Разработка рекомендательной системы на основе данных из профиля социальной сети «ВКонтакте»
There have been implemented engineering and development of multi-agent recommender system «EZSurf» that performs analysis of interests and provides recommendations for the social network «VKontakte» users based on the data from profile of particular user. During the work process different methods and technological solutions have been analyzed with examination of their advantages and disadvantages. Besides of that the comparative analysis of analogous products has been held where the most similar is Russian start-up service - Surfingbird. Based on this analysis the decision of recommender system implementation and integration has been accepted. The feature of this system is that it uses social network “VKontakte” profile for user’s data collection and API of third-party services (LastFM, TheMovieDB) for an extraction of information about similar objects. Such an approach contributes into optimization of recommender system, because it does not require creation of its own object classification system and objects database. The functionality of multi-agent system was separated between three agents. First agent (Collector) collects user data from “VKontakte” profile using VK API. Second agent (Analyzer) collects similar objects from databases of thitd-party services (LastFM, TheMovieDB) that will be the criteria for further search of recommendatory content. For search and selection of information an agent (Recommender) that works as web-crawler has been implemented. System «EZSurf» can be exploited by the users of social network “VKontakte” in everyday life for time economy on web-surfing process. At the same time they will get recommendations on content that are filtered depending on preferences of every particular user.