A social-evolutionary approach to compose a similarity function used on event recommendation

With the development of web 2.0, social networks have achieved great space on the internet, with that many users provide information and interests about themselves. There are expert systems that use the user´s interests to recommend different products, these systems are known as Recommender Systems. One of the main techniques of a Recommender Systems is the Collaborative Filtering (User based) which recommends products to users based on what other similar people liked in the past. However, the methods to determine similarity between users have presented some problems. Therefore, this work presents a proposal of using social variables in the composition of the similarity function applied to a user on the recommendation of events. To test the proposal, details of friends and events of two target-users of the social network Facebook have been extracted. The results were compared with different deterministic heuristics, the Euclidean Distance and a aleatory method. The proposed model showed promising results and great potential to expand to different contexts.