Social Relations and Methods in Recommender Systems: A Systematic Review

With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations. Currently, authors consider the user's historical behavior and find contextual information about the user, such as social relationships, time information, and location. In this work, a systematic review of the literature on recommender systems that use the information on social relationships between users was carried out. As the main findings, social relations were classified into three groups: trust, friend activities, and user interactions. Likewise, the collaborative filtering approach was the most used, and with the best results, considering the methods based on memory and model. The most used metrics that we found, and the recommendation methods studied in mobile applications are presented. The information provided by this study can be valuable to increase the precision of the recommendations

SocialDining: Design and Analysis of a Group Recommendation Application in a Mobile Context

Mobile social networks are rapidly becoming an important new domain showcasing the power of mobile computing systems. These networks combine mobile location information with social networking data to enable fully context-aware environments. This paper describes SocialDining, a system that fuses mobile and social data to power novel context-aware recommendation services that provide recommendations to small groups of users who want to meet together for food or drink at local restaurants. We report our analysis on the data collected from 31 users for the SocialDining application over the course of 15 weeks

Facilitating Mobile Music Sharing and Social Interaction with Push!Music

Push!Music is a novel mobile music listening and sharing system, where users automatically receive songs that have autonomously recommended themselves from nearby players depending on similar listening behaviour and music history. Push!Music also enables users to wirelessly send songs between each other as personal recommendations. We conducted a two-week preliminary user study of Push!Music, where a group of five friends used the application in their everyday life. We learned for example that the shared music in Push!Music became a start for social interaction and that received songs in general were highly appreciated and could be looked upon as ‘treats’