Abstract
There is an increasing amount of multimedia content available to end users. Recommender systems help these end users by selecting a small but relevant subset of items for each user based on her/his preferences. This paper investigates the influence of affective metadata (metadata that describe the user’s emotions) on the performance of a content-based recommender (CBR) system for images. The underlying assumption is that affective parameters are more closely related to the user’s experience than generic metadata (e.g. genre) and are thus more suitable for separating the relevant items from the non-relevant. We propose a novel affective modeling approach based on users’ emotive responses. We performed a user-interaction session and compared the performance of the recommender system with affective versus generic metadata. The results of the statistical analysis showed that the proposed affective parameters yield a significant improvement in the performance of the recommender system.
Similar content being viewed by others
References
Adomavicius G., Tuzhilin A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE. Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Ali, K., Van Stam, W.: TiVo: making show recommendations using a distributed collaborative filtering architecture. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 394–401. ACM, New York, NY, USA (2004)
Arapakis, I., Moshfeghi, Y., Joho, H., Ren, R., Hannah, D., Jose, J., Gardens, L.: Integrating facial expressions into user profiling for the improvement of a multimodal recommender system. In: Proceedings of the IEEE International Conference on Multimedia and Expo, pp. 1440–1443 (2009)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: using social and content-based information in recommendation. In: Proceedings of the National Conference on Artificial Intelligence, pp. 714–720. Wiley, New York (1998)
Batliner A., Steidl S., Hacker C., Noth E.: Private emotions versus social interaction: a data-driven approach towards analysing emotion in speech. User Model. User-Adapt. Interact. J. Pers. Res. 18(1), 175–206 (2008). doi:10.1007/s11257-007-9039-4
Berger H., Denk M., Dittenbach M., Pesenhofer A., Merkl D.: Photo-based user profiling for tourism recommender systems. In: Psaila, G., Wagner, R. (eds) E-Commerce and Web Technologies, vol. 4655, pp. 46–55. Springer, Berlin (2007)
Bradley, M.M., Lang, P.J.: The International Affective Picture System (IAPS) in the Study of Emotion and Attention, chap. 2. Series in Affective Science. Oxford University Press, Oxford (2007)
Burke R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002). doi:10.1023/A:1021240730564
Carberry S., de Rosis F.: Introduction to special issue on affective modeling and adaptation. User Model. User-Adapt. Interact. 18(1), 1–9 (2008)
Caridakis G., Karpouzis K., Wallace M., Kessous L., Amir N.: Multimodal users affective state analysis in naturalistic interaction. J. Multimodal User Interfaces 3(1), 49–66 (2010)
Coan J., Allen J.: Handbook of Emotion Elicitation and Assessment. Oxford University Press, New York (2007)
Conati C., Maclaren H.: Empirically building and evaluating a probabilistic model of user affect. User Model. User-Adapt. Interact. 19(3), 267–303 (2009)
Cowie R., Cowie E.D., Tsapatsoulis N., Votsis G., Kollias S., Fellenz W., Taylor J.: Emotion recognition in human-computer interaction. IEEE Signal Process. Mag. 18(1), 32–80 (2001)
Darwin C.: The Expression of the Emotions in Man and Animals. Oxford University Press, New York (1872)
D’Mello S., Craig S., Witherspoon A., McDaniel B., Graesser A.: Automatic detection of learner’s affect from conversational cues. User Model. User-Adapt. Interact. J. Pers. Res. 18(1), 45–80 (2008). doi:10.1007/s11257-007-9037-6
