Toward a Robust Diversity-Based Model to Detect Changes of Context

Being able to automatically and quickly understand the user context during a session is a main issue for recommender systems. As a first step toward achieving that goal, we propose a model that observes in real time the diversity brought by each item relatively to a short sequence of consultations, corresponding to the recent user history. Our model has a complexity in constant time, and is generic since it can apply to any type of items within an online service (e.g. profiles, products, music tracks) and any application domain (e-commerce, social network, music streaming), as long as we have partial item descriptions. The observation of the diversity level over time allows us to detect implicit changes. In the long term, we plan to characterize the context, i.e. to find common features among a contiguous sub-sequence of items between two changes of context determined by our model. This will allow us to make context-aware and privacy-preserving recommendations, to explain them to users. As this is an ongoing research, the first step consists here in studying the robustness of our model while detecting changes of context. In order to do so, we use a music corpus of 100 users and more than 210,000 consultations (number of songs played in the global history). We validate the relevancy of our detections by finding connections between changes of context and events, such as ends of session. Of course, these events are a subset of the possible changes of context, since there might be several contexts within a session. We altered the quality of our corpus in several manners, so as to test the performances of our model when confronted with sparsity and different types of items. The results show that our model is robust and constitutes a promising approach.Comment: 27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2015), Nov 2015, Vietri sul Mare, Ital

Active tag recommendation for interactive entity search : Interaction effectiveness and retrieval performance

We introduce active tag recommendation for interactive entity search, an approach that actively learns to suggest tags from preceding user interactions with the recommended tags. The approach utilizes an online reinforcement learning model and observes user interactions on the recommended tags to reward or penalize the model. Active tag recommendation is implemented as part of a realistic search engine indexing a large collection of movie data. The approach is evaluated in task-based user experiments comparing a complete search system enhanced with active tag recommendation to a control system in which active tag recommendation is not available. In the experiment, participants (N = 45) performed search tasks on the movie domain and the corresponding search interactions, information selections, and entity rankings were logged and analyzed. The results show that active tag recommendation (1) improves the ranking of entities compared to written-query interaction, (2) increases the amount of interaction and effectiveness of interactions to rank entities that end up being selected in a task, and (3) reduces, but does not substitute, the need for written-query interaction (4) without compromising task execution time. The results imply that active learning for search support can help users to interact with entity search systems by reducing the need for writing queries and improve search outcomes without compromising the time used for searching.Peer reviewe