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2 Annals of Computer Science and Information Systems

2 2022

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Evaluating diversification in group recommender systems

100%

Oliveira A. C. d. , Durao F. A.

Annals of Computer Science and Information Systems

2022

tom Vol. 32

47--54

The formation of groups is an ordinary event in ourroutines. For example, people used to lunch, travel, or hang out in groups. Conversely, getting a consensus over an item may be difficult for some groups as the number of digital information increases. Group Recommender Systems (GRS) rise to assist in this task, as they filter which items may be more relevant to the group. Although there are consensus techniques to help in this matter, recommendations to groups can become monotonous, and this opens space for applying diversification techniques to improve recommendations. In this paper, we expose a model for recommendation to groups using diversification techniques and present the results of the online experiment where the proposal obtained an increase in precision at all levels compared with baseline.

Exploiting social capital for recommendation in social networks

80%

Souza P. R. d. , Durao F. A. , Almeida J. P. D. d.

Annals of Computer Science and Information Systems

2022

tom Vol. 32

55--62

Many computational techniques have been proposed by social networks to analyze the users' behaviors to recommend relevant content for them. Social networks generate a huge volume of information, which users cannot consume, generating a problem known as information overload. This way, filtering relevant information to help users with this problem becomes necessary. Social networks have many available features, such as relationships and interactions, which can be used to investigate the users' behaviors regarding news on their feed. The value of news can be defined as Social Capital, which is used by this work to model the user's preferences. This paper aims to investigate, model, and quantify interactions on social networks by exploiting social capital to develop a recommender system. Hence, in order to evaluate recommendations, an experiment was conducted with real users. Results show that our proposal was able to generate relevant recommendations on at least 62\% of the scenarios.