Search Engine Query Logs Research Articles

Mining Domain Terminologies Using Search Engine's Query Log

Domain terminologies are a basic resource for various natural language processing tasks. To automatically discover terminologies for a domain of interest, most traditional approaches mostly rely on a domain-specific corpus given in advance; thus, the performance of traditional approaches can only be guaranteed when collecting a high-quality domain-specific corpus, which requires extensive human involvement and domain expertise. In this article, we propose a novel approach that is capable of automatically mining domain terminologies using search engine's query log—a type of domain-independent corpus of higher availability, coverage, and timeliness than a manually collected domain-specific corpus. In particular, we represent query log as a heterogeneous network and formulate the task of mining domain terminology as transductive learning on the heterogeneous network. In the proposed approach, the manifold structure of domain-specificity inherent in query log is captured by using a novel network embedding algorithm and further exploited to reduce the need for the manual annotation efforts for domain terminology classification. We select Agriculture and Healthcare as the target domains and experiment using a real query log from a commercial search engine. Experimental results show that the proposed approach outperforms several state-of-the-art approaches.

Automatic prediction of news intent for search queries

PurposeThe purpose of this paper is to predict news intent by exploring contextual and temporal features directly mined from a general search engine query log.Design/methodology/approachFirst, a ground-truth data set with correctly marked news and non-news queries was built. Second, a detailed analysis of the search goals and topics distribution of news/non-news queries was conducted. Third, three news features, that is, the relationship between entity and contextual words extended from query sessions, topical similarity among clicked results and temporal burst point were obtained. Finally, to understand the utilities of the new features and prior features, extensive prediction experiments on SogouQ (a Chinese search engine query log) were conducted.FindingsNews intent can be predicted with high accuracy by using the proposed contextual and temporal features, and the macro average F1 of classification is around 0.8677. Contextual features are more effective than temporal features. All the three new features are useful and significant in improving the accuracy of news intent prediction.Originality/valueThis paper provides a new and different perspective in recognizing queries with news intent without use of such large corpora as social media (e.g. Wikipedia, Twitter and blogs) and news data sets. The research will be helpful for general-purpose search engines to address search intents for news events. In addition, the authors believe that the approaches described here in this paper are general enough to apply to other verticals with dynamic content and interest, such as blog or financial data.

The Effect of Big Data on Recommendation Quality. The Example of Internet Search

Are there economies of scale to data in internet search? This paper is first to use real search engine query logs to empirically investigate how data drives the quality of internet search results. We find evidence that the quality of search results improve with more data on previous searches. Moreover, our results indicate that the type of data matters as well: personalized information is particularly valuable as it massively increases the speed of learning. We also provide some evidence that factors not directly related to data such as the general quality of the applied algorithms play an important role. The suggested methods to disentangle the effect of data from other factors driving the quality of search results can be applied to assess the returns to data in various recommendation systems in e-commerce, including product and information search. We also discuss the managerial, privacy, and competition policy implications of our findings.

Search engines, news wires and digital epidemiology: Presumptions and facts

BackgroundDigital epidemiology tries to identify diseases dynamics and spread behaviors using digital traces collected via search engines logs and social media posts. However, the impacts of news on information-seeking behaviors have been remained unknown. MethodsData employed in this research provided from two sources, (1) Parsijoo search engine query logs of 48 months, and (2) a set of documents of 28 months of Parsijoo’s news service. Two classes of topics, i.e. macro-topics and micro-topics were selected to be tracked in query logs and news. Keywords of the macro-topics were automatically generated using web provided resources and exceeded 10k. Keyword set of micro-topics were limited to a numerable list including terms related to diseases and health-related activities. The tests are established in the form of three studies. Study A includes temporal analyses of 7 macro-topics in query logs. Study B considers analyzing seasonality of searching patterns of 9 micro-topics, and Study C assesses the impact of news media coverage on users’ health-related information-seeking behaviors. ResultsStudy A showed that the hourly distribution of various macro-topics followed the changes in social activity level. Conversely, the interestingness of macro-topics did not follow the regulation of topic distributions. Among macro-topics, “Pharmacotherapy” has highest interestingness level and wider time-window of popularity. In Study B, seasonality of a limited number of diseases and health-related activities were analyzed. Trends of infectious diseases, such as flu, mumps and chicken pox were seasonal. Due to seasonality of most of diseases covered in national vaccination plans, the trend belonging to “Immunization and Vaccination” was seasonal, as well. Cancer awareness events caused peaks in search trends of “Cancer” and “Screening” micro-topics in specific days of each year that mimic repeated patterns which may mistakenly be identified as seasonality. In study C, we assessed the co-integration and correlation between news and query trends. Our results demonstrated that micro-topics sparsely covered in news media had lowest level of impressiveness and, subsequently, the lowest impact on users’ intents. ConclusionOur results can reveal public reaction to social events, diseases and prevention procedures. Furthermore, we found that news trends are co-integrated with search queries and are able to reveal health-related events; however, they cannot be used interchangeably. It is recommended that the user-generated contents and news documents are analyzed mutually and interactively.

