"I would like to watch something like 'The Terminator'..." Cooperative Query Personalization Based on Perceptual Similarity

In this paper, we showcase a privacy-preserving query personalization system for experience items like movies, music, games, or books. Personalizing queries for such items is notoriously difficult as meaningful query attributes are either missing in the database or would require extensive domain knowledge not available to most users. For this reason, state-of-the-art content provision platforms as e.g., Netflix or Amazon usually rely on recommender systems to support their users, and are often working in parallel with traditional SQL-style queries. Unfortunately, recommender systems have several shortcomings as for example high barriers for new users joining the system, which first have to setup a preference profile in a lengthy process, the inability to pose meaningful queries beyond recommendations matching the personal profile, and severe privacy concerns due to storing personal rating data for all users long-term. In order to provide an alternative, we present in this demonstration paper a powerful and intuitive query-by-example (QBE) interaction system. Bayesian Navigation is used to personalize a user’s query on the fly. The central challenge when using QBE is the selection of features to represent the items in the database. Here, we rely on a high-dimensional feature space which was mined from rating data of a large number of users, allowing us to measure perceived similarity between items to steer the query process. This also addresses many issues of recommender systems as our query capabilities can be used by any user anonymously in a drive-by fashion. In our proposed demo, users can try our never before presented system hands-on, and can use it to discover interesting movies tailored to their preferences with a pleasantly simple and enjoyable user experience.