Georgios Theocharous

An Interactive Points of Interest Guidance System

In this paper we propose an intelligent user interface for a Point-of-Interest (POI) recommendation system. Our approach solves many challenges, such as learning from passive data, sequential real-time recommendations, Inferring the users propensity to listen to a recommendation, and minimizing recommendation fatigue. We demonstrate our approach on a real world POI data set from Flicker.

Personalizable and Interactive Sequence Recommender System

Sequence recommender systems assist people in making decisions, such as which product to purchase and what places to visit on vacation. Despite their ubiquity, most sequence recommender systems are black boxes and do not offer justifications for their recommendations or provide user controls for steering the algorithm. In this paper, we design and develop an interactive sequence recommender system (SeRIES) prototype that uses visualizations to explain and justify the recommendations and provides controls so that users may personalize the recommendations. We conducted a user study comparing SeRIES to a black-box system with 12 participants using real visitor trajectory data in Melbourne and show that SeRIES users are more informed about how the recommendations are generated, more confident in following the recommendations, and more engaged in the decision process.

Importance of Recommendation Policy Space in Addressing Click Sparsity in Personalized Advertisement Display

We study a specific case of personalized advertisement recommendation (PAR) problem, which consist of a user visiting a system (website) and the system displaying one of K ads to the user. The system uses an internal ad recommendation policy to map the user’s profile (context) to one of the ads. The user either clicks or ignores the ad and correspondingly, the system updates its recommendation policy. The policy space in large scale PAR systems are generally based on classifiers. A practical problem in PAR is extreme click sparsity, due to very few users actually clicking on ads. We systematically study the drawback of using classifier-based policies, in face of extreme click sparsity. We then suggest an alternate policy, based on rankers, learnt by optimizing the Area Under the Curve (AUC) ranking loss, which can significantly alleviate the problem of click sparsity. We create deterministic and stochastic policy spaces and conduct extensive experiments on public and proprietary datasets to illustrate the improvement in click-through-rate (CTR) obtained by using the ranker-based policy over classifier-based policy.