The Effect of Big Data on Recommendation Quality: The Example of Internet Search

Are there economies of scale to data in internet search? This paper is first to use real search engine query logs to empirically investigate how data drives the quality of internet search results. We find evidence that the quality of search results improve with more data on previous searches. Moreover, our results indicate that the type of data matters as well: personalized information is particularly valuable as it massively increases the speed of learning. We also provide some evidence that factors not directly related to data such as the general quality of the applied algorithms play an important role. The suggested methods to disentangle the effect of data from other factors driving the quality of search results can be applied to assess the returns to data in various recommendation systems in e-commerce, including product and information search. We also discuss the managerial, privacy, and competition policy implications of our findings.

Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China

ObjectivesHand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition...

Data Mining Framework for Web Search Personalization

Web users may have diverse search objectives when they search by giving queries and submitting it to a search engine. The inference and analysis of user search goals can be very useful for providing an expected result for a user search query. The below mentioned framework will give the data identified with the client objectives by analysing search engine query logs. User would practically benefit if search results could be displayed in different categories with manual rating so that user defined highest rated URLs are displayed at the top. It will help user to save effort and time in retrieving the required information. For achieving this feedback sessions are constructed from user click-through logs and pseudo-documents are generated. Pseudo-documents represent the feedback sessions for clustering. Using “Classified Average Precision (CAP)” criterion the performance of inferring user search goals can be evaluated.

A machine learning approach for result caching in web search engines

A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement.

Cross-Lingual Topic Discovery From Multilingual Search Engine Query Log

Today, major commercial search engines are operating in a multinational fashion to provide web search services for millions of users who compose search queries by different languages. Hence, the search engine query log, which serves as the backbone of many search engine applications, records millions of users’ search history in a wide spectrum of human languages and demonstrates a strong multilingual phenomenon. However, with its salience, the multilingual nature of a search engine query log is usually ignored by existing works, which usually consider query log entries of different languages as being orthogonal and independent. This kind of oversimplified assumption heavily distorts the underlying structure of web search data. In this article, we pioneer in recognition of the multilingual nature of a query log and make the first attempt to cross the language barrier in query logs. We propose a novel model named Cross-Lingual Query Log Topic Model (CL-QLTM) to analyze query logs from a cross-lingual perspective and derive the latent topics of web search data. The CL-QLTM comprehensively integrates web search data in different languages by collectively utilizing cross-lingual dictionaries, as well as the co-occurrence relations in the query log. In order to relieve the efficiency bottleneck of applying the CL-QLTM on voluminous query logs, we propose an efficient parameter inference algorithm based on the MapReduce computing paradigm. Both qualitative and quantitative experimental results show that the CL-QLTM is able to effectively derive cross-lingual topics from multilingual query logs and spawn a wide spectrum of new search engine applications.

Inferring User Search Goals with feedback Sessions using Fuzzy K-Means Algorithm

This document shows the concept for a broad topic and ambiguous query, different types of users may have different search goals when they submit the query to the search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance information and user experience. In this paper, we propose a novel approach to infer user search goals by analyzing search engine query logs. First, we propose a framework to search different user search goals for a query by making cluster to the proposed feedback sessions. Feedback sessions are constructed from user click-through logs i.e. user response and can efficiently reflect the information needs to users. Second, we propose a novel approach to create pseudo-documents to better represent the feedback sessions for clustering. Finally, we propose a new criterion Classified Average Precision (CAP) to calculate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to check the effectiveness of our proposed methods

New query suggestion framework and algorithms: A case study for an educational search engine

Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as “comparison of queries”. We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66–90% statistically significant increase in relevance of query suggestions compared to a baseline method.

Query intent inference via search engine log

Mining the latent intents behind search queries is critical for contemporary search engines. Therefore, there has been lots of effort on studying how to infer the intents of search queries via search engine query log. However, the task of query log-based intent inference is not trivial, since it involves cross-disciplinary knowledge of data modeling and data mining. In this paper, we tackle the problem of query intent inference by integrating multiple information sources in a seamless manner. We first propose a comprehensive data model called Search Query Log Structure (SQLS) that represents the relation between search queries via the User dimension, the URL dimension, the Session dimension and the Term dimension. In order to explore the effective ways of using such multidimensional information modeled by SQLS, we survey and compare three frameworks, namely Result-Oriented Framework, Laplacian-Oriented Framework and Topic-Oriented Framework, to infer the intents of search queries. Experimental results show that the three frameworks significantly outperform the state-of-the-art approach and meet the diverse requirements arising from different application scenarios.

Query Topic Classification and Sociology of Web Query Logs

In the paper, the objects, tasks, and a general procedure of the sociological analysis of Web search engine query logs are described and illustrated by a methodologically complete study of the cross-nation search image changes based on two-year spaced query logs of the national search audience.

Query suggestion with diversification and personalization

Web search query suggestion is an important functionality that facilitates information seeking of search engine users. In existing work, the concepts of diversification and personalization have been individually introduced to query suggestion systems. In this paper, we propose a new query suggestion paradigm, Query Suggestion With Diversification and Personalization (QS-DP) to effectively integrate diversification and personalization into one unified framework. In the QS-DP, the suggested queries are effectively diversified to cover different facets of the input query while the ranking of the suggested queries are personalized to ensure that the top ones are those that align with a user’s personal preferences. We evaluate QS-DP on a commercial search engine query log against several existing query suggestion methods. The experimental results verify our hypothesis that diversification and personalization can be effectively integrated and they are able to enhance each other within the QS-DP framework, which significantly outperforms several strong baselines with respect to a series of metrics.

Evaluation of Reranked Recommended Queries in Web Information Retrieval using NDCG and CV

Tremendous growth of the Web, lack of background knowledge about the Information Retrieval (IR), length of the input query keywords and its ambiguity, Query Recommendation is an important procedure which analyzes the real search intent of the user and recommends set of queries to be used in future to retrieve the relevant and required information. The proposed method recommends the queries by generating frequently accessed queries, rerank the recommended queries and evaluates the recommendation with the help of the ranking measures Normalized Discounted Cumulative Gain (NDCG) and Coefficient of Variance (CV). The proposed strategies are experimentally evaluated using real time American On Line (AOL) search engine query log.

Cost-Effective Conceptual Design for Information Extraction

It is well established that extracting and annotating occurrences of entities in a collection of unstructured text documents with their concepts improves the effectiveness of answering queries over the collection. However, it is very resource intensive to create and maintain large annotated collections. Since the available resources of an enterprise are limited and/or its users may have urgent information needs, it may have to select only a subset of relevant concepts for extraction and annotation. We call this subset a conceptual design for the annotated collection. In this article, we introduce and formally define the problem of cost-effective conceptual design where, given a collection, a set of relevant concepts, and a fixed budget, one likes to find a conceptual design that most improves the effectiveness of answering queries over the collection. We provide efficient algorithms for special cases of the problem and prove it is generally NP-hard in the number of relevant concepts. We propose three efficient approximations to solve the problem: a greedy algorithm, an approximate popularity maximization (APM for short), and approximate annotation-benefit maximization (AAM for short). We show that, if there are no constraints regrading the overlap of concepts, APM is a fully polynomial time approximation scheme. We also prove that if the relevant concepts are mutually exclusive, the greedy algorithm delivers a constant approximation ratio if the concepts are equally costly, APM has a constant approximation ratio, and AAM is a fully polynomial-time approximation scheme. Our empirical results using a Wikipedia collection and a search engine query log validate the proposed formalization of the problem and show that APM and AAM efficiently compute conceptual designs. They also indicate that, in general, APM delivers the optimal conceptual designs if the relevant concepts are not mutually exclusive. Also, if the relevant concepts are mutually exclusive, the conceptual designs delivered by AAM improve the effectiveness of answering queries over the collection more than the solutions provided by APM.

A New Algorithm for Inferring User Search Goals with Feedback Sessions

For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. In this paper, we propose a novel approach to infer user search goals by analyzing search engine query logs. First, we propose a framework to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are constructed from user clickthrough logs and can efficiently reflect the information needs of users. Second, we propose a novel approach to generate pseudo-documents to better represent the feedback sessions for clustering. Finally, we propose a new criterion “Classified Average Precision (CAP)” to evaluate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to validate the effectiveness of our proposed methods.

Query ranking model for search engine query recommendation

In this paper, we propose a query ranking model to select and order queries for search engine query recommendations. In contrast to existing similarity-based query recommendation methods (Agglomerative clustering of a search engine query log, 2000; The query-flow graph: model and applications, 2008], this model is based on utility, and ranks a query based on the joint probability of events whereby a query is selected by the user, the search results of the query are selected by the user, and the chosen search results satisfy the user’s information needs. We thus define three utilities in our model: a query-level utility representing the attractiveness of a query to the user, a perceived utility measuring the user’s actions given the search results, and a posterior utility measuring the user’s satisfaction with the chosen search results. We propose methods to compute these three utilities from query log data. In experiments involving real query log data, our proposed query ranking model outperformed seven other baseline methods in generating useful recommendations.

QUERY RECOMMENDATIONS AND ITS EVALUATION IN WEB INFORMATION RETRIEVAL

Search Engine retrieves significant and essential information from the web based on query terms given by the user. Due to the lack of background knowledge about the information required, shorter length queries posed by the user, the ambiguity of query keywords and dynamic growth of the web, irrelevant and redundant results are also retrieved by the search engine. Query recommendations is an important technique which analyze the real search intent of the user and suggests the alternative queries to be used by the user in future to satisfies their information need. The proposed method recommends and ranks the alternative queries and evaluates the ranking order of the recommendations with the help of the ranking measures Normalized Discounted Cumulative Gain (NDCG) and Coefficient of Variance (CV). These measures identify the relationship between the ranking techniques. The proposed strategies are experimentally evaluated using a real time search engine query log.

SG-WSTD: A framework for scalable geographic web search topic discovery

Search engine query logs are recognized as an important information source that contains millions of users’ web search needs. Discovering Geographic Web Search