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Group Recommender Systems: Aggregation, Satisfaction and Group Attributes

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Recommender Systems Handbook

Abstract

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modeling the user’s affective state . It summarizes results from previous research in these areas. It explores how group attributes can be incorporated in aggregation strategies. Additionally, it shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.

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Notes

  1. 1.

    These strategies are too complicated to fully explain here, see the original papers for details.

  2. 2.

    This does not necessarily mean that these strategies are bad, as complexity can also play a role. In fact, in Experiment 2 Borda count was amongst the best performing strategies.

  3. 3.

    In terms of satisfaction functions predicting the same relative satisfaction scores for group members as predicted by participants, see [28] for details.

  4. 4.

    We transformed a rating r into (r-scale_midpoint)2 if r ≥ scale_midpoint, and -(r-scale_midpoint)2 if r<scale_midpoint.

  5. 5.

    We transformed a rating r by a user u into r × (TotalRatingsAverage ÷ TotalRatings(u)), where TotalRatingsAverage is the sum for all items of the average ratings by all users, and TotalRatings(u) is the sum for all items of u’s rating.

  6. 6.

    In a between-subject design, two different topics were used evoking different moods.

  7. 7.

    For reasons explained in [30], a learning rather than recommender task was used, and satisfaction with performance measured. There was an easy (E), medium (M) and difficult (D) variant of the task, so we could predict accurately how satisfied participants would be with performance on an individual task, and could focus on modeling the effect of sequences on satisfaction. Half the participants did tasks in order E-D-M, the other half in order D-E-M.

  8. 8.

    Additionally, it seems plausible (but requires investigation) that users would be more dissatisfied with disliked selected items when their expertise is higher than that of other group members.

  9. 9.

    This may work well when using an Additive or Multiplicative strategy, but does not really work for the Least Misery and Most Pleasure strategies used in [15], and hence unfortunately some of the formulas in [15] which incorporate expertise lack validity.

  10. 10.

    Personal impact is not completely distinct from Role: somebody’s role may influence their personal impact. However, it is still possible for people with the same official roles to have a different cognitive centrality.

  11. 11.

    We have added the word positional to the terms homogeneous and heterogeneous used in [47] to avoid confusion with the earlier use of these words to indicate how diverse group preferences are.

  12. 12.

    This initially offers the user non-personalized recommendations, however not necessarily by purely using popularity (e.g. Average without Misery can be used and fairness principles can be applied towards the other group members when recommending a sequence).

References

  1. Alfonseca, E., Carro, R.M., Martín, E., Ortigosa, A., Paredes, P.: The Impact of Learning Styles on Student Grouping for Collaborative Learning: A Case Study. UMUAI 16 (2006) 377–401

    Google Scholar 

  2. Ardissono, L., Goy,A., Petrone, G., Segnan, M., Torasso, P.: Tailoring the Recommendation of Tourist Information to Heterogeneous User Groups. In S. Reich, M. Tzagarakis, P. De Bra (eds.), Hypermedia: Openness, Structural Awareness, and Adaptivity, International Workshops OHS-7, SC-3, and AH-3. Lecture Notes in Computer Science 2266, Springer Verlag, Berlin (2002) 280–295

    Google Scholar 

  3. Asch,S.E.:Studies of independence and conformity: a minority of one against a unanimous majority. Pschol. Monogr. 70 (1956) 1–70

    Google Scholar 

  4. Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the fourth ACM conference on Recommender systems (2010) 119–126. ACM.

    Google Scholar 

  5. Beliakov, G., Calvo, T., James, S.: Aggregation functions for recommender systems. In this handbook (2015).

    Google Scholar 

  6. Berkovsky, S., Freyne, J.: Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the fourth ACM conference on Recommender systems 111–118. ACM.

    Google Scholar 

  7. Bermingham, A., O’Rourke, J., Gurrin, C., Collins, R., Irving, K., Smeaton, A. F.: Automatically recommending multimedia content for use in group reminiscence therapy. In: Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare (2013) 49–58. ACM.

    Google Scholar 

  8. Bourke, S., McCarthy, K., Smyth, B: Using social ties in group recommendation. In: Proceedings of the 22nd Irish Conference on Artificial Intelligence and Cognitive Science (2011) University of Ulster-Magee. Intelligent Systems Research Centre.

    Google Scholar 

  9. Carvalho, L. A., Macedo, H. T.: Users’ satisfaction in recommendation systems for groups: an approach based on noncooperative games. In: Proceedings of the 22nd international conference on World Wide Web Companion (2013) 951–958.

