Reinforcement Learning Using Negative Relevance Feedback

In the task of information filtering, the profile of the user´s interest and preference is the key to the performance of the system. In traditional method, the profile usually represented as a set of features in the vector space model, but this kind of profile could not be satisfied by the user for lack of the negative information. This paper proposes on approach to construct the user´s profile based on negative relevance feedback and Reinforcement Learning(RL) This approach uses not only positive feedback  but also negative feedback, and suggests relevance feedback method with reinforcement learning. The proposed method improves the performance of filtering system by eliminating the documents that users don´t prefer. We carried filtering about four topics so as to compare the proposed method to Rocchio and Widrow-Hoff(WH), representative relevance feedback methods. The experimental result shows that the performance of the case, where only positive relevance feedback is used, is 3 to 6% better than that of Rocchio and 2 to 3% better than that of Widrow-Hoff. And the performance of the case, where both positive and negative relevance feedback are used, is 6 to 10% better than that of Rocchio and 2 to 8% better than that of Widrow-Hoff.