| dc.description.abstract | Recommender systems have emerged as essential tools for enhancing user engagement and content discovery in various domains, including the sports industry. In the context of sports viewing, personalized recommendations have become increasingly significant, enabling users to easily connect with their favorite sports teams, explore new content, and broaden their viewing preferences. Collaborative filtering (CF) stands out as a popular recommendation algorithm that analyzes the similarities and patterns in user-item interactions. By examining the behavior and preferences of a group of users, CF identifies similar users and recommends items that have been positively received by those with similar tastes. Applying CF to sports recommendations presents an opportunity to introduce users to new sports events enjoyed by their peers. However, recommending upcoming live sports events introduces unique challenges, such as limited availability and the need to strike a balance between catering to users' favorite sports and introducing them to new content. This master thesis aims to address these challenges through the development of a personalized recommendation system for upcoming sports events using CF. The system will analyze user viewing history to provide tailored recommendations that facilitate content discovery and enable users to easily locate their preferred sports events. The research objectives include identifying the most suitable collaborative filtering model for sports content recommendation, investigating the factors that influence sports fans' preferences for specific types of live sports events, and evaluating the effectiveness of personalized recommendations compared to non-personalized approaches. The proposed system is implemented and A/B tested on TV 2 Play, one of Norway's largest digital streaming platforms, with the ultimate goal of enhancing user experience and engagement by delivering personalized and relevant recommendations for sports content. This research contributes to the field by proposing a novel collaborative filtering recommender for sports based on user viewing sessions, exploring effective strategies for recommending upcoming live sports events, and assessing the system's performance in terms of accuracy and user satisfaction. | |
