User experience of recommender systems in social media.

This Bachelors thesis was done as a literary review and it analyses recommender systems, social media and user experience by studying the effects of recommender systems to user experience in social media as well as their social aspects. Recommender systems are systems that combine information technology and customer data to ease marketing and sales online by recommending products to the client. In social media the recommended product can be a group, page or person of interest to the client. This thesis presents four ways to produce the recommendation; content-based, collaborative, social network-based and hybrid. Social media refers to a web-based service used to create and maintain social relationships and networks. It typically involves interaction and trade between users. User experience is the whole experience the user forms from the interaction before, during and after using a system. In the user experience of recommender systems the experience is affected by the systems objective aspects, i.e. the system itself, and subjective aspects, i.e. the users’ view of the systems, and can be divided into system-, process- and outcome-related parts. User experience is dynamic and dependent on the bias and state of mind of the user but recommender systems can influence this via their own abilities. This thesis claims that the style of recommendation and the presentation of those recommendations have an effect on the perceived quality and usability of the system for the user. It is pointed out that every style of recommendation has social characteristics and that those characteristics and with them the effects on user experience get more noticeable the more the system moves from content based towards hybrid approach. In the end are highlighted and pointed out potential directions for additional and further research. These include in practice a survey or otherwise carried out research on the user experience of recommender systems and in theory a more detailed research on recommender systems in the context both user experience and social media.