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Quijano-Sanchez, Lara; Sauer, Christian; Recio Garcia, Juan Antonio; Diaz-Agudo, Belen (2017)
Publisher: Elsevier
Languages: English
Types: Article
Subjects: Innovation-and-user-experience, Software-engineering, Intelligent-systems
Recommender systems help users to identify which items from a variety of choices best match their needs and preferences. In this context, explanations act as complementary information that can help users to better comprehend the system’s output and to encourage goals such as trust, confidence in decision-making or utility. In this paper we propose a Personalized Social Individual Explanation approach (PSIE). Unlike other expert systems the PSIE proposal novelly includes explanations about the system’s group recommendation and explanations about the group’s social reality with the goal of inducing a positive reaction that leads to a better perception of the received group recommendations. Among other challenges, we uncover a special need to focus on “tactful” explanations when addressing users’ personal relationships within a group and to focus on personalized reassuring explanations that encourage users to accept the presented recommendations. Besides, the resulting intelligent system significatively increases users’ intent (likelihood) to follow the recommendations, users’ satisfaction and the system’s efficiency and trustworthiness.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • commender systems. Journal of AdvRanced Management Science Al-Taie, M. Z., Kadry, S., 2014. Visualization of explanations in re2 (2), 140-144.
    • C Amer-Yahia, S., Roy, S. B., Chawlat, A., Das, G., Yu, C., 2009. Group recommendation: semantics and efficiency. Proc. VLDB Endow.
    • Baltrunas, L., Makcinskas, ST.,Ricci, F., 2010. Group recommenda2 (1), 754-765.
    • tions with rank aggregation and collaborative filtering. In: InternaU tional Conference on Recommender Systems, RecSys '10. ACM, pp. 119-126.
    • tions: anNalysis of data aggregation strategies. In: International Berkovsky, S., Freyne, J., 2010. Group-based recipe recommendaConference on Recommender Systems, RecSys '10. pp. 111-118.
    • faActionvs. promotion. In: Beyond Personalization, the Workshop Bilgic, M., Mooney, R. J., 2005. Explaining recommendations: Satison the Next Stage of Recommender Systems Research, IUI '05.
    • pp. 13-18.
    • MBoratto, L., Carta, S., 2011. State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Information Retrieval and Mining in Distributed Environments. Vol.
    • 324. pp. 1-20.
    • Cantador, I., Castells, P., 2012. Group recommender systems: New perspectives in the social web. In: Recommender Systems for the Social Web. pp. 139-157.
    • Chazara, P., Negny, S., Montastruc, L., 2016. Flexible knowledge representation and new similarity measure: Application on case based reasoning for waste treatment. Expert Syst. Appl. 58, 143-154.
    • Christakis, N. A., Fowler, J. H., 2009. Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives.
    • Cialdini, R. B., Trost, M. R., 1998. Social influence: Social norms, conformity and compliance. In: The handbook of social psychology, 2-Volume Set. pp. 151-192.
    • Cosley, D., Lam, S. K., Albert, I., Konstan, J. A., Riedl, J., 2003.
    • Is seeing believing?: how recommender system interfaces affect users' opinions. In: Conference on Human Factors in Computing Systems, CHI '03. pp. 585-592.
    • da Silva, E. Q., Camilo-Junior, C. G., Pascoal, L. M. L., Rosa, T. C., 2016. An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering. Expert Syst. Appl. 53, 204-218.
    • Diez, D., Barr, C., Cetinkaya-Rundel, M., 2013. OpenIntro Statistics: Second Edition. CreateSpace Independent Publishing Platform.
    • Fogg, B. J., Marshall, J., Laraki, O., Osipovich, A., Varma, C., Fang, N., Paul, J., Rangnekar, A., Shon, J., Swani, P., Treinen, M., 2001.
    • What makes web sites credible?: a report on a large quantitative study. In: Conference on Human Factors in Computing Systems, CHI' 01. pp. 61-68.
