ECIR 2016 Tutorial


Group Recommender Systems: State of the Art, Emerging Aspects and Techniques, and Research Challenges

Ludovico Boratto (University of Cagliari, Italy) –


Abstract. A recommender system aims at suggesting to users items that might interest them and that they have not considered yet. A class of systems, known as group recommendation, provides suggestions in contexts in which more than one person is involved in the recommendation process. The goal of this tutorial is to provide the ECIR audience with an overview on group recommendation. We will first illustrate the recommender systems principles, then formally introduce the problem of producing recommendations to groups, and present a survey based on the tasks performed by these systems. We will also analyze challenging topics like their evaluation, and present emerging aspects and techniques in this area. The tutorial will end with a summary that highlights open issues and research challenges.

Tutorial Outline

Recommender systems  are designed to provide information items that are expected to interest a user [11]. Given their capability to increase the revenue in commercial environments, nowadays they are employed by the most relevant websites, such as Amazon and Netflix.

Group recommender systems are a class of systems designed for contexts in which more than one person is involved in the recommendation process [7]. Group recommendation has been highlighted as a challenging research area, with the first survey on the topic [7] being placed in the Challenges section of the widely-known book “The Adaptive Web”, and recent research indicating it as a future direction in recommender systems, since it presents numerous open issues and challenges [10].

With respect to classic recommendation, a system that works with groups has to complete a set of specific and additional tasks. This tutorial will present how the state-of-the-art approaches in the literature handle these tasks in order to produce recommendations to groups.

The evaluation of the accuracy of a system for a group is not a trivial aspect [10], so we will also analyze the evaluation techniques for group recommender systems.

Recent studies are characterized by advanced recommendation techniques and novel aspects, such as social data and temporal features, so the tutorial will also cover these emerging aspects and techniques.

In conclusion, the open issues and research challenges in this area will be presented.

This tutorial will cover these topics in six sections. In detail, the outline of the tutorial is the following.

  1. Recommender systems principles
    • Definition and application domains
    • Main classes of systems
  2. Group recommendation introduction
    • Definition and application domains
    • Problem statement
  3. Tasks and state of the art survey
    • Types of groups. In [2], we highlighted that the type of group handled by a system is one of the characterizing aspects of a group recommender system and we provided a classification of the different types of groups, which was also adopted by Carvalho et al. in their WWW’13 paper [4].
    • Preference acquisition. A group recommender system might acquire the preferences by considering only those expressed by the individual users, or by allowing the groups to express them.
    • Group modeling [9] is the process adopted to combine the individual preferences in a unique model that represents the group.
    • Rating prediction is the most characterizing aspect in all the types of recommender systems, and also plays an important role when working with groups, since the ratings might be predicted for the individual users or specifically for the groups [7].
    • Help the members to achieve consensus. This task is adopted in order to find an agreement on what should be proposed to the group.
    • Explanation of the recommendations, i.e., the task performed by some of the systems to justify why an item has been suggested to the group.
  4. Evaluation methods
    • Offline methods, which evaluate a system on existing datasets.
    • User surveys that test the effectiveness of a system by asking users to answer questionnaires.
    • Live systems that work in real-world domains, like the social networks.
  5. Emerging aspects and techniques
    • Advanced recommendation techniques applied to group recommendation. The last advances in recommendation techniques, such as generative and stochastic models, have recently been employed in group recommender systems too [8, 12].
    • Temporal aspects in group recommendation. Recently, the temporal factor has been considered in the recommendation process [1, 5].
    • Social group recommender systems. The widespread relevance of social media has recently had an impact also on this research area [6, 8].
    • Group recommendation with automatic detection of groups. There are scenarios in which groups do not exist, but the recommendations have to be proposed to groups because of limitations on the number of recommendation lists that can be produced (i.e., it is not possible to suggest a different list of items to each user), so a clustering of the users specifically designed for recommendation purposes has to be performed [3].
  6. Summary
    • Open issues
    • Research challenges

Target Audience

This tutorial is aimed at anyone interested in the topic of producing recommendation to groups of users, from data mining and machine learning researchers to practitioners from industry. For those not familiar with group recommendation or recommender systems in general, the tutorial will cover the necessary background material to understand these systems and will provide a state-of-the-art survey on the topic. Additionally, the tutorial aims to offer a new perspective that will be valuable and interesting even for more experienced researchers that work in this domain, by providing the recent advances in this area and by illustrating the current research challenges.


Ludovico Boratto is a research assistant at the University of Cagliari (Italy). His main research area are Recommender Systems, with special focus on those that work with groups of users and in social environments. In 2010 and 2014, he spent 10 months at the Yahoo! Lab in Barcelona as a visiting researcher.


  1. Amer-Yahia, S., Omidvar-Tehrani, B., Basu Roy, S., Shabib, N.: Group recommendation with temporal affinities. In: Proc. 18th International Conference on Extending Database Technology (EDBT). pp. 421–432. (2015)
  2. Boratto, L., Carta, S.: State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Information Retrieval and Mining in Distributed Environments, Studies in Computational Intelligence, vol. 324, pp. 1–20. Springer Berlin Heidelberg (2011)
  3. Boratto, L., Carta, S.: Using collaborative filtering to overcome the curse of dimensionality when clustering users in a group recommender system. In: ICEIS 2014 – Proceedings of the 16th International Conference on Enterprise Information Systems, Volume 2. pp. 564–572. SciTePress (2014)
  4. Carvalho, L.A.M., Macedo, H.T.: Generation of coalition structures to provide proper groups’ formation in group recommender systems. In: Proceedings of the 22Nd International Conference on World Wide Web Companion. pp. 945–950. International World Wide Web Conferences Steering Committee (2013)
  5. Chen, J., Liu, Y., Li, D.: Dynamic group recommendation with modified collaborative filtering and temporal factor. The International Arab Journal of Information Technology (IAJIT) (2014)
  6. Christensen, I.A., Schiaffino, S.: Social influence in group recommender systems. Online Information Review 38(4), 524–542 (2014)
  7. Jameson, A., Smyth, B.: Recommendation to groups. In: The Adaptive Web, Methods and Strategies of Web Personalization, Lecture Notes in Computer Science, vol. 4321, pp. 596–627. Springer, Berlin (2007)
  8. Kim, H.N., Saddik, A.E.: A stochastic approach to group recommendations in social media systems. Information Systems 50(0), 76 – 93 (2015)
  9. Masthoff, J.: Group recommender systems: Combining individual models. In: Recommender Systems Handbook, pp. 677–702. Springer (2011)
  10. Ricci, F.: Recommender systems: Models and techniques. In: Encyclopedia of Social Network Analysis and Mining, pp. 1511–1522. Springer New York (2014)
  11. Ricci, F., Rokach, L., Shapira, B.: Recommender systems: Introduction and challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 1–34. Springer US (2015), 978-1-4899-7637-6_1
  12. Yuan, Q., Cong, G., Lin, C.Y.: Com: A generative model for group recommendation. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 163–172. KDD ’14, ACM (2014)

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