A Particle Swarm approach to mitigate the apparent diversity-accuracy dilemma in recommendation domains in recommendation domains

Advances in Recommender Systems (RSs) have been focused on improving the system’s accuracy. However, accuracy alone is not enough to assess the practical effects. In real scenarios, diversity has been identified as a key dimension of recommendation utility. Thus, the main researches are focused in improve both, accuracy and diversity. This challenge remains an apparent dilemma that remains open and can boost sales by offering consumers both their mainstream and specific tastes. For this reason, we propose an approach to handle the accuracy-diversity dilemma. Our approach, based on a Particle Swarm Optimization (PSO), is a post-processing method to re-rank items from traditional RSs in order to improve diversity without accuracy losses. Experimental results in entertainment and e-commerce scenarios show that our strategy can improve users satisfaction. We improve the diversity up to 70% without significant accuracy losses.

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