GRSPOID: um sistema de recomendação de pontos de interesse para grupos com diversificação

Abstract

In recent years the availability of data on the Web has been driven by the increasing use of social networks and smartphone applications. In addition to textual content, geo-location information is also shared, favoring the emergence of numerous location-based services. The location information of a Point of Interest (POI) can be used to understand the profile of users, their interests, and movements. In this way, it is possible to identify, for example, places of interest for a particular user, and even classify them into categories, such as cafes, universities, bars, malls, etc. This type of data is widely used for Points of Interest Recommender Systems, which aim to assist users in the search for places of interest, whether on a daily basis or during a trip. These systems are traditionally recommended for individuals, however, there are scenarios where individuals gather in groups, thus increasing the complexity of the problem. In addition to the need to find individual preferences, the recommendation must consider the preferences of the group as a whole, which requires the application of a consensus technique. Another obstacle is that non-diversified recommendations tend to always be in the same category, decreasing the group's interest in recommendations from already known Points of Interest. This master's thesis proposes a Points of Interest Recommendation System for Groups using diversity. To evaluate the proposed model, an exhaustive experiment was carried out with 19 groups, some with 3 and others with 5 members. To evaluate the diversified recommendations, precision metrics were used in positions 3, 5, and 10. According to the results, the recommendations for positions 5 and 10 obtained more satisfactory results when diversity was applied. After the experiment with real users, an offline analysis was also performed with variations of the proposed model and aggregation techniques. According to the results obtained, it was possible to verify that the recommendation models with diversity obtained better results than the non-diversified approach in most of the tested configurations.