Monday , April 22 2024

Personalized Recommendation for Individual Users Based on the Group Recommendation Principles

Institute of Informatics and Software Engineering,
Faculty of Informatics and Information, Technologies,
Slovak University of Technology in Bratislava
Ilkovicova, Bratislava, 842 16, Slovakia
{kompan, bielik}

Abstract: The amount of information available on the Web is increasing day by day. Users are overloaded and cannot access desired information in a reasonable time. Plenty of approaches for the web personalization, which try to solve information overload have been proposed in the literature. Important feature of any personalization is its accuracy, or more precisely accuracy of personalized recommendation provided to a user. In this paper we propose a new recommendation approach for the single-user collaborative filtering based on the principles of the recommendations for group of users. We explore the best configuration for such an approach according to the group size used for computation, the aggregation strategy of ratings used within groups or the number of similar users used for the recommendation. We did an experiment over the MovieLens and SME.SK news portal datasets. Proposed approach is compared to the standard collaborative and group recommender respectively. The results support our hypothesis that the proposed approach brings statistically significant improvement and it is applicable on various domains, thus can be used for the single-user recommendation tasks.

Keywords: Collaborative filtering, group recommendation, virtual groups, aggregation strategies.

>>Full Text
Michal KOMPAN, Mária BIELIKOVÁ, Personalized Recommendation for Individual Users Based on the Group Recommendation Principles, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (3), pp. 331-342, 2013.


Recommender systems are an integral part of the Web nowadays. The need for the personalized web increases day by day, while people are generally overwhelmed by the amount of information available over the Web. Personalized web enables users to get access to relevant information matching their interests by filtering or recommending interesting items. Personalized recommendation becomes crucial for the business sector, where it can help to increase profits (e.g., recommending interesting products increases the chances of getting a purchase orderalso by increasing users’ visits of the web sites). Personalized recommendation is the most used approach to satisfy both-users and the business sector respectively.

The recommendation task can be defined as:

a10f1                                                                               (1)

where U represents users, S represents the recommendation objects and f is the usefulness function (usefulness of the objects for specific user u).

Several approaches have been proposed for the personalized recommendation. Two principal approaches include content-based and collaborative recommendation. In order to obtain better results these two approaches, are often combined in so-called “hybrid” approaches [5].

The content-based recommendation uses the similarity between recommended items (user liked an article about a new car, thus he/she will probably like a similar article about cars). The similarity between recommended items and a user profile can be computed based on numerous aspects such as a simple text similarity or advanced content analysis is performed. Moreover, various enhancements for specific domains as news have been proposed [11, 24, 25].

The second approach is collaborative recommendation (filtering). This approach to personalized recommendation instead of the content similarity takes advantage of user to user similarity, which is usually computed based on user’s content ratings (e.g., the readerA likes articles about cars and politics, the readerB likes articles about cars, thus the readerB will probably like also articles about politics similarly to the readerA).

Three basic models to collaborative filtering can be distinguished in respect to the focus of the computation process: user-based, model-based and item-based approaches [6]. The user-based approach creates sets of user neighbors (similar users) and then the assumption that similar users like similar items is applied. On the contrary, the item-based approach creates set of similar items, while the rating is computed based on the similar items user ratings. Finally, the model-based approach constructs users sets while the rating is derived based on the other users (within the set) ratings.

Various approaches within the collaborative recommendation have been proposed. The matrix factorization models as SVD, SVD++, PLSA or neural networks are comparable to the state-of-art approaches, while they often offer memory efficient model [13]. Neighborhood based models are used more often, thanks to their simplicity and possibility to easily understand the reason for providing specific recommendations (as this is one of the recommender system important characteristics). Providing explanations for the recommendation is often crucial from the user’s satisfaction point of view.

In order to obtain best results, these approaches are often mixed. For example, in order to create scalable news recommendation Das et al. proposed recommender system based on PLSI and MinHash as one of model-based approaches and item co-visitation as a representative of the user-based approach [8].

