Abstract

People nowadays find it difficult to identify the best places to spend their leisure time performing different activities. Some services have been created to give a complete list of the opportunities offered by the city in which they live, but they overload people with information and make it difficult for them to identify what is more interesting. Personalized recommendations partially solve this problem of overload, but they need a deeper understanding of the personal tastes of people and of the different ways in which people want to spend their leisure time. In this thesis we identify the requirements for a recommender system for leisure activities, study which data are needed and which algorithm better identifies the most interesting options for each requester. We explore the effects of data quality on recommendations, identifying which kind of information is needed to better understand user needs and who can provide better-quality opinions. We analyse the possibility of using crowdsourcing as a means for collecting ratings when volunteering is not providing the needed amount of ratings or when a new dataset of ratings is needed to answer some interesting research questions. Finally, we show how the lessons learned can be applied in practice, presenting a prototype of personalized restaurant recommender service.