    • Forcher, B., Roth-Berghofer, T., Agne, S., Dengel, A., 2014. Intuitive justifications of medical semantic search results. Engineering Applications of Artificial Intelligence.
    • Garcia, I., Sebastia, L., Onaindia, E., 2011. On the design of individual and group recommender systems for tourism. Expert Syst.
    • Appl. 38 (6), 7683-7692.
    • Gedikli, F., Jannach, D., Ge, M., 2014. How should i explain? a comparison of different explanation types for recommender systems.
    • Int. J. Hum.-Comput. Stud. 72 (4), 367-382.
    • Golbeck, J., 2006. Generating predictive movie recommendations from trust in social networks. In: International Conference on Trust Management, iTrust '06. pp. 93-104.
    • Groh, G., Birnkammerer, S., Ko¨llhofer, V., 2012. Social recommender systems. In: Recommender Systems for the Social Web. pp. 3-42.
    • Guy, I., Zwerdling, N., Carmel, D., Ronen, I., Uziel, E., Yogev, S., Ofek-Koifman, S., 2009. Personalized recommendation of social software items based on social relations. In: International Conference on Recommender Systems, RecSys '09. pp. 53-60.
    • Herlocker, J. L., Konstan, J. A., Riedl, J., 2000. Explaining collaborative filtering recommendations. In: ACM Conference on Computer Supported Cooperative Work, CSCW'00. pp. 241-250.
    • Hingston, M., Kay, J., 2006. User friendly recommender systems.
    • Jamali, M., Ester, M., 2009. Using a trust network to improve top-n recommendation. In: International Conference on Recommender Systems, RecSys '09. pp. 181-188.
    • Jameson, A., 2004. More than the sum of its members: challenges for group recommender systems. In: Conference on Advanced visual interfaces, AVI'04. ACM, pp. 48-54.
    • Jameson, A., Smyth, B., 2007. Recommendation to groups. In: The Adaptive Web, Methods and Strategies of Web Personalization.
    • Vol. 4321 of Lecture Notes in Computer Science. pp. 596-627.
    • Jannach, D., Zanker, M., Felfernig, A., Friedrich, G., 2011. Recommender Systems An Introduction. Cambridge University Press.
    • Knijnenburg, B. P., Bostandjiev, S., O'Donovan, J., Kobsa, A., 2012.
    • Inspectability and control in social recommenders. In: International Conference on Recommender Systems, RecSys '12. pp. 43- 50.
    • Lamche, B., Adigu¨zel, U., Wo¨rndl, W., 2014. Interactive explanations Workshop on Interfaces and Human Decision Making for DRecomin mobile shopping recommender systems. In: RecSys'14 Joint Lopez-Suarez, A., Kamel, M. S., 1994. Dykor: a metEhodfor genermender Systems (IntRS14).
    • Based Syst. 7 (3), 177-188. T ating the content of explanations in knowledge systems. Knowl.- Massa, P., Avesani, P., 2007. Trust-aware recommender systems. In: International Conference on RecommePnderSystems, RecSys '07.
    • pp. 17-24.
    • Masthoff, J., 2011. Group recommender systems: Combining individ677-702.
    • Masthoff, J., Gatt, A., 2006. CInpursuit of satisfaction and the prevention of embarrassment: affective state in group recommender 281-319. C systems. User Modeling and User-Adapted Interaction 16 (3-4), McCarthy, J. F., 2002. Pocket restaurant finder: A situated recombile Ad-Hoc ACommunication at the ACM Conference on Human mender systems for groups. In: Proceeding of Workshop on MoFactors in Computer Systems, HFCS '02.
    • Mccarthy, K., Mcginty, L., Smyth, B., Salamo´, M., 2006. Social interaction in the cats group recommender. In: Workshop on the Social Navigation and Community-Based Adaptation Technologies In Conjunction with Adaptive Hypermedia and Adaptive WebBased Systems AH'06.
    • Mccarthy, K., Reilly, J., Mcginty, L., Smyth, B., 2004. Thinking positively - explanatory feedback for conversational recommender systems. Tech. rep., European Cased Based Reasoning Workshops, ECCBR '04.
