X. Li, C. Guo, W. Chu, Y. Wang & J. Shavlik (2014).
Deep Learning Powered In-Session Contextual Ranking using Clickthrough Data. Workshop on Personalization: Methods and Applications, at Neural Information Processing Systems (NIPS).
Poster (PDF).

This publication is contained in the following 2 PDF files
File 1, File 2.

Abstract:

User interactions with search engines provide many cues that can be leveraged to improve the relevance of search results through personalization. The context information (history of queries, clicked documents, etc.) provides strong signals about users' search intent, which can be used to personalize the search experience and improve a web search engine. We demonstrate how to generate the semantic features from in-session contextual information with deep learning models, and incorporate these semantic features into the current ranking model to re-rank the results. We evaluate our approach using a large, real-world search log data from a major commercial web search engine, and the experimental results show our approach can significantly improve the performance of the search engine. Furthermore, we also find that the domain-specific, click-based features can effectively decrease the unsatisfied clicks for the current ranking model to improve the search experience.

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