Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Being able to automatically and quickly under-<lb>stand the user context during a session is a main issue for<lb>recommender systems. As a first step toward achieving that<lb>goal, we propose a model that observes in real time the<lb>diversity brought by each item relatively to a short sequence<lb>of consultations, corresponding to the recent user history. Our<lb>model has a complexity in constant time, and is generic since<lb>it can apply to any type of items within an online service (e.g.<lb>profiles, products, music tracks) and any application domain (e-<lb>commerce, social network, music streaming), as long as we have<lb>partial item descriptions. The observation of the diversity level<lb>over time allows us to detect implicit changes. In the long term,<lb>we plan to characterize the context, i.e. to find common features<lb>among a contiguous sub-sequence of items between two changes<lb>of context determined by our model. This will allow us to<lb>make context-aware and privacy-preserving recommendations,<lb>to explain them to users. As this is an on-going research, the<lb>first step consists here in studying the robustness of our model<lb>while detecting changes of context. In order to do so, we use a<lb>music corpus of 100 users and more than 210,000 consultations<lb>(number of songs played in the global history). We validate<lb>the relevancy of our detections by finding connections between<lb>changes of context and events, such as ends of session. Of<lb>course, these events are a subset of the possible changes of<lb>context, since there might be several contexts within a session.<lb>We altered the quality of our corpus in several manners, so as<lb>to test the performances of our model when confronted with<lb>sparsity and different types of items. The results show that our<lb>model is robust and constitutes a promising approach. Keywords-User Modeling; Diversity; Context; Real-Time<lb>Analysis of Navigation Path; Recommender Systems

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