Learning to Attend, Copy, and Generate for Session-Based Query Suggestion

Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more effective, search engines provide related queries to help users in specifying the information need in their search process. In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion. In our model, we employ a query-aware attention mechanism to capture the structure of the session context. is enables us to control the scope of the session from which we infer the suggested next query, which helps not only handle the noisy data but also automatically detect session boundaries. Furthermore, we observe that, based on the user query reformulation behavior, within a single session a large portion of query terms is retained from the previously submitted queries and consists of mostly infrequent or unseen terms that are usually not included in the vocabulary. We therefore empower the decoder of our model to access the source words from the session context during decoding by incorporating a copy mechanism. Moreover, we propose evaluation metrics to assess the quality of the generative models for query suggestion. We conduct an extensive set of experiments and analysis. e results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion.Comment: Accepted to be published at The 26th ACM International Conference on Information and Knowledge Management (CIKM2017

An Extended Relevance Model for Session Search

The session search task aims at best serving the user's information need given her previous search behavior during the session. We propose an extended relevance model that captures the user's dynamic information need in the session. Our relevance modelling approach is directly driven by the user's query reformulation (change) decisions and the estimate of how much the user's search behavior affects such decisions. Overall, we demonstrate that, the proposed approach significantly boosts session search performance

Overview of the personalized and collaborative information retrieval (PIR) track at FIRE-2011

The Personalized and collaborative Information Retrieval (PIR) track at FIRE 2011 was organized with an aim to extend standard information retrieval (IR) ad-hoc test collection design to facilitate research on personalized and collaborative IR by collecting additional meta-information during the topic (query) development process. A controlled query generation process through task-based activities with activity logging was used for each topic developer to construct the final list of topics. The standard ad-hoc collection is thus accompanied by a new set of thematically related topics and the associated log information. We believe this can better simulate a real-world search scenario and encourage mining user information from the logs to improve IR effectiveness. A set of 25 TREC formatted topics and the associated metadata of activity logs were released for the participants to use. In this paper we illustrate the data construction phase in detail and also outline two simple ways of using the additional information from the logs to improve retrieval effectiveness

Cross Validation Of Neural Network Applications For Automatic New Topic Identification

There are recent studies in the literature on automatic topic-shift identification in Web search engine user sessions; however most of this work applied their topic-shift identification algorithms on data logs from a single search engine. The purpose of this study is to provide the cross-validation of an artificial neural network application to automatically identify topic changes in a web search engine user session by using data logs of different search engines for training and testing the neural network. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that it could be possible to identify topic shifts and continuations successfully on a particular search engine user session using neural networks that are trained on a different search engine data log