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. that go beyond the quality of the prediction algorithm. Several models attempted to address this problem, explaining and predicting the use of a system; nonetheless the Technology Acceptance Model (TAM) has been the one that has met with approval within the Information Systems community (3). This paper aims at exploring potential user acceptance issues on a traditional recommender system, using the TAM. Within the basic TAM model, we incorporate a new latent variable representing self-assessed user skills to use a recommender system. We conducted an empirical user study using a movie recommender system as a testbed, as well as a questionnaire applicable to any recommender system in the entertainment domain (books, music, movies, etc.). The results evidence that the two main factors impacting on user acceptance are perceived usefulness and perceived ease of use, which is also affected by supposed skills in the use of recommender systems. The remainder of this paper is organized as follows. Section 2 presents the Technology Acceptance Model used in our study. Section 3 presents some related work regarding the application of TAM to recommender systems. Section 4 describes the methodology used in our study and Section 5 presents the results obtained. Finally, Section 6 presents our conclusions.