Context-Based Information Retrieval is recently modelled as an exploration exploitation trade-off (exr exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to explore the non-top-ranked documents the user may not desire in his her current situation if the risk level is high. We introduce in this paper an algorithm named CBIR-R-greedy that considers the risk level of the user's situation to adaptively balance between exr and exp.
@cite_9: 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.
Handling semantic concepts to express situations and their associated risk level, the first approach compute the risk using concepts. This approach permits to get the risk of the situation directly from the risk of each of its concepts. The second approach compute the risk using the semantic similarity between the current situation and situations stocked in the system as it is done in @cite_9 , and it comes from the assumption that similar situations have the same risk level. The third approach is computing the risk using the variance of the reward. In this case, we assume that risky situations get very low number of user's clicks. (2) We propose an algorithm called CBIR-R-greedy that include the risk computing in its management of the exr exp trade-off. High exploration (resp. high exploitation) is achieved when the current user situation is "not risky" (resp. "risky");