A personalized ranking method based on inverse reinforcement learning in search engines

This paper proposes a new, novel ranking method called Inverse-Reinforcement Learning Ranking. The main goal is to find a reward function representing the user's perceived utility after clicking on each result. It is necessary to utilize log information of all users' queries in the search engine dataset to reach this goal while assuming that the decisions (clicks) of the users are the best (optimal policy). The respective reward function is constructed using features extracted through a feature selection, and their corresponding weights are obtained by two optimization models, which are applicable for ranking results represented to the new users. In addition, new performance criteria were developed to illustrate the performance of the presented ranking method. To evaluate and test the proposed ranking algorithm, a real medium-sized dataset from a search engine is preprocessed and used in this research. Findings show promising results and decisive superiority over the default ranking method. It is illustrated that clicks on the top five results, top ten, and even the first results are remarkably improved by about 13-19% in all experiments, and the perplexity remarkably decreases by almost 23% after applying the ranking method.