    • McCarthy, K., Salamo´, M., Coyle, L., McGinty, L., Smyth, B., Nixon, P., 2006. Group recommender systems: a critiquing based approach. In: International Conference on Intelligent User Interfaces, IUI'06. pp. 267-269.
    • Nguyen, H., Masthoff, J., 2008. Using digital images to enhance the credibility of information. In: in Persuasive Technology symposium in association with the Society for the Study of AISB. pp.
    • O'Connor, M., Cosley, D., Konstan, J. A., Riedl, J., 2001. Polylens: a on Computer Supported Cooperative Work, ECSCW '01. Tpp. 199- recommender system for groups of users. In: European Conference 218.
    • Park, D. H., Kim, H. K., Choi, I. Y., Kim, J. K., P2012. A literature Syst. Appl. 39 (11), 10059-10072. I review and classification of recommender systems research. Expert recommendation algorithms. In: MuRltimedia Tools an ApplicaPessemier, T. D., Dooms, S., Martens, L., 2013. Comparison of group tions.
    • commender systems. Knowl.-BCasedSyst. 20 (6), 542-556.
    • Pu, P., Chen, L., 2007. Trust-inspiring explanation interfaces for revelopment of a group rSecommender application in a social netQuijano-Sa´nchez, L., D´ıaz-Agudo, B., Recio-Garc ´ıa, J. A., 2014. Dework. Knowl.-Based Syst. 71, 72-85.
    • D´ıaz, G., 2013. USocial factors in group recommender systems.
    • Quijano-Sa´nchez, L., Recio-Garc´ıa, J. A., D ´ıaz-Agudo, B., Jim e´nezACM TIST 4 (1), 8.
    • N Ricci, F., Rokach, L., Shapira, B. (Eds.), 2015. Recommender Systems Handbook. Springer.
    • Salamo´, M., McCarthy, K., Smyth, B., 2012. Generating recommenA dations for consensus negotiation in group personalization services. Personal and Ubiquitous Computing 16 (5), 597-610.
    • Salehi-Abari, A., Boutilier, C., 2015. Preference-oriented social netMConference on Recommender Systems, RecSys '15. pp. 35-42.
    • works: Group recommendation and inference. In: International Sharma, A., Cosley, D., 2013. Do social explanations work?: studying and modeling the effects of social explanations in recommender systems. In: International World Wide Web Conference, WWW '13. pp. 1133-1144.
    • Symeonidis, P., Nanopoulos, A., Manolopoulos, Y., 2008. Providing justifications in recommender systems. IEEE Transactions on Systems, Man, and Cybernetics, Part A 38 (6), 1-1272.
    • Symeonidis, P., Nanopoulos, A., Manolopoulos, Y., 2009. Moviexplain: a recommender system with explanations. In: International Conference on Recommender Systems, RecSys '09. pp. 317-320.
    • Thomas, K., Kilmann, R., 1974. Thomas-Kilmann Conflict Mode Instrument. Tuxedo, N.Y.
    • Tintarev, N., 2007. Explanations of recommendations. In: International Conference on Recommender Systems, RecSys '07. pp.
    • Tintarev, N., Masthoff, J., 2008. Personalizing movie explanations using comercial meta-data. In: Adaptive Hypermedia. pp. 204-213.
    • Tintarev, N., Masthoff, J., 2012. Evaluating the effectiveness of explanations for recommender systems - methodological issues and empirical studies on the impact of personalization. User Model.
    • User-Adapt. Interact. 22 (4-5), 399-439.
    • Vig, J., Sen, S., Riedl, J., 2009. Tagsplanations: explaining recommendations using tags. In: International Conference on Intelligent User Interfaces, IUI'09. pp. 47-56.
    • Zanker, M., Ninaus, D., 2010. Knowledgeable explanations for recommender systems. In: International Conference on Web Intelligence, WI' 10. pp. 657-660.
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