Contextual bandits for context-based information retrieval

Djallel Bouneffouf (1, 2, 3) , Amel Bouzeghoub (1, 2, 3) , Alda Lopes Gancarski (1, 2, 3)

1 TSP - INF - Département Informatique
2 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
3 CNRS - Centre National de la Recherche Scientifique

Recently, researchers have started to model interactions between users and search engines as an online learning ranking. Such systems obtain feedback only on the few top-ranked documents results. To obtain feedbacks on other documents, the system has to explore the non-top-ranked documents that could lead to a better solution. However, the system also needs to ensure that the quality of result lists is high by exploiting what is already known. Clearly, this results in an exploration/exploitation dilemma. We introduce in this paper an algorithm that tackles this dilemma in Context-Based Information Retrieval (CBIR) area. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user's situation is most relevant for exploration or exploitation. Within a deliberately designed online framework we conduct evaluations with mobile users. The experimental results demonstrate that our algorithm outperforms surveyed algorithms

Mots clés: Information retrieval; Machine learning; Exploration/exploitation dilemma; Artificial intelligence; Reinforcement learning

Domaines: Génie logiciel [cs.SE]

Djallel Bouneffouf, Amel Bouzeghoub, Alda Lopes Gancarski. Contextual bandits for context-based information retrieval. ICONIP 2013 : 20th International Conference on Neural Information Processing, Nov 2013, Daegu, South Korea. pp.35 - 42, ⟨10.1007/978-3-642-42042-9_5⟩. ⟨hal-01258059⟩