Motivation and Goals

Personalized recommendations are integral part of nowadays Web-based applications. The amount of information available to the Web users is tremendous and increasing day by day as a result of the information accessibility and tendency to transform Web users from the passive role of content consumers to the active content producers.

The problem of information overload is studied a and researched from nearly beginning of the ''information revolution''. Personalized recommendations are generally used to overcome some of the problems connected to information overload by reducing irrelevant or recommending relevant information.We are facing up the tremendous social activity over the Web increase. The popularity of social oriented services is increasing continuously. Thanks to these trends the need for group recommendations - recommendations suitable for the group members is increasing.

Today's group recommenders do not or minimally considers group structure, social connections or users personalities in order to generate recommendations. Consideration of such extra information can significantly improve recommendations and increase users' satisfaction as it is clear that similar intergroup processes as in the real life can be observed.

We target at improving recommendation approaches based on identified problems. We propose novel methods for the personalized recommendation for group and single users according the following aims:

• improving group recommendation performance with respect to the user satisfaction, focusing on considering various aspects of recommendation and users and specific domain optimization.
• improving single-user personalized recommendation by enhancing it with group recommendation principles focusing on improving the performance of recommendation for new users and/or specific domains and improving the accuracy of standard recommendation approaches.

Results

In our work we focused on designing and evaluating such approaches of personalized recommendation, which help users in everyday life situation to overcome information overloading problems.

Moreover, we proposed approaches for the single-user recommendation based on the group principles by considering virtual users or users' context influence and the group recommendation itself.

Our results can be summarised as:

• Improvement of the group recommendation in the movie domain by proposing voting-based recommender based for active groups.
• Improvement of the group recommendation by proposing novel influence based recommendation method, considering users' personalities and intergroup relationships.
• Group recommendation improvement by proposing novel group recommendations method for the learning task recommendation with users' learning styles consideration.
• Improvement of the context-aware recommendation by proposing single-user context-aware recommender which, is based on the context to context influence assumption.
• Improvement of the single-user recommendation for new users by proposing novel approach for recom mendation based on the virtual group construction.
• Improvement of the single-user recommendation by proposing novel recommendation approach enhanced by virtual groups construction and aggregation of the group preferences.

Conclusions

The consideration of social aspects influence and personality types during the preference aggregation process (adjusting the ratings) can bring qualitatively better recommendation for individuals and for the group as a whole respectively. In connection to the satisfaction modeling, various real-life scenarios can be modelled. Not only the horizontal influence but also the vertical influence modelling can be benefcial in some domains (e.g., considering teachers preferences within the group).

Selected publications

Kompan, M.,Bieliková, M.

Social Structure and Personality Enhanced Group Recommendation. In Proc of 2nd Workshop on Emotions and Personality in Personalized Services in conjunction with UMAP 2014, Aalborg Denmark.

Kompan, M.,Bieliková, M.

Group Recommendations: Survey and Perspectives. In Computing and Informatics, ISSN 1335-9150, Vol. 33, No. 2, 2014.

Kompan, M.,Bieliková, M.

Context-based Satisfaction Modelling for Personalized Recommendations. In proceedings of the 8th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2013), IEEE, pp. 33-38.

Kompan, M.,Bieliková, M.

Personalized Recommendation for Individual Users Based on the Group Recommendation Principles. In Studies in Informatics and Control, ISSN 1220-1766, Vol. 22, No. 3, 2013, pp. 331-342.

Bieliková, M., Kompan, M., Zeleník, D.

Effective Hierarchical Vector-Based News Representation for Personalized Recommendation. Computer Science and Information Systems Vol. 9, No. 1., ISSN 1820-0214, pp. 303-322.

Kompan, M.,Bieliková, M.

Content-Based News Recommendation.. In BUCCAFURRI, F. -- SEMERARO, G. Vol. 61 E-Commerce and Web Technologies : 11 th International Conference, EC-Web 2010 Bilbao, Spain, September 1-3, 2010 Proceedings. pp. 61-72.

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