The Reason Why: A Survey of Explanations for Recommender Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8382)


Recommender Systems refer to those applications that offer contents or items to the users, based on their previous activity. These systems are broadly used in several fields and applications, being common that an user interact with several recommender systems during his daily activities. However, most of these systems are black boxes which users really don’t understand how to work. This lack of transparency often causes the distrust of the users. A suitable solution is to offer explanations to the user about why the system is offering such recommendations. This work deals with the problem of retrieving and evaluating explanations based on hybrid recommenders. These explanations are meant to improve the perceived recommendation quality from the user’s perspective. Along with recommended items, explanations are presented to the user to underline the quality of the recommendation. Hybrid recommenders should express relevance by providing reasons speaking for a recommended item. In this work we present an attribute explanation retrieval approach to provide these reasons and show how to evaluate such approaches. Therefore, we set up an online user study where users were asked to provide movie feedback. For each rated movie we additionally retrieved feedback about the reasons this movie was liked or disliked. With this data, explanation retrieval can be studied in general, but it can also be used to evaluate such explanations.


Hybrid recommender systems Explanations Evaluation Persuasion Satisfaction Decision support 


  1. 1.
    Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 257–297. Springer, New York (2011)CrossRefGoogle Scholar
  2. 2.
    McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI ’06 Extended Abstracts on Human factors in Computing Systems, CHI EA ’06, pp. 1097–1101. ACM, New York (2006)Google Scholar
  3. 3.
    Vig, J., Sen, S., Riedl, J.: Tagsplanations: explaining recommendations using tags. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, IUI ’09, pp. 47–56. ACM, New York (2009)Google Scholar
  4. 4.
    Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, CSCW ’00, pp. 241–250. ACM, New York (2000)Google Scholar
  5. 5.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5–53 (2004)CrossRefGoogle Scholar
  6. 6.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW ’94, pp. 175–186. ACM, New York (1994)Google Scholar
  7. 7.
    McSherry, D.: Explanation in recommender systems. Artif. Intell. Rev. 24(2), 179–197 (2005)CrossRefzbMATHGoogle Scholar
  8. 8.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, New York (2011)CrossRefGoogle Scholar
  9. 9.
    Sae-Ueng, S., Pinyapong, S., Ogino, A., Kato, T.: Personalized shopping assistance service at ubiquitous shop space. In: International Conference on Advanced Information Networking and Applications Workshops, pp. 838–843 (2008)Google Scholar
  10. 10.
    Puerta Melguizo, M.C., Boves, L., Deshpande, A., Ramos, O.M.: A proactive recommendation system for writing: helping without disrupting. In: Proceedings of the 14th European Conference on Cognitive Ergonomics: Invent! Explore!, ECCE ’07, pp. 89–95. ACM, New York (2007)Google Scholar
  11. 11.
    McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the net. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 627–636. ACM, New York (2009)Google Scholar
  12. 12.
    Candillier, L., Jack, K., Fessant, F., Meyer, F.: State-of-the-art recommender systems. In: Chevalier, M., Julien, C., Soule-Dupuy, C. (eds.) Collaborative and Social Information Retrieval and Access-Techniques for Improved User Modeling, pp. 1–22. IGI Global, Hershey (2009)CrossRefGoogle Scholar
  13. 13.
    Boim, R., Milo, T., Novgorodov, S.: Diversification and refinement in collaborative filtering recommender. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 739–744. ACM, New York (2011)Google Scholar
  14. 14.
    Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 145–186. Springer, New York (2011)CrossRefGoogle Scholar
  15. 15.
    Barman, K., Dabeer, O.: Local popularity based collaborative filters. In: 2010 IEEE International Symposium on Information Theory Proceedings (ISIT), pp. 1668–1672. IEEE (2010)Google Scholar
  16. 16.
    Bellogín, A., Cantador, I., Castells, P.: A study of heterogeneity in recommendations for a social music service. In: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, pp. 1–8. ACM (2010)Google Scholar
  17. 17.
    Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)CrossRefGoogle Scholar
  18. 18.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  19. 19.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2010)CrossRefGoogle Scholar
  20. 20.
    Kelleher, J., Bridge, D.: An accurate and scalable collaborative recommender. Artif. Intell. Rev. 21(3–4), 193–213 (2004)CrossRefzbMATHGoogle Scholar
  21. 21.
    Castellanos, A., Cigarrán, J., García-Serrano, A.: Content-based Recommendation: Experimentation and Evaluation in a Case Study. Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2013) (2013)Google Scholar
  22. 22.
    Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: State of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, New York (2011)CrossRefGoogle Scholar
  23. 23.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  24. 24.
    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)CrossRefGoogle Scholar
  25. 25.
    Kim, H.-N., Ha, I., Lee, K.-S., Jo, G.-S., El-Saddik, A.: Collaborative user modeling for enhanced content filtering in recommender systems. Decis. Support Syst. 51(4), 772–781 (2011)CrossRefGoogle Scholar