From the other point of view we distinguish a single-user andgroup recommenders. In the last years the phenomenon of social networking [18] and mobile devices increase has brought us to the increasing demand for recommendations designed for groups of users [12], because of the group oriented domains increase (e.g., TV, movie, holidays). The possibility of the usage of group recommendation approaches within standard single-user recommendation was raised by Masthoff [14]. In the group recommendation we use not only content or users’ similarity, but inter-group relations are considered (derived explicitly or implicitly [10]) in order to provide recommendation for the whole group instead of single-user.

Most of the proposed group recommendation approaches deal with TV or music domains as these include activities which are usually performed in groups of users [4]. The classic example of such a system is MusicFX [16], which was designed to influence a music played in the gym by actual present users. Ntousi et al. proposed the framework gRecs [19], which uses agglomerative hierarchical clustering in order to compute and predict user’s and thus group’s preferences. Recently the group recommendation was used in new domains as holiday or restaurant recommendation [17, 2].

In the group recommendation members of the group do not include only similar users (groups are formed naturally such as people in the cinema, bus, watching TV etc.), so some kind of aggregation of single-user recommendations or single-user preferences has to be performed in order to produce one list of recommendation for every group member.

As the group recommendation process is highly dependent on the aggregation strategy used, several strategies have been proposed [15]. When the standard plurality voting is used, several users can be highly unsatisfied (the minority is outvoted).

Strategies, when the majority of users take into account (e.g., average, dictatorship) are generally considered as strategies without minimal satisfaction (the minimal level of satisfaction of every group member is not guaranteed).

On the contrary, strategies as least misery or fairness ensure that the minimal satisfaction for every item and user is guaranteed across the group members.

Broadly speaking, while the standard single-user recommendation tries to satisfy actual user needs, the group recommendation based on the used aggregation strategy and the goal of the recommendation tries to maximize satisfaction of every user of the group.

In this paper we propose a novel approach for the collaborative recommendation. We explore the potential of usage group recommendation principles to generate recommendations for a single-user. We believe that recommendations based on group principles can introduce the recommended items variety. Moreover, based on used aggregation strategy, proposed approach can be applied in various settings and domains and thus help us to overcome some standard collaborative recommender shortcomings as the cold-start problem. We investigate the influence of several aspects as the group size or number of users used for the recommendation to the proposed approach effectiveness.

This paper has two primary contributions:

Exploring the usage group recommendation principles in the individual personalized recommendation by proposing new domain independent recommendation approach.

Analysis of aggregation strategies used for the group preference aggregation and number of similar users used for the collaborative filtering.

The paper is organized as follows. Section 2 describes the proposed approach in three basic steps: virtual groups construction, similarity computation and recommendation. In Section 3 we describe experiments focused to reveal performance aspect of each step of proposed approach. We conclude the paper with discussion on method properties based on performed experiments and outline future work directions.

Roadmap validation approach and results are discussed in Section 5. The contribution of the collaborative networks discipline for the provision of integrated care services is discussed in Section 6. Implementation aspects and conclusions complement the paper.