Ekman P.: Basic emotions. In: Dalgleish, T., Power, T. (eds) Handbook of Cognition and Emotion, Wiley, New York (1999)
Fleiss J.L.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76(5), 378–382 (1971)
Goldberg L.R., Johnson J.A., Eber H.W., Hogan R., Ashton M.C., Cloninger C.R., Gough H.G.: The international personality item pool and the future of public-domain personality measures. J. Res. Pers. 40, 84–96 (2006)
González, G., López, B., de la Rosa, J.L.L.: Managing emotions in smart user models for recommender systems. In: Proceedings of 6th International Conference on Enterprise Information Systems ICEIS 2004, vol. 5, pp. 187–194 (2004)
Grouplens Data Sets.: http://www.grouplensorg/node/12. Accessed Sept 2010
Hanjalic A.: Extracting moods from pictures and sounds. IEEE Signal Process. Mag. 23(2), 90 (2006)
Hastie T., Tibshirani R., Friedman J.H.: The Elements of Statistical Learning. Springer, New York (2001)
Herlocker J., Konstan J., Terveen L., Riedl J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 53 (2004)
Ioannou S., Raouzaiou A., Tzouvaras V., Mailis T., Karpouzis K., Kollias S.: Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Netw. 18(4), 423–435 (2005)
Irun, M., Moltó Brotons, F.: Looking at pictures in North America and Europe: a cross-cultural study on the IAPS. In: Poster presented at the 1997 FEPS Meeting in Konstanz (1997)
Joho, H., Jose, J., Valenti, R., Sebe, N.: Exploiting facial expressions for affective video summarisation. In: Proceeding of the ACM International Conference on Image and Video Retrieval, pp. 1–8. ACM (2009)
Kim Y., Yum B., Song J., Kim S.: Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites. Expert Syst. Appl. 28(2), 381–393 (2005)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol. 2, no. 12, pp. 1137–1143. Morgan Kaufmann, San Mateo (1995)
Koren Y., Bell R., Volinsky C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009). doi:ieeecomputersociety.org/10.1109/MC.2009.263
Lang, P., Bradley, M., Cuthbert, B.: International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical Report a-6. Technical Report, University of Florida, Gainesville, FL (2005)
Lehman E.L., Romano J.: Testing Statistical Hypotheses. Springer, New York (2005)
Lew M.S., Sebe N., Djeraba C., Jain R.: Content-based multimedia information retrieval: state of the art and challenges. ACM Trans. Multimed. Comput. 2(1), 1–19 (2006)
Lichtenstein A., Oehme A., Kupschick S., Jürgensohn T.: Comparing Two Emotion Models for Deriving Affective States from Physiological Data, pp. 35–50. Springer-Verlag, Berlin (2008). doi:10.1007/978-3-540-85099-1_4
McNee, S., Lam, S., Konstan, J., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: User Modeling 2003: 9th International Conference, UM 2003, Johnstown, PA, USA, June 22–26, 2003: Proceedings, pp. 178–187. Springer-Verlag, Berlin (2003)
McQuiggan S., Mott B., Lester J.: Modeling self-efficacy in intelligent tutoring systems: an inductive approach. User Model. User-Adapt. Interact. J. Pers. Res. 18(1), 81–123 (2008). doi:10.1007/s11257-007-9040-y
Mehrabian A.: Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament. Curr. Psychol. 14(4), 261–292 (1996)
Nunes, M.A., Cerri, S., Blanc, N.: Improving recommendations by using personality traits in user profiles. In: Proceedings of I-KNOW’08 8th International Conference on Knowledge Management and Knowledge Technologies, pp. 92–100. Graz, Austria (2008)
Pantic M., Vinciarelli A.: Implicit human-centered tagging. IEEE Signal Process. Mag. 26(6), 173–180 (2009)
Pazzani, M., Billsus, D.: Content-Based Recommendation Systems. The Adaptive Web, pp. 325–341. doi:10.1007/978-3-540-72079-9_10 (2007)
Picard R.W.: Affective Computing. MIT Press, Cambridge (2000)
Plutchik R.: The nature of emotions. Am. Sci. 89(4), 344–350 (2001)
