Marcelo G. Armentano, Ingrid Christensen, and Silvia Schiaffino

ISISTAN Research Institute (CONICET/UNICEN), Tandil, Argentina. (e-mail: (marcelo.armentano@isistan.unicen.edu.ar, ingrid.christensen@isistan.unicen.edu.ar, silvia.schiaffino@isistan.unicen.edu.ar).

Manuscript received on May 5, 2015.
Accepted for publication on June 5, 2015.
Published on June 15, 2015.

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.

Key words: Recommender systems, evaluation, user acceptance, technology acceptance model.

DESCARGAR ARTÍCULO EN FORMATO PDF

ACKNOWLEGMENT

This study was partially supported by research projects PIP-0181-CONICET and PICT-2011-0366 awarded by ANPCyT, Argentina.

[1] F. Ricci, L. Rokach, and B. Shapira, Introduction to Recommender Systems Handbook. Springer, 2011, ch. 1, pp. 1-35.         [ Links ]

[2] K. Swearingen and R. Sinha, "Beyond algorithms: An HCI perspective on recommender systems," in ACM SIGIR Workshop on Recommender Systems, vol. 13, 2001, pp. 393-408.         [ Links ]

[3] M. Chuttur, "Overview of the technology acceptance model: Origins, developments and future directions," Working Papers on Information Systems, vol. 9, no. 37, pp. 1-22, 2009.         [ Links ]

[4] M. G. Armentano, R. Abalde, S. Schiaffino, and A. Amandi, "User acceptance of recommender systems: Influence of the preference elicitation algorithm," in 9th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), Nov. 2014, pp. 72-76.         [ Links ]

[5] F. D. Davis, "Perceived usefulness, perceived ease of use, and user acceptance of information technology," MIS Q., vol. 13, no. 3, pp. 319-340, Sep. 1989.         [ Links ]

[6] I. Ajzen and M. Fishbein, Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice-Hall, 1980.         [ Links ]

[7] H.-Y. Lee, H. Ahn, and I. Han, "VCR: Virtual community recommender using the technology acceptance model and the user's needs type," Expert Systems with Applications, vol. 33, no. 4, pp. 984-995, Nov. 2007.         [ Links ]

[8] A. Asosheh, S. Bagherpour, and N. Yahyapour, "Extended acceptance models for recommender system adaption, case of retail and banking service in Iran," WSEAS Trans. on Business and Economics, vol. 5, no. 5, pp. 189-200, May 2008.         [ Links ]

[9] R. Hu and P. Pu, "Acceptance issues of personality based recommender systems," in Proc. of ACM RecSys'09. New York, NY, USA: ACM, 2009, pp. 221-224.         [ Links ]

[10] D. Baier and E. Stüber, "Acceptance of recommendations to buy in online retailing," Journal of Retailing and Consumer Services, vol. 17, no. 3, pp. 173-180, 2010, new Technologies and Retailing: Trends and Directions. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0969698910000202        [ Links ]

[11] P. Pu, L. Chen, and R. Hu, "A user-centric evaluation framework for recommender systems," in Proceedings of the fifth ACM conference on Recommender systems. ACM, 2011, pp. 157-164.         [ Links ]

[12] H.-C. Chen, C.-C. Hsu, C.-H. Chang, and Y.-M. Huang, "Applying the technology acceptance model to evaluate the learning companion recommendation system on Facebook," in IEEE Fourth International Conference on Technology for Education (T4E), 2012, pp. 160-163.         [ Links ]

[13] B. P. Knijnenburg, M. C. Willemsen, Z. Gantner, H. Soncu, and C. Newell, "Explaining the user experience of recommender systems," User Modeling and User-Adapted Interaction, vol. 22, no. 4-5, pp. 441-504, 2012.         [ Links ]

[14] Y.-H. Hung, P.-C. Hu, and W.-T. Lee, "Improving the design and adoption of travel websites: An user experience study on travel information recommender systems," in 5th IASDR International Conference, Tokio, Japan, 2013.         [ Links ]

[15] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, "User acceptance of information technology: Toward a unified view," MIS Quarterly, vol. 27, no. 3, pp. 425-478, 2003.         [ Links ]

[16] Y. yao Wang, "Antecedents of review and recommendation systems acceptance," Ph.D. dissertation, Iowa State University, 2011.         [ Links ]

[17] P. Pu, L. Chen, and R. Hu, "Evaluating recommender systems from the user's perspective: survey of the state of the art," User Modeling and User-Adapted Interaction, vol. 22, no. 4-5, pp. 317-355, 2012.         [ Links ]

[18] H. F. Kaiser, "The varimax criterion for analytic rotation in factor analysis," Psychometrika, vol. 23, no. 3, pp. 187-200, 9 1958.         [ Links ]

[19] ----------, "The application of electronic computers to factor analysis," Educational and Psychological Measurement, vol. 20, pp. 141-151, 1960.         [ Links ]

[20] R. B. Cattell, "The scree test for the number of factors," Multivariate Behavioral Research, vol. 1, pp. 245-276, 1966.         [ Links ]

[21] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate data analysis. Prentice Hall Higher Education, 2010.         [ Links ]