13 déc
13/12/2018 14:00

Sciences & Société

Soutenance de thèse : Diana NURBAKOVA

Recommendation of Activity Sequences during Distributed Events

Doctorante : Diana NURBAKOVA

Laboratoire INSA : LIRIS
Ecole doctorale : EDA512 : InfoMaths

Multi-day events such as conventions, festivals, cruise trips, to which we refer to as distributed events, have become very popular in recent years, attracting hundreds or thousands of participants. Their programs are usually very dense, making it challenging for the attendees to make a decision which events to join. Recommender systems appear as a common solution in such an environment. While many existing solutions deal with personalised recommendation of single items, recent research focuses on the recommendation of consecutive items that exploits user's behavioural patterns and relations between entities, and handles geographical and temporal constraints.
In this thesis, we first formulate the problem of recommendation of activity sequences, classify and discuss the types of influence that have an impact on the estimation of the user's interest in items.
Second, we propose an approach (ANASTASIA) to solve this problem, which aims at providing an integrated support for users to create a personalised itinerary of activities. ANASTASIA brings together three components, namely: (1) estimation of the user’s interest in single items, (2) use of sequential influence on activity performance, and (3) building of an itinerary that takes into account spatio-temporal constraints. Thus, the proposed solution makes use of the methods based on sequence learning and discrete optimisation.
Moreover, stating the lack of publicly available datasets that could be used for the evaluation of event and itinerary recommendation algorithms, we have created two datasets, namely: (1) event attendance on board of a cruise (Fantasy_db) based on a conducted user study, and (2) event attendance at a major comic book convention (DEvIR). This allows to perform evaluation of recommendation methods, and contributes to the reproducibility of results.