    Google Scholar 

  10. de Campos, L.M., Fernandez-Luna, J.M., Huete, J.F., Rueda-Morales, M.A.: Managing uncertainty in group recommending processes. UMUAI 19 (2009) 207–242

    Google Scholar 

  11. De Carolis, B.: Adapting news and advertisements to groups. In: Pervasive Advertising (2011). 227–246. Springer. CITED identification of people in group; GAIN system

    Google Scholar 

  12. de Mello Neto, W. L., Nowé, A.: Insights on social recommender system. In: Proceedings of the Workshop on Recommendation Utility Evaluation: Beyond RMSE, at ACM RecSyS12 (2012) 33–38.

    Google Scholar 

  13. De Pessemier, T., Dooms, S., Martens, L.: Comparison of group recommendation algorithms. Multimedia Tools and Applications, (2013) 1–45.

    Google Scholar 

  14. Felfernig, A., Zehentner, C., Ninaus, G., Grabner, H., Maalej, W., Pagano, D., Reinfrank, F.: Group decision support for requirements negotiation. In: Advances in User Modeling (2012) 105–116. Springer.

    Google Scholar 

  15. Gatrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., Seada, K.: Enhancing group recommendation by incorporating social relationship interactions. In: Proceedings of the 16th ACM international conference on Supporting group work (2010) 97–106. ACM.

    Google Scholar 

  16. Gorla, J., Lathia, N., Robertson, S., Wang, J.: Probabilistic group recommendation via information matching. In: Proceedings of the 22nd international conference on World Wide Web (2013) 495–504.

    Google Scholar 

  17. Guzzi, F., Ricci, F., Burke, R. Interactive multi-party critiquing for group recommendation. In: Proceedings of the fifth ACM conference on Recommender systems (2011) 265–268. ACM.

    Google Scholar 

  18. Harrer, A., McLaren, B.M., Walker, E., Bollen L., Sewall, J.: Creating Cognitive Tutors for Collaborative Learning: Steps Toward Realization. UMUAI 16 (2006) 175–209

    Google Scholar 

  19. Herr, S., Rösch, A., Beckmann, C., Gross, T.: Informing the design of group recommender systems. In: CHI12 Extended Abstracts on Human Factors in Computing Systems (2012) 2507–2512. ACM.

    Google Scholar 

  20. Introne, J., Alterman,R.: Using Shared Representations to Improve Coordination and Intent Inference. UMUAI 16 (2006) 249–280

    Google Scholar 

  21. Ioannidis, S., Muthukrishnan, S., Yan, J. (2013): A consensus-focused group recommender system. arXiv preprint arXiv:1312.7076.

    Google Scholar 

  22. Jameson, A.: More than the Sum of its Members: Challenges for Group Recommender Systems. International Working Conference on Advanced Visual Interfaces, Gallipoli, Italy (2004)

    Book  Google Scholar 

  23. Jameson, A., Smyth, B.: Recommendation to groups. In: Brusilovsky, P., Kobsa, A., Njedl, W. (Eds). The Adaptive Web Methods and Strategies of Web Personalization. Springer (2007) 596–627

    Google Scholar 

  24. Kim, H. N., Bloess, M., El Saddik, A.: Folkommender: a group recommender system based on a graph-based ranking algorithm. Multimedia Systems (2013) 1–17

    Google Scholar 

  25. Kurdyukova, E., Hammer, S., André, E.: Personalization of content on public displays driven by the recognition of group context. In: Ambient Intelligence (2012) 272–287. Springer.

    Google Scholar 

  26. Liu, X., Tian, Y., Ye, M., Lee, W. C.: Exploring personal impact for group recommendation. In: Proceedings of the 21st ACM international conference on Information and knowledge management (2012) 674–683. ACM.

    Google Scholar 

  27. Masthoff, J.: Modeling the multiple people that are me. In: P. Brusilovsky, A.Corbett, and F. de Rosis (eds.) Proceedings of the 2003 User Modeling Conference, Johnstown, PA. Springer Verlag, Berlin (2003) 258–262

    Google Scholar 

  28. Masthoff, J.: Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers. UMUAI 14 (2004) 37–85

    Google Scholar 

  29. Masthoff, J.: Selecting News to Suit a Group of Criteria: An Exploration. 4th Workshop on Personalization in Future TV - Methods, Technologies, Applications for Personalized TV, Eindhoven, the Netherlands (2004)

    Google Scholar 

  30. Masthoff, J., Gatt, A.: In Pursuit of Satisfaction and the Prevention of Embarrassment: Affective state in Group Recommender Systems. UMUAI 16 (2006) 281–319

    Google Scholar 

  31. Masthoff, J.: The user as wizard: A method for early involvement in the design and evaluation of adaptive systems. Fifth Workshop on User-Centred Design and Evaluation of Adaptive Systems (2006)

    Google Scholar 

  32. Masthoff, J., Vasconcelos, W.W., Aitken, C., Correa da Silva, F.S.: Agent-Based Group Modelling for Ambient Intelligence. AISB symposium on Affective Smart Environments, Newcastle, UK (2007)

    Google Scholar 

  33. McCarthy, J., Anagnost, T.: MusicFX: An Arbiter of Group Preferences for Computer Supported Collaborative Workouts. CSCW, Seattle, WA. (1998) 363–372

    Google Scholar 

  34. McCarthy, K., McGinty, L., Smyth, B., Salamo, M.: The needs of the many: A case-based group recommender system. European Conference on Case-Based Reasoning, Springer (2006) 196–210

    Google Scholar 

  35. McGinty, L., Reilly, J. On the evolution of critiquing recommenders. In F. Ricci, L. Rokach, B. Shapira, and P.B. Kantor: Recommender Systems Handbook, First Edition, Springer (2011) 73–105

    Google Scholar 

  36. O’ Conner, M., Cosley, D., Konstan, J.A., Riedl, J.: PolyLens: A Recommender System for Groups of Users. ECSCW, Bonn, Germany (2001) 199–218. As accessed on http://www.cs.umn.edu/Research/GroupLens/poly-camera-final.pdf

  37. Pera, M. S., Ng, Y. K.: A group recommender for movies based on content similarity and popularity. Information Processing & Management. (2012)

    Google Scholar 

  38. Piliponyte, A., Ricci, F., Koschwitz, J. Sequential music recommendations for groups by balancing user satisfaction. In: Proceedings of the Workshop on Group Recommender Systems: Concepts, Technology, Evaluation at UMAP13.(2013) 6–11.

    Google Scholar 

  39. Quijano-Sanchez, L., Recio-Garcia, J. A., Diaz-Agudo, B., Jimenez-Diaz, G.: Social factors in group recommender systems. ACM Transactions on Intelligent Systems and Technology, 4(1) 8 (2013).

    Article  Google Scholar 

  40. Read, T., Barros, B., Bárcena, E., Pancorbo, J.: Coalescing Individual and Collaborative Learning to Model User Linguistic Competences. UMUAI 16 (2006) 349–376

    Google Scholar 

  41. Salamó, M., McCarthy, K., Smyth, B.: Generating recommendations for consensus negotiation in group personalization services. Personal and Ubiquitous Computing, 16(5) (2012) 597–610.

    Google Scholar 

  42. Senot, C., Kostadinov, D., Bouzid, M., Picault, J., Aghasaryan, A., Bernier, C.: Analysis of strategies for building group profiles. In: Proceedings of User Modeling, Adaptation, and Personalization (2010) 40–51. Springer.

    Google Scholar 

  43. Stettinger, M., Ninaus, G., Jeran, M., Reinfrank, F., Reiterer, S.: WE-DECIDE: A decision support environment for groups of users. In: Recent Trends in Applied Artificial Intelligence (2013) 382–391. Springer.

    Google Scholar 

  44. Suebnukarn, S., Haddawy, P.: Modeling Individual and Collaborative Problem-Solving in Medical Problem-Based Learning. UMUAI 16 (2006) 211–248

    Google Scholar 

  45. Tintarev, N., Masthoff, J.: Explaining recommendations: Design and evaluation. In this handbook (2015).

    Google Scholar 

  46. Umyarov, A., Tuzhilin, A.: Using external aggregate ratings for improving individual recommendations. ACM Transactions on the Web 5 (2011) 1–45

    Article  Google Scholar 

  47. Wang, Z., Zhou, X., Yu, Z., Wang, H., Ni, H.: Quantitative evaluation of group user experience in smart spaces. Cybernetics and Systems: An International Journal 41:2 (2010) 105–122

    Article  MATH  Google Scholar 

  48. Ye, M., Liu, X., Lee, W. C.: Exploring social influence for recommendation: a generative model approach. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (2012) ACM: 671–680

    Google Scholar 

  49. Yu, Z., Zhou, X., Hao, Y. Gu, J.: TV Program Recommendation for Multiple Viewers Based on User Profile Merging. UMUAI 16 (2006) 63–82

    Google Scholar 

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Acknowledgement

Judith Masthoff’s research has been partly supported by Nuffield Foundation Grant No. NAL/00258/G.

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  1. University of Aberdeen, AB24 3UE, Aberdeen, UK

    Judith Masthoff

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  1. Judith Masthoff
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Correspondence to Judith Masthoff .

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  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Masthoff, J. (2015). Group Recommender Systems: Aggregation, Satisfaction and Group Attributes. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_22

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  • DOI: https://doi.org/10.1007/978-1-4899-7637-6_22

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