  26. 26.
    Lucas, J.P., Luz, N., Moreno, M.N., Anacleto, R., Figueiredo, A.A., Martins, C.: A hybrid recommendation approach for a tourism system. Expert Syst. Appl. 40(9), 3532–3550 (2012)CrossRefGoogle Scholar
  27. 27.
    Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  28. 28.
    Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)CrossRefGoogle Scholar
  29. 29.
    Vozalis, M., Margaritis, K.G.: Enhancing collaborative filtering with demographic data: The case of item-based filtering. In: 4th International Conference on Intelligent Systems Design and Applications, pp. 361–366 (2004)Google Scholar
  30. 30.
    Jack, K., Duclayee, F.: Improving explicit preference entry by visualising data similarities. In: Intelligent User Interfaces, International Workshop on Recommendation and Collaboration (ReColl), Spain (2008)Google Scholar
  31. 31.
    Berkovsky, S., Kuflik, T., Ricci, F.: Cross-representation mediation of user models. User Model. User-Adapt. Inter. 19(1–2), 35–63 (2009)CrossRefGoogle Scholar
  32. 32.
    Peis, E., del Castillo, J.M., Delgado-López, J.: Semantic recommender systems. Analysis of the state of the topic. 6, 1–5 (2008)Google Scholar
  33. 33.
    Ghani, R., Fano, A.: Building recommender systems using a knowledge base of product semantics. In: Proceedings of the Workshop on Recommendation and Personalization in ECommerce at the 2nd International Conference on Adaptive Hypermedia and Adaptive Web based Systems, pp. 27–29 (2002)Google Scholar
  34. 34.
    Cantador, I., Castells, P.: Multilayered semantic social network modeling by ontology-based user profiles clustering: Application to collaborative filtering. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, pp. 334–349. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  35. 35.
    Wang, R.-Q., Kong, F.-S.: Semantic-enhanced personalized recommender system. In: 2007 International Conference on Machine Learning and Cybernetics, vol. 7, pp. 4069–4074. IEEE (2007)Google Scholar
  36. 36.
    Mobasher, B.: Contextual user modeling for recommendation. In: Keynote at the 2nd Workshop on Context-Aware Recommender Systems (2010)Google Scholar
  37. 37.
    Said, A., De Luca, E.W., Albayrak, S.: Inferring contextual user profiles-improving recommender performance. In: Proceedings of the 3rd RecSys Workshop on Context-Aware Recommender Systems (2011)Google Scholar
  38. 38.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, New York (2011)CrossRefGoogle Scholar
  39. 39.
    Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive Mob. Comput. 6(2), 161–180 (2010)CrossRefGoogle Scholar
  40. 40.
    Sinha, R.R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries’01, pp. -1–1 (2001)Google Scholar
  41. 41.
    Walter, F., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. Auton. Agent. Multi-Agent Syst. 16(1), 57–74 (2008)CrossRefGoogle Scholar
  42. 42.
    Johnson, H., Johnson, P.: Explanation facilities and interactive systems. In: Proceedings of the 1st International Conference on Intelligent User Interfaces, IUI ’93, pp. 159–166. ACM, New York (1993)Google Scholar
  43. 43.
    Johnson, H., Johnson, P.: Different explanatory dialogue styles and their effects on knowledge acquisition by novices. In: Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences, 1992, vol. 3, pp. 47–57 (1992)Google Scholar
  44. 44.
    Tintarev, N., Masthoff, J.: A survey of explanations in recommender systems. In: Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop, ICDEW ’07, pp. 801–810. IEEE Computer Society, Washington, DC (2007)Google Scholar
  45. 45.
    Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Moviexplain: a recommender system with explanations. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys ’09, pp. 317–320. ACM, New York (2009)Google Scholar
  46. 46.
    Bilgic, M., Mooney, R.J.: Explaining recommendations: Satisfaction vs. promotion. In: Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, January 2005Google Scholar
  47. 47.
    Berkovsky, S., Freyne, J., Oinas-Kukkonen, H.: Influencing individually: Fusing personalization and persuasion. ACM Trans. Interact. Intell. Syst. 2, 9:1–9:8 (2012)CrossRefGoogle Scholar
  48. 48.
    Fogg, B.J.: Persuasive technology: using computers to change what we think and do. Ubiquity 2002 (2002)Google Scholar
  49. 49.
    Torning, K., Oinas-Kukkonen, H.: Persuasive system design: state of the art and future directions. In: Proceedings of the 4th International Conference on Persuasive Technology, Persuasive ’09, pp. 30:1–30:8. ACM, New York (2009)Google Scholar
  50. 50.
    Al-Qaed, F., Sutcliffe, A.: Adaptive decision support system (adss) for b2c e-commerce. In: Proceedings of the 8th International Conference on Electronic Commerce: The New e-commerce: Innovations for Conquering Current Barriers, Obstacles and Limitations to Conducting Successful Business on the Internet, ICEC ’06, pp. 492–503. ACM, New York (2006)Google Scholar
  51. 51.
    Häubl, G., Trifts, V.: Consumer decision making in online shopping environments: The effects of interactive decision aids. Mark. Sci. 19, 4–21 (2000)CrossRefGoogle Scholar
  52. 52.
    Jedetski, J., Adelman, L., Yeo, C.: How web site decision technology affects consumers. IEEE Internet Comput. 6, 72–79 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.DAI-LaborTechnische Universität BerlinBerlinGermany
  2. 2.UNEDMadridSpain
  3. 3.Fachhochschule PotsdamPotsdamGermany

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