Deriving query suggestions for site search

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T

Generating Query Suggestions to Support Task-Based Search

We address the problem of generating query suggestions to support users in completing their underlying tasks (which motivated them to search in the first place). Given an initial query, these query suggestions should provide a coverage of possible subtasks the user might be looking for. We propose a probabilistic modeling framework that obtains keyphrases from multiple sources and generates query suggestions from these keyphrases. Using the test suites of the TREC Tasks track, we evaluate and analyze each component of our model.Comment: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17), 201

Intent Models for Contextualising and Diversifying Query Suggestions

The query suggestion or auto-completion mechanisms help users to type less while interacting with a search engine. A basic approach that ranks suggestions according to their frequency in the query logs is suboptimal. Firstly, many candidate queries with the same prefix can be removed as redundant. Secondly, the suggestions can also be personalised based on the user's context. These two directions to improve the aforementioned mechanisms' quality can be in opposition: while the latter aims to promote suggestions that address search intents that a user is likely to have, the former aims to diversify the suggestions to cover as many intents as possible. We introduce a contextualisation framework that utilises a short-term context using the user's behaviour within the current search session, such as the previous query, the documents examined, and the candidate query suggestions that the user has discarded. This short-term context is used to contextualise and diversify the ranking of query suggestions, by modelling the user's information need as a mixture of intent-specific user models. The evaluation is performed offline on a set of approximately 1.0M test user sessions. Our results suggest that the proposed approach significantly improves query suggestions compared to the baseline approach.Comment: A short version of this paper was presented at CIKM 201

On-device Query Caching For Enhancing Zero-Prefix Query Suggestions

User interfaces (UI) that provide search functionality, e.g., search boxes, virtual assistants, etc. often include mechanisms that provide users with query suggestions within the UI. Query suggestions presented prior to receiving any input from the user are referred to as zero-prefix query suggestions. Zero-prefix query suggestions are typically derived by a ranking algorithm that is based on recently submitted and/or recurrent queries, accessed from a user-permitted server-side query cache. However, resource and operational constraints of a server cache can result in suboptimal zero-prefix query suggestions. This disclosure describes the implementation of a local on-device cache to overcome these limitations and improve the relevance and effectiveness of zero-prefix query suggestions

Diversifying query suggestions based on query documents

Many domain-specific search tasks are initiated by document-length queries, e.g., patent invalidity search aims to find prior art related to a new (query) patent. We call this type of search Query Document Search. In this type of search, the initial query docu-ment is typically long and contains diverse aspects (or sub-topics). Users tend to issue many queries based on the initial document to retrieve relevant documents. To help users in this situation, we propose a method to suggest diverse queries that can cover multi-ple aspects of the query document. We first identify multiple que-ry aspects and then provide diverse query suggestions that are effective for retrieving relevant documents as well being related to more query aspects. In the experiments, we demonstrate that our approach is effective in comparison to previous query suggestion methods

Query Suggestions as Summarization in Exploratory Search

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