Offline optimization for user-specific hybrid recommender systems

Massive availability of multimedia content has given rise to numerous recommendation algorithms that tackle the associated information overload problem. Because of their growing popularity, selecting the best one is becoming an overload problem in itself. Hybrid algorithms, combining multiple individual algorithms, offer a solution, but often require manual configuration and power only a few individual recommendation algorithms. In this work, we regard the problem of configuring hybrid recommenders as an optimization problem that can be trained in an offline context. Focusing on the switching and weighted hybridization techniques, we compare and evaluate the resulting performance boosts for hybrid configurations of up to 10 individual algorithms. Results showed significant improvement and robustness for the weighted hybridization strategy which seems promising for future self-adapting, user-specific hybrid recommender systems

Online optimization for user-specific hybrid recommender systems

User-specific hybrid recommender systems aim at harnessing the power of multiple recommendation algorithms in a user-specific hybrid scenario. While research has previously focused on self-learning hybrid configurations, such systems are often too complex to take out of the lab and are seldom tested against real-world requirements. In this work, we describe a self-learning user-specific hybrid recommender system and assess its ability towards meeting a set of pre-defined requirements relevant to online recommendation scenarios: responsiveness, scalability, system transparency and user control. By integrating a client-server architectural design, the system was able to scale across multiple computing nodes in a very flexible way. A specific user-interface for a movie recommendation scenario is proposed to illustrate system transparency and user control possibilities, which integrate directly in the hybrid recommendation process. Finally, experiments were performed focusing both on weak and strong scaling scenarios on a high performance computing environment. Results showed performance to be limited only by the slowest integrated recommendation algorithm with very limited hybrid optimization overhead

Coherence and Inconsistencies in Rating Behavior - Estimating the Magic Barrier of Recommender Systems

Recommender Systems have to deal with a wide variety of users and user types that express their preferences in di erent ways. This di erence in user behavior can have a profound impact on the performance of the recommender system. Users receive better (or worse) recommendations depending on the quantity and the quality of the information the system knows about them. Speci cally, the inconsistencies in users' preferences impose a lower bound on the error the system may achieve when predicting ratings for one particular user { this is referred to as the magic barrier. In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are a icted with inconsistencies { noise. Furthermore, we propose a measure of the consistency of user ratings (rating coherence) that predicts the performance of recommendation methods. More speci cally, we show that user coherence is correlated with the magic barrier; we exploit this correlation to discriminate between easy users (those with a lower magic barrier) and di cult ones (those with a higher magic barrier). We report experiments where the recommendation error for the more coherent users is lower than that of the less coherent ones. We further validate these results by using two public datasets, where the necessary data to identify the magic barrier is not available, in which we obtain similar performance improvementsThis research was in part supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P

The Magic Barrier of Recommender Systems – No Magic, Just Ratings

Recommender Systems need to deal with different types of users who represent their preferences in various ways. This difference in user behaviour has a deep impact on the final performance of the recommender system, where some users may receive either better or worse recommendations depending, mostly, on the quantity and the quality of the information the system knows about the user. Specifically, the inconsistencies of the user impose a lower bound on the error the system may achieve when predicting ratings for that particular user. In this work, we analyse how the consistency of user ratings (coherence) may predict the performance of recommendation methods. More specifically, our results show that our definition of coherence is correlated with the so-called magic barrier of recommender systems, and thus, it could be used to discriminate between easy users (those with a low magic barrier) and difficult ones (those with a high magic barrier). We report experiments where the rating prediction error for the more coherent users is lower than that of the less coherent ones. We further validate these results by using a public dataset, where the magic barrier is not available, in which we obtain similar performance improvements