Applying the technology acceptance model to evaluation of recommender systems

Abstract

In general, the study of recommender systems emphasizes the efficiency of techniques to provide accurate recommendations rather than factors influencing users' acceptance of the system; however, accuracy alone cannot account for users' satisfying experience. Bearing in mind this gap in the research, we apply the technology acceptance model (TAM) to evaluate user acceptance of a recommender system in the movies domain. Within the basic TAM model, we incorporate a new latent variable representing self-assessed user skills to use a recommender system. The experiment included 116 users who answered a satisfaction survey after using a movie recommender system. The results evince that perceived usefulness of the system has more impact than perceived ease of use to motivate acceptance of recommendations. Additionally, users' previous skills strongly influence perceived ease of use, which directly impacts on perceived usefulness of the system. These findings can assist developers of recommender systems in their attempt to maximize users' experience.