  1. ADOMAVICIUS, G., A. TUZHILIN, Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, 2005, pp.734-749.
  2. ARDISSONO, L., A. GOY, G. PETRONE, P. SEGNAN, P. TORASSO, Intrigue: Personalized Recommendation of Tourist Attractions for Desktop and Hand held Devices, Applied Artificial Intelligence, 2003, pp. 687-714.
  3. BALTRUNAS, L., F. RICCI, Group Recommendations with Rank Aggregation and Collaborative Filtering, In Proceedings of the 4th ACM Conference on Recommender Systems, ACM 2010, pp. 119-126.
  4. BORATTO, L., S. CARTA, 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 of Studies in Computer Intelligence, Springer, 2011, pp. 1-20.
  5. BRUSILOVSKY, P., A. KOBSA, W. NEJDL, The Adaptive Web, In Lecture Notes in Computer Sciences, vol. 4321, Springer, 2007, p. 762.
  6. CANDILLIER, L., F. MEYER, M. BOULLÉ, Comparing State-of-the-Art Collaborative Filtering Systems, In Proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, Springer, 2007, pp. 548-562.
  7. CREMONESI, P., Y. KOREN, R. TURRIN, Performance of Recommender Algorithms on Top-n Recommendation Tasks, In Proceedings of the 4th ACM Conference on Recommender Systems, 2010, pp. 39-46.
  8. DAS, A. S., M. DATAR, A. GARG, S. RAJARAM, Google News Personalization: Scalable Online Collaborative Filtering, In Proceedings of the 16th International Conference on World Wide Web, ACM 2007, pp. 271-280.
  9. GONG, S. J., A Collaborative Recommender Based on User Information and Item Information, In Proceedings of the International Symposium on Information Processes, Academy Publisher, 2009, pp. 1-4.
  10. JUNG, H., S. PARK, Pattern Searching in a Social Network, Studies in Informatics and Control, vol. 19(2), 2010, pp. 125-134.
  11. KOMPAN, M., M. BIELIKOVÁ, Content-Based News Recommendation, In Lecture Notes in Business Information Processing, vol. 61, Springer, 2010, pp. 61-72.
  12. KOMPAN, M., M. BIELIKOVÁ, Group Recommendations: Survey and Perspectives, In Computing and Informatics. 2013. [to appear]
  13. KOREN, Y., Advances in Collaborative Filtering, In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender Systems Handbook, Springer, 2011, pp. 145-186.
  14. MASTHOFF, J., Group Modelling: Selecting a Sequence of Television Items to Suit a Group of Viewers, In Proceedings of the International Conference on User Modelling and User-Adapted Interaction, Springer, 2004, pp. 37-85.
  15. MASTHOFF, J., Group Recommender Systems: Combining Individual Models, In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, Springer, 2011, pp. 677-702.
  16. MCCARTHY, J. F., T. D. ANAGNOST, MUSIC FX: An Arbiter of Group Preferences for Computer Supported Collaborative Workouts, In Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work, ACM, 1998, pp. 363-372.
  17. MCCARTHY, K., M. SALAMO, L. COYLE, L. MCGINTY, B. SMYTH, P. NIXON, Cats: A Synchronous Approach to Collaborative Group Recommendation, In Proceeding of FLAIRS Conf., AAAI Press, 2006, pp. 86-91.
  18. MICHALCO, J., P. NÁVRAT, Arrangement of Face-to-Face Meetings using Social Media, Studies in Informatics and Control, vol. 21(4), 2012, pp. 383-392.
  19. NTOUTSI, I., K. STEFANIDIS, K. NORVAG, H. P. KRIEGEL, gRecs: A Group Recommendation System based on User Clustering, In Proceedings of the 17th International Conference on Database Systems for Advanced Applications, Springer, 2012, pp. 1-4.
  20. RECIO-GARCIA, J. A., G. JIMENEZ-DIAZ, A. A. SANCHEZ-RUIZ, B. DIAZ-AGUDO, Personality Aware Recommendations to Groups, In Proceedings of the 3rd ACM Conference on Recommender Systems, ACM, 2009, pp. 325-328.
  21. SENOT, C., D. KOSTADINOV, M. BOUZID, J. PICAULT, A. AGHASARYAN, C. BERNIER, Analysis of Strategies for Building Group Profiles, User Modelling, Adaptation and Personalization, Springer, 2010, pp. 40-51.
  22. SHANI, G., A. GUNAWARDANA, Evaluating Recommendation Systems, In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, Springer, 2011, pp. 257-297.
  23. SU, X., T. M. KHOSHGOFTAAR, A Survey of Collaborative Filtering Techniques, Advances in Artificial Intelligence, 2009, pages 1-20.
  24. SUCHAL, J., P. NÁVRAT, Full Text Search Engine as Scalable k-nearest Neighbour Recommendation System, In Artificial Intelligence in Theory and Practice III, vol. 331 of IFIP Advances in Information and Communication Technologies, Springer, 2010., pp. 165-173.
  25. ŠALOUN, P., Z. VELART, Evaluation of Concept Space Rating Algorithm and Navigation Scheme, In Proceedings of the International Conference on Web Intelligence and Intelligent Agent Tech., vol. 3, 2010, pp. 288-291.
  26. WANG, Z., M. ZHANG, Y. TAN, W. WANG, Y. ZHANG, L. CHEN, Recommendation Algorithm Based on Graph Model Considering User Background Information, Proceedings of the 9th International Conference on Creating, Connecting and Collaborating through Computing, 2011, pp. 32-39.