using a combination of implicit and explicit feedback from users, we are able to provide better recommendations, as shown by our experimental evaluation presented in this paper.

Improving Personalized Ranking in Recommender Systems with Multimodal Interactions

This paper proposes a conceptual framework which uses multimodal user feedback to generate a more accurate personalized ranking of items to the user. Our technique is a response to the actual scenario on the Web, where users can consume content following different interaction paradigms, such as rating, browsing, sharing, etc. We developed a post-processing step to ensemble rankings generated by unimodal-based state-of-art algorithms, using a set of heuristics which analyze the behavior of the user during consumption. We provide an experimental evaluation using the Movie Lens 10M dataset, and the results show that better recommendations can be provided when multimodal interactions are considered for profiling the preferences of the users.

CoRec: a co-training approach for recommender systems

In Recommender Systems, a large amount of labeled data must be available beforehand to obtain good predictions. However, labeled data are often limited and expensive to obtain, since labeling typically requires human expertise, time, and labor. This paper proposes a framework, named CoRec, which is based on a co-training approach that drives two recommenders to agree with each others predictions to generate their own. We used three publicly available datasets from movies, jokes and books domains, as well as two well-known recommender algorithms, to demonstrate the efficiency of the approach under different configurations. The experiments show that better accuracy can be obtained when recommender algorithms are simultaneously co-trained from multiple views to make predictions.

Boosting collaborative filtering with an ensemble of co-trained recommenders

Abstract Collaborative Filtering (CF) is one of the best performing and most widely used approaches for recommender systems. Although significant progress has been made in this area, current CF methods still suffer from cold-start and sparsity problems. A primary issue is that the fraction of users willing to rate items tends to be very small in most practical applications, which causes the number of users and/or items with few or no interactions in recommendation databases to be large. As a direct consequence of ratings sparsity, recommender algorithms may provide poor recommendations (reducing accuracy) or decline recommendations (reducing coverage). This paper proposes an ensemble scheme based on a co-training approach, named ECoRec, that drives two or more recommenders to agree with each others’ predictions to generate their own. The experiments on eight real-life public databases show that better accuracy can be obtained when recommender algorithms are simultaneously trained from multiple views and combined into an ensemble to make predictions.

Case recommender: a flexible and extensible python framework for recommender systems

This paper presents a polished open-source Python-based recommender framework named Case Recommender, which provides a rich set of components from which developers can construct and evaluate customized recommender systems. It implements well-known and state-of-the-art algorithms in rating prediction and item recommendation scenarios. The main advantage of the Case Recommender is the possibility to integrate clustering and ensemble algorithms with recommendation engines, easing the development of more accurate and efficient approaches.