Pogačnik M., Tasič J., Meža M., Košir A.: Personal content recommender based on a hierarchical user model for the selection of TV programmes. User Model. User Adapt. Interact. 15, 425–457 (2005)
Porayska-Pomsta K., Mavrikis M., Pain H.: Diagnosing and acting on student affect: the tutor’s perspective. User Model. User-Adapt. Interact. J. Pers. Res. 18(1), 125–173 (2008). doi:10.1007/s11257-007-9041-x
Posner J., Russell J.A., Peterson B.: The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17, 715–734 (2005)
Rashid, A., Albert, I., Cosley, D., Lam, S., McNee, S., Konstan, J., Riedl, J.: Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th International Conference on Intelligent User Interfaces, January, ACM, pp. 13–16 (2002)
Ribeiro R., Pompéia S., Bueno O.: Comparison of Brazilian and American norms for the International Affective Picture System (IAPS). Revista Brasileira de Psiquiatria 27, 208–215 (2005)
Rottenberg J., Ray R.D., Gross J.J.: Emotion Elicitation Using Films, chap. 2. Oxford University Press, London (2007)
Scheirer J., Fernandez R., Klein J., Picard R.W.: Frustrating the user on purpose: a step toward building an affective computer. Interact. Comput. 14(2), 93–118 (2002). doi:10.1016/S0953-5438(01)00059-5
Scherer K.: What are emotions? And how can they be measured? Soc. Sci. Inf. 44(4), 695 (2005)
Schröeder, M., Baggia, P., Burkhardt, F., Oltramari, A., Pelachaud, C., Peter, C., Zovato, E.: Emotion markup language (emotionml) 1.0. W3C Working Draft 29 July 2010. http://www.w3.org/TR/2010/WD-emotionml-20100729/ (2010)
Shan M.K., Kuo F.F., Chiang M.F., Lee S.Y.: Emotion-based music recommendation by affinity discovery from film music. Expert. Syst. Appl. 36(4), 7666–7674 (2009). doi:10.1016/j.eswa.2008.09.042
Tkalčič, M., Tasič, J, Košir, A.: The LDOS-PerAff-1 corpus of face video clips with affective and personality metadata. In: Kipp M (ed.) Proceedings of the LREC 2010 Workshop on Multimodal Corpora: Advances in Capturing, Coding and Analyzing Multimodality (2010)
Verschuere, B., Crombez, G., Koster, E.: Cross Cultural Validation of the IAPS. Ghent University, Ghent, Belgium. http://users.ugent.be/~bvschuer/Iaps.pdf (2007)
Villon, O., Lisetti, C.: A user-modeling approach to build user’s psycho-physiological maps of emotions using bio-sensors. In: The 15th IEEE International Symposium on Robot and Human Interactive Communication, 2006, ROMAN 2006, pp 269–276 (2006)
Vinciarelli A., Pantic M., Bourlard H.: Social signal processing: survey of an emerging domain. Image Vis. Comput. 27(12), 1743–1759 (2009)
Westen D.: Psychology: Mind, Brain and Culture. 2nd edn. Wiley, New York (1999)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. 2nd edn. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2005)
Yannakakis G., Hallam J., Lund H.: Entertainment capture through heart rate activity in physical interactive playgrounds. User Model. User-Adapt. Interact. J. Pers. Res. 18(1), 207–243 (2008). doi:10.1007/s11257-007-9036-7
Yik M., Russell J.A., Ahn C.k., Fernandez Dols J.M., Suzuki N.: Relating the five-factor model of personality to a circumplex model of affect: a five-language study. In: McCrae, R.R., Allik, J. (eds) The Five-Factor Model of Personality Across Cultures, pp. 79–104. Kluwer Academic Publishers, New York (2002)
Zeng Z., Pantic M., Roisman G.I., Huang T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009). doi:10.1109/TPAMI.2008.52
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tkalčič, M., Burnik, U. & Košir, A. Using affective parameters in a content-based recommender system for images. User Model User-Adap Inter 20, 279–311 (2010). https://doi.org/10.1007/s11257-010-9079-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11257-010-9079-z