A Survey on Query Suggestion

In this paper we propose a query suggestion method for price comparison search engines. Query suggestion techniques are used for generating alternative queries to facilitate web users in information seeking; in this specific domain, suggestions provided to web users need to be properly generated taking into account that the suggested products must be still available for sale. We propose a novel approach based on a slightly variant of classical query-URL graphs: the query-product click-through bipartite graph. Information extracted both from search engine logs and specific domain features are exploited to build the graph, and one of the advantages of this model is that such a graph can be used to suggest not only related queries but also related products. Concepts used in the proposed method are not restricted to our context but are used in many other major e-commerce and search engine websites, we tested the model on several challenging datasets, and also compared with a recent query suggestion approach specifically designed for price comparison engines. Our solution outperforms the competing approach, achieving higher results in terms of relevance of the provided suggestions and coverage rates on top-8 suggestions.

Query suggestion or auto-completion mechanisms are widely used by search engines and are increasingly attracting interest from the research community. However, the lack of commonly accepted evaluation methodology and metrics means that it is not possible to compare results and approaches from the literature. Moreover, often the metrics used to evaluate query suggestions tend to be an adaptation from other domains without a proper justification. Hence, it is not necessarily clear if the improvements reported in the literature would result in an actual improvement in the users' experience. Inspired by the cascade user models and state-of-the-art evaluation metrics in the web search domain, we address the query suggestion evaluation, by first studying the users behaviour from a search engine's query log and thereby deriving a new family of user models describing the users interaction with a query suggestion mechanism. Next, assuming a query log-based evaluation approach, we propose two new metrics to evaluate query suggestions, pSaved and eSaved. Both metrics are parameterised by a user model. pSaved is defined as the probability of using the query suggestions while submitting a query. eSaved equates to the expected relative amount of effort (keypresses) a user can avoid due to the deployed query suggestion mechanism. Finally, we experiment with both metrics using four user model instantiations as well as metrics previously used in the literature on a dataset of 6.1M sessions. Our results demonstrate that pSaved and eSaved show the best alignment with the users satisfaction amongst the considered metrics.

Search has become a ubiquitous, everyday activity, but finding the right information at the right time in an electronic document collection can still be a very challenging process. Significant time is being spent on identifying suitable search terms, exploring matching documents, rephrasing the search request and assessing whether a document contains the information sought. Once another user is faced with a similar information need, the whole process starts again. There is significant potential in cutting down on this activity by taking a user straight to the required information. As well as delivering technical information and vital regulatory information, a knowledge management solution is concerned with capturing valuable insight and experience in order to share it amongst workers. A search engine has been developed and deployed to technical support staff and we were able to assess its impact on mobile workers. The architecture is based on open-source software to satisfy the basic search functionality, such as indexing, search result ranking, faceting and spell checking. The search engine indexes a number of knowledge repositories relevant to the field engineers. On top of that we have developed an adaptive query suggestion mechanism called Sunny Aberdeen. Query suggestions provide an interactive feature that can guide the user through the search process by providing alternative terminology or suggesting ‘best matches’. In our search engine, the query suggestions are generated and adapted over time using state-of-the-art machine learning approaches, which exploit past user interactions with the search engine to derive query suggestions. Apart from continuously updating the suggestions, this framework is also capable of reflecting current search trends as well as forgetting relations that are no longer relevant. Query log analysis of the system running in a real-life context indicates that the system was able to cut down the number of repeat faults and speeds up the decision process for sending out staff to certain jobs.

Chapter 20 - Query Suggestion with Large Scale Data

With the exponential growth of information on the internet, users have been relying on search engines for finding the precise documents. However, user queries are often short. The inherent ambiguity of short queries imposes great challenges for search engines to understand user intent. Query suggestion is one key technique for search engines to augment user queries so that they can better understand user intent. In the past, query suggestions have been relying on either term-frequency--based methods with little semantic understanding of the query, or word-embedding--based methods with little personalization efforts. Here, we present a sequence-to-sequence-model--based query suggestion framework that is capable of modeling structured, personalized features and unstructured query texts naturally. This capability opens up the opportunity to better understand query semantics and user intent at the same time. As the largest professional network, LinkedIn has the advantage of utilizing a rich amount of accurate member profile information to personalize query suggestions. We applied this framework in the LinkedIn production traffic and showed that personalized query suggestions significantly improved member search experience as measured by key business metrics at LinkedIn.

Query suggestions help users of a search engine to refine their queries. Previous work on query suggestion has mainly focused on incorporating directly observable features such as query co-occurrence and semantic similarity. The structure of such features is often set manually, as a result of which hidden dependencies between queries and users may be ignored. We propose an AHNQS model that combines a hierarchical structure with a session-level neural network and a user-level neural network to model the short- and long-term search history of a user. An attention mechanism is used to capture user preferences. We quantify the improvements of AHNQS over state-of-the-art RNN-based query suggestion baselines on the AOL query log dataset, with improvements of up to 21.86% and 22.99% in terms of MRR@10 and Recall@10, respectively, over the state-of-the-art; improvements are especially large for short sessions.

Search engine users usually strive to reformulate their queries in the search process to gain useful information. It is hard for search engines to understand users’ search intents and return appropriate results if they submit improper or ambiguous queries. Therefore, query reformulation is a bottleneck issue in the usability of search engines. Modern search engines normally provide users with some query suggestions for references. To help users to better learn their information needs, it is of vital importance to investigate users’ reformulation behaviors thoroughly. In this paper, we conduct a detailed investigation of users’ session-level reformulation behavior on a large-scale session dataset and discover some interesting findings that previous work may not notice before: (1) Intent ambiguity may be the direct cause of long sessions rather than the complexity of users’ information needs; (2) Both the added and the deleted terms in a reformulation step can be influenced by the clicked results to a greater extent than the skipped ones; (3) Users’ specification actions are more likely to be inspired by the result snippets or the landing pages, while the generalization behaviors are impacted largely by the result titles. We further discuss some concerns about the existing query suggestion task and give some suggestions on the potential research questions for future work. We hope that this work could provide assistance for the researchers who are interested in the relative domain.

PurposeThe purpose of this study is to generate diversified results for temporally ambiguous queries and the candidate queries are ensured to have a high coverage of subtopics, which are derived from different temporal periods.Design/methodology/approachTwo novel time-aware query suggestion diversification models are developed by integrating semantics and temporality information involved in queries into two state-of-the-art explicit diversification algorithms (i.e. IA-select and xQuaD), respectively, and then specifying the components on which these two models rely on. Most importantly, first explored is how to explicitly determine query subtopics for each unique query from the query log or clicked documents and then modeling the subtopics into query suggestion diversification. The discussion on how to mine temporal intent behind a query from query log is also followed. Finally, to verify the effectiveness of the proposal, experiments on a real-world query log are conducted.FindingsPreliminary experiments demonstrate that the proposed method can significantly outperform the existing state-of-the-art methods in terms of producing the candidate query suggestion for temporally ambiguous queries.Originality/valueThis study reports the first attempt to generate query suggestions indicating diverse interested time points to the temporally ambiguous (input) queries. The research will be useful in enhancing users’ search experience through helping them to formulate accurate queries for their search tasks. In addition, the approaches investigated in the paper are general enough to be used in many domains; that is, experimental information retrieval systems, Web search engines, document archives and digital libraries.

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.

Major Web Search Engines take as a common practice to provide Suggestions to users in order to enhance their search experience. Such suggestions have normally the form of queries that are, to some extent, similar to the queries already submitted by the same or related users. The final aim of query suggestions is typically to help users to satisfy their information needs more quickly. In this paper we face this problem from a somewhat different perspective, and we propose a new query suggestion model based on Search Shortcuts, that consist in finding and proposing to the user Successful queries that allowed, in the past, several users to satisfy their information needs. This model differs from traditional query suggestion approaches, and allows the evaluation to be performed effectively by exploiting actual user sessions from the Microsoft 2006 RFP dataset. We evaluate several algorithms applied to this problem, both traditional Collaborative Filtering techniques and ad-hoc solutions, and report on preliminary results achieved.

Traditional query suggestion methods mainly leverage click-through information to find related queries as recommendations, without considering the semantic relateness between queries. In addition, few studies use click-through distribution in diversifying query suggestions. To address these issues, we propose a novel and effective framework to generate relevant and diversified query suggestions. We combine query semantics and click-through information together to generate query suggestion candidates which are highly relevant to original query , we use click-through distribution to diversify the candidates. We evaluate our method on a large-scale search log dataset of a commercial engine, experimental results indicate that our framework has significantly improved the relevance and diversity of suggested queries by comparing to four baseline methods.

This paper proposes a query suggestion method combining two ranked retrieval methods: TF-IDF and Jaccard coefficient. Four performance criteria plus user evaluation have been adopted to evaluate this combined method in terms of ranking and relevance from different perspectives. Two experiments have been conducted using carefully designed eighty test queries which are related to eight topics. One experiment aims to evaluate the quality of the query suggestions generated by the proposed method, and the other aims to evaluate the improvement of the relevance of retuned documents in interactive web search by using thequery suggestions so as to evaluate the effectiveness of the developed method. The experimental results show that the method developed in this paper is the best method for query suggestion among the methods evaluated, significantly outperforming the most popularly used TF-IDF method. In addition, the query suggestions generated by the proposed method significantly improve the relevance of returned documents in interactive web search in terms of increasing the precision or the number of highly relevant documents.

We conducted a user study to analyze how health literacy, topic familiarity and the terminology used in past queries affect query behavior in health searches. We found that users with inadequate health literacy have less success in web searches and show more difficulties in query formulation. These users and the ones not familiar with the topic use medico-scientific terminology less often than users with more health literacy and topic familiarity. We conclude that search engines should help these groups of users in query formulation and, since technical documents stimulate the use of medico-scientific terminology in query reformulation, mechanisms like query suggestion can have long-term benefits. © 2013 Springer-Verlag.

We analyze in this paper the design architecture of a web-based personalized query suggestion system usable in information retrieval contexts. Our system comes in the form of a transparent proxy for the Google search engine (although it can work with other search engines too, with minimal code changes) that is able to personalize the list of query suggestions provided by Google, based on the recent user web browsing history. The most important design choices are presented with a special focus on the integration with the Google search engine. The integration analysis is directed by the well known application integration criteria. We have made our system transparent to the user when searching information on Google and we have shown through performance tests that our system has very small computational costs for the user experience.

Question-Answering (QA) system is improved version of search engines or can be stated as specific Search engines because search engines deliver data in the form of URLs whereas Question-Answering system produces direct answer to the query asked. That's why more focus has been put on developing better Question Answering systems that could respond to large variety of user query. Semantic Web Technology has aided to the development of such systems from past many years because it could add meaning to the query being asked. Such systems are more efficient than the systems developed in other languages. In this paper, we have proposed a QA system of JAVA Programming Language, where knowledge will be represented in the form of Ontology and based on relations derived from domain knowledge to obtained final answer. Information present in Ontology is represented in the form of tables to feed in knowledge base. It combines the techniques of Natural Language Processing and Semantic Web.