RDQS: A Relevant and Diverse Query Suggestion Generation Framework

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.

Search systems in online content platforms are typically biased toward a minority of highly consumed items, reflecting the most common user behavior of navigating toward content that is already familiar and popular. Query suggestions are a powerful tool to support query formulation and to encourage exploratory search and content discovery. However, classic approaches for query suggestions typically rely either on semantic similarity, which lacks diversity and does not reflect user searching behavior, or on a collaborative similarity measure mined from search logs, which suffers from data sparsity and is biased by highly popular queries. In this work, we argue that the task of query suggestion can be modelled as a link prediction task on a heterogeneous graph including queries and documents, enabling Graph Learning methods to effectively generate query suggestions encompassing both semantic and collaborative information. We perform an offline evaluation on an internal Spotify dataset of search logs and on two public datasets, showing that node2vec leads to an accurate and diversified set of results, especially on the large scale real-world data. We then describe the implementation in an instant search scenario and discuss a set of additional challenges tied to the specific production environment. Finally, we report the results of a large scale A/B test involving millions of users and prove that node2vec query suggestions lead to an increase in online metrics such as coverage (+1.42% shown search results pages with suggestions) and engagement (+1.21% clicks), with a specifically notable boost in the number of clicks on exploratory search queries (+9.37%).

Query formulation is one of the most difficult and important aspects of information seeking and retrieval. Two techniques, term relevance feedback and query suggestion, provide methods to help users formulate queries, but each is limited in different ways. In this research we combine these two techniques by automatically creating query suggestions using term relevance feedback techniques. To evaluate our approach, we conducted an interactive information retrieval study with 55 subjects and 20 topics. Each subject completed four topics, half with a term suggestion system and half with a query suggestion system. We also investigated the source of the suggestions: approximately half of all subjects were provided with system-generated suggestions, while half were provided with user-generated suggestions. Results show that subjects used more query suggestions than term suggestions and saved more documents with these suggestions, even though there were no significant differences in performance. Subjects preferred the query suggestion system and rated it higher along a number of dimensions including its ability to help them think of new approaches to searching. Qualitative data provided insight into subjects' usage and ratings, and indicated that subjects often used the suggestions even when they did not click on them.

Personalized query suggestion based on user behavior

Query suggestion attracts great concern recently. It is crucial for capturing frequently asked questions in question-answering system and most popular topics in search engine. Besides these, is also used in advertising retrieval systems, e-commerce system for advertising push to get more profits. The paper gives a general review of query suggestion methods. On the whole, all the methods can be grouped into two categories: session based methods and click-through based methods. Adjacency based query suggestion, co-occurrence based query suggestion, query-flow graph based query suggestion, clustering based query suggestion, and bipartite graph based query suggestion are presented respectively in detail. Furthermore, how to evaluate the performance of query suggestion is denoted. Finally the important related issues of the area in further research are discussed.

Chapter 20 - Query Suggestion with Large Scale Data

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.

The degree to which users' make their search intent explicit can be assumed to represent an upper bound on the level of service that search engines can provide. In a departure from traditional query expansion mechanisms, we introduce Intentional Query Suggestion as a novel idea that is attempting to make users' intent more explicit during search. In this paper, we present a prototypical algorithm for Intentional Query Suggestion and we discuss corresponding data from comparative experiments with traditional query suggestion mechanisms. Our preliminary results indicate that intentional query suggestions 1) diversify search result sets (i.e. it reduces result set overlap) and 2) have the potential to yield higher click-through rates than traditional query suggestions.

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.

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.

This paper proposes a query auto-completion interface that displays additional information on query suggestions and their search results along with the original query suggestions. Users of conventional query auto-completion interfaces cannot determine whether they can obtain relevant information from the search results of suggested queries until they actually issue them and check their search results. To address this problem, we propose a query auto-completion interface that incorporates useful information, such as words in the search results and images of the suggested queries, into the original query suggestions.

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.

Users often issue all kinds of queries to look for the same target due to the intrinsic ambiguity and flexibility of natural languages. Some previous work clusters queries based on co-clicks; however, the intents of queries in one cluster are not that similar but roughly related. It is desirable to conduct automatic mining of queries with equivalent intents from a large scale search logs. In this paper, we take account of similarities between query strings. There are two issues associated with such similarities: it is too costly to compare any pair of queries in large scale search logs, and two queries with a similar formulation, such as "SVN" (Apache Subversion) and support vector machine (SVM), are not necessarily similar in their intents. To address these issues, we propose using the similarities of query strings above the co-click based clustering results. Our method improves precision over the co-click based clustering method (lifting precision from 0.37 to 0.62), and outperforms a commercial search engine's query alteration (lifting $$F_1$$F1 measure from 0.42 to 0.56). As an application, we consider web document retrieval. We aggregate similar queries' click-throughs with the query's click-throughs and evaluate them on a large scale dataset. Experimental results indicate that our proposed method significantly outperforms the baseline method of using a query's own click-throughs in all metrics.

In addition to search queries and the corresponding clickthrough information, search engine logs record multidimensional information about user search activities, such as search time, location, vertical, and search device. Multidimensional mining of search logs can provide novel insights and useful knowledge for both search engine users and developers. In this paper, we describe our topic-concept cube project, which addresses the business need of supporting multidimensional mining of search logs effectively and efficiently. We answer two challenges. First, search queries and click-through data are well recognized sparse, and thus have to be aggregated properly for effective analysis. Second, there is often a gap between the topic hierarchies in multidimensional aggregate analysis and queries in search logs. To address those challenges, we develop a novel topic-concept model that learns a hierarchy of concepts and topics automatically from search logs. Enabled by the topicconcept model, we construct a topic-concept cube that supports online multidimensional mining of search log data. A distinct feature of our approach is that, in addition to the standard dimensions such as time and location, our topic-concept cube has a dimension of topics and concepts, which substantially facilitates the analysis of log data. To handle a huge amount of log data, we develop distributed algorithms for learning model parameters efficiently. We also devise approaches to computing a topic-concept cube. We report an empirical study verifying the effectiveness and efficiency of our approach on a real data set of 1.96 billion queries and 2.73 billion clicks.

The ambiguity of query may have potential impact on the performance of Query Suggestion. For getting better candidates adapting to query’s ambiguity, we propose an efficient log-based Query Suggestion method. Firstly we construct a Query-URL graph from logs and calculate the bidirectional transition probabilities between queries and URLs. Then, by taking URL’s rank and order into consideration, we make a strength metric of the Query-URL edge. Besides, we conduct random walk with the edge strength and transition probability to measure the closeness among queries. To reflect the influence of query ambiguity, we exploit an entropy-based method to calculate the entropy of each query as a quantitative indicator for ambiguity, making a notion of ambiguity similarity as an available factor in relevance estimation. Finally we incorporate ambiguity similarity with closeness to derive a comprehensive relevance measurement. Experimental results show that our approach can achieve a good effect.