Personalized Persuasive Recommender System: A Framework and a Machine Learning‐Based Implementation

ABSTRACT Since the emergence of Recommender Systems (RS), most of the research has focused on improving the accuracy of a recommender system. However, the literature has demonstrated increasing evidence that it is vital for a recommender system to focus not only on the accuracy of the provided recommendations but also on other factors that influence the acceptance of recommendations and the extent to which these recommendations are convincing or persuasive. Consequently, there becomes a need for new research paradigms to help improve the capabilities of recommender systems, which goes beyond recommendation accuracy. One of the recently emerged research directions that consider this need fosters the idea of adopting human‐related theories from the social sciences domain, such as persuasiveness of social communication. In this context, however, a challenging, non‐trivial, and not fully explored issue that arises is: how to integrate human‐related theories into a recommender system to increase user's acceptance ? This paper aims to address this issue by providing a reference architecture framework to adapt and integrate persuasion features as a substantial characteristic of recommender systems. The proposed framework, named Per sonalized Per suasive RS ( PerPer ), adopts concepts from the social sciences literature, namely personality traits and persuasion principles. This paper also introduces a machine learning‐based implementation of PerPer . In particular, it adapts the Learning Automata concepts to support learning capabilities. PerPer is evaluated using a user study where we implemented a prototype of a movie RS. The user study involved three parts, namely, the Conventional Recommender System (CRS) and two variants of PerPer that we called the General Reinforcement Approach (PerPer‐GRA) and the Boosted Reinforcement Approach (PerPer‐BRA). The analysis of the results obtained from 44 participants shows that PerPer was able to enhance users' acceptance of the recommendations in comparison to CRS. The results also show that the PerPer‐BRA outperforms the PerPer‐GRA in terms of accelerating the convergence of the best persuasion method while maintaining improvement in users' acceptance.