Multi-Vendor Matrix Factorization with Differential Privacy

Recommender systems usually base their predictions on user-item interaction, a technique known as collaborative filtering. Vendors that utilize collaborative filtering generally exclusively use their own user-item interactions, but the accuracy of the recommendations may improve if several vendors share their data. Since user-item interaction...

Your Turn To Roll: Exploring gaps in group recommendation research

In group recommendation, a key question is how preferences from individuals should be obtained and then aggregated into a group outcome. Collecting individual preferences can be done through implicit or explicit means, but there is insufficient research available on what option is optimal. For comparing different possible aggregation strategies,...

Deep visual genre-aware descriptors for movie recommendation

In the last years, the popularity of video-on-demand services has been constantly increasing, especially for the young audiences who are more adept at using new technologies. Through those platforms, the viewers have access to a huge volume of movies at any moment that makes the viewing decision for most of them a very challenging task....

Modeling User Dynamics for Item Recommendation

Users may show a behavioral pattern in consuming the items. For example, one might assume that a user is interested in comedy movies when this user watches comedy movies frequently. Recommender systems are designed to understand the preference of a user from his interactions with the items and suggest items that correspond to his preference....

Improving Recommender Systems Algorithms for Personalized Music Video Television by Incorporating User Consumption Behaviour and Multiple Types of User Feedback

This thesis explores the effects of incorporating user consumption behavior and multiple types of user feedback to improve recommender systems for personalized music video television. An industrial use case is made possible by the availability of anonymized user interaction data on curation-based personalized music television system provided by...

Towards a Social Web based solution to bootstrap new domains in cross-domain recommendations

Most recommender systems recommend items from a single domain. However, usually users’ preferences span across multiple domains. Cross-domain recommender systems can successfully recommend items in multiple domains when there is knowledge about the user’s preferences for items in at least one of the domains and when there is knowledge about...