Towards an automated query modification assistant

Users who need several queries before finding what they need can benefit from an automatic search assistant that provides feedback on their query modification strategies. We present a method to learn from a search log which types of query modifications have and have not been effective in the past. The method analyses query modifications along two dimensions: a traditional term-based dimension and a semantic dimension, for which queries are enriches with linked data entities. Applying the method to the search logs of two search engines, we identify six opportunities for a query modification assistant to improve search: modification strategies that are commonly used, but that often do not lead to satisfactory results.Comment: 1st International Workshop on Usage Analysis and the Web of Data (USEWOD2011) in the 20th International World Wide Web Conference (WWW2011), Hyderabad, India, March 28th, 201

A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion

Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a probabilistic suggestion model that is able to account for sequences of previous queries of arbitrary lengths. Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that it outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our model is general enough to be used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management (CIKM) 201

Corpus-Level End-to-End Exploration for Interactive Systems

A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans. One example is Dynamic Search (DS), which models the process that a human works with a search engine agent to accomplish a complex and goal-oriented task. Early DS agents using Reinforcement Learning (RL) have only achieved limited success for (1) their lack of direct control over which documents to return and (2) the difficulty to recover from wrong search trajectories. In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. In our method, an entire text corpus is compressed into a global low-dimensional representation, which enables the agent to gain access to the full state and action spaces, including the under-explored areas. We also propose a new form of retrieval function, whose linear approximation allows end-to-end manipulation of documents. Experiments on the Text REtrieval Conference (TREC) Dynamic Domain (DD) Track show that CE3 outperforms the state-of-the-art DS systems.Comment: Accepted into AAAI 202

Does user search behaviour mediate user knowledge and search satisfaction?

Information searching in web environment is habitually tedious and challenging task.Rapid growth of web information infrastructure has led to the rapid publication of information on web environment.Too many information publish on web cause information overload problem that preclude the success of information searching.Thus, reduce the search satisfaction.This study has identified that user knowledge lead to user search behaviour and user search behaviour lead to search satisfaction.This paper discusses the investigation empirically through the search log analysis and questionnaire.The respondents were among the students at a local university in Malaysia.The findings support that the user search behaviour has a mediator effect on the relationship between the user knowledge and search satisfaction

Search log analysis method to uncover user search behaviour on web searching environment

User search behaviour was conceptualized as a strategy undertaken by the user in searching for information.Typically, searching activity on the web involved several steps; query formulation and reformulation, browsing the search results, and search results evaluation.The scope of this study has limited itself to query formulation that reflects the user search behaviour.The proposed method has been shown to successfully identify and classify user behaviour into two components namely; breadth search query and depth search query.The queries were initially recorded into search log through search interface.The search interface is one of the innovative tools that interface the Google search engine. Through this interface, user can enter the query and obtain the search results. In addition, the queries are also recorded for further analysis

How does Cognitive Ability impact the use of Query Reformulation Moves?

People have different mental strengths and weakness, which can be measured according to cognitive ability. Learning about strengths and preferences in terms of search behavior, and looking for patterns between behaviors and cognitive abilities, creates the opportunity to make search tools and systems more effectively meet user needs and preferences. While we know that different cognitive abilities exist, and that people form and reform search queries in a variety of ways, we do not know how these two elements interact, or if the interaction is predictable or significant. This paper performs secondary analysis of data collected during a study of cognitive ability, adding in the element of query reformulation moves. It assesses the effect of these cognitive abilities on study participants' search formulation behaviors. Analysis showed that the most common search move was adding a concept to a query, followed by deleting concepts and manipulating search terms. Of the cognitive abilities, the only statistically significant differences between high and low groups were found in the visualization ability. Those in the high skill group made significantly more moves, and significantly more term manipulation moves, than their low skill counterparts.Master of Science in Information Scienc