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A Survey for Recommender System for Groups

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Information and Communication Technology and Applications (ICTA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1350))

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Abstract

Recommendation system has been seen to be very concentrated on individual recommendations but few of the new techniques are now concentrated on groups. The aim of this paper is to provide an overview of the existing state of the art techniques for collecting ratings, strategies used in aggregating these strategies and the practical application for group recommendations. This study explored five databases which include IEEE, Science Direct, Springer, ACM and Google Scholar, from which 300 publications were screened. Irrelevant, duplicate and ambiguous papers were removed. At the end, 26 papers were used for depth analysis. This study provides a systematic review of the available evidence based literature concerning recommender systems for groups.

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References

  1. Jameson, A. More than the sum of its members: challenges for group recommender systems

    Google Scholar 

  2. Lieberman, H., Vandyke, N. & Vivacqua, A. Let’s browse: a collaborative Web browsing agent

    Google Scholar 

  3. Mccarthy, J. Pocket restaurantfinder: A situated recommender system for groups

    Google Scholar 

  4. Pizzutilo, S., Decarolis, B., Cozzolongo, G. & Ambruoso, F. Group modeling in a public space: methods, techniques, experiences

    Google Scholar 

  5. Chao, D., Balthrop, J. & Forrest, S. Adaptive radio: achieving consensus using negative preferences

    Google Scholar 

  6. Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Applied Artificial Intelligence. 17, 687–714 (2003)

    Article  Google Scholar 

  7. Jameson, A. & Smyth, B. Recommendation to groups. (Springe 2007

    Google Scholar 

  8. Pazzani, M. & Billsus, D. Content-based recommendation systems. (Springe 2007

    Google Scholar 

  9. Schafer, J., Frankowski, D., Herlocker, J. & Sen, S. Collaborative filtering recommender systems. (Springe 2007

    Google Scholar 

  10. Masthoff, J. Group modeling: Selecting a sequence of television items to suit a group of viewers. (Springe 2004

    Google Scholar 

  11. Masthoff, J. Group recommender systems: aggregation, satisfaction and group attributes. (Springe 2015

    Google Scholar 

  12. Mccarthy, K., Mcginty, L., Smyth, B. & Salamó, M. The needs of the many: a case-based group recommender system

    Google Scholar 

  13. Mccarthy, J. & Anagnost, T. MusicFX: an arbiter of group preferences for computer supported collaborative workouts

    Google Scholar 

  14. Yu, Z., Zhou, X., Hao, Y., Gu, J.: TV program recommendation for multiple viewers based on user profile merging. User Modeling And User-adapted Interaction. 16, 63–82 (2006)

    Article  Google Scholar 

  15. Goren-bar, D., Glinansky, O.: FIT-recommend ing TV programs to family members. Computers & Graphics. 28, 149–156 (2004)

    Article  Google Scholar 

  16. Crossen, A. & Budzik, J. Promoting social interaction in public spaces: The flytrap active environment. (Springe 2006

    Google Scholar 

  17. Borda, J. amie Royale des Sciences, Paris. Cook Wd (2006) Distance-based And Ad Hoc Consensus Models In Ordinal Preference Ranking. Eur. J. Oper. Res. 172 pp. 369–385 (178)

    Google Scholar 

  18. Copeland, A.H. A reasonable social welfare function. (University of Michigan, 1951)

    Google Scholar 

  19. Andreadis, P. Coarse preferences: representation, elicitation, and decision making. (The University of Edinburg, 2019)

    Google Scholar 

  20. Kuhlman, C., Doherty, D., Nurbekova, M., Deva, G., Phyo, Z., Schoenhagen, P., Vanvalkenburg, M., Rundensteiner, E. & Harrison, L. Evaluating Preference Collection Methods for Interactive Ranking Analytics

    Google Scholar 

  21. Chen, Li, Chen, Guanliang, Wang, Feng: Recommender systems based on user reviews: the state of the art. User Modeling and User-Adapted Interaction 25(2), 99–154 (2015). https://doi.org/10.1007/s11257-015-9155-5

    Article  Google Scholar 

  22. Jindal, N. & Liu, B. Mining comparative sentences and relations

    Google Scholar 

  23. Sigurbjörnsson, B. & Vanzwol, R. Flickr tag recommendation based on collective knowledge

    Google Scholar 

  24. Zhang, S., Tay, Y., Yao, L., Sun, A. & An, J. Next item recommendation with self-attentive metric learning

    Google Scholar 

  25. Trang Tran, Thi Ngoc, Atas, Müslüm, Felfernig, Alexander, Stettinger, Martin: An overview of recommender systems in the healthy food domain. Journal of Intelligent Information Systems 50(3), 501–526 (2017). https://doi.org/10.1007/s10844-017-0469-0

    Article  Google Scholar 

  26. Colomo-Palacios, R., García-Peñalvo, F.J., Stantchev, V., Misra, S.: Towards a social and context-aware mobile recommendation system for tourism. Pervasive and Mobile Computing. 38, 505–515 (2017)

    Article  Google Scholar 

  27. Colomo-Palacios, R., Casado-Lumbreras, C., Soto-Acosta, P., Misra, S.: Providing knowledge recommendations: an approach for informal electronic mentoring. Interactive Learning Environments. 22(2), 221–240 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Technical University of Munich, Munich, Germany

    Ananya Misra

Authors
  1. Ananya Misra
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Correspondence to Ananya Misra .

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Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. Federal University of Technology Minna, Minna, Nigeria

    Bilkisu Muhammad-Bello

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Misra, A. (2021). A Survey for Recommender System for Groups. In: Misra, S., Muhammad-Bello, B. (eds) Information and Communication Technology and Applications. ICTA 2020. Communications in Computer and Information Science, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-69143-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-69143-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69142-4

  • Online ISBN: 978-3-030-69143-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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