Providing A Topic-Based LSTM Model to Re-Rank Search Results.

The ranking of search results is directly affected by user click preferences and is an effective way to improve the quality of the result of search engines. To tailor the ranking, it is necessary to use the submitted query information by the user, such as click history, user profile, the previous queries history, or query click entropy. There are ranking methods that explore the issue of underlying information using traditional machine learning algorithms. Recently LSTM (Long Short-Term Memory) based models are investigated in this field. As traditional ML models require considering short-term and long-term preferences in predicting, the LSTM based models can improve prediction efficiency by considering both short and long-term. This paper proposes a topic-based LSTM model to re-rank search results on a submitted input quey using the previous queries sequence and user click history. In this model, we use the topic distribution of user documents to the LSTM model. We compare the model with topic-based ranking models with data from an AOL search engine and Session TREC 2013,2014 to show its performance. The result reveals significant improvement in the Topic-based LSTM model using topics in the Mean Reciprocal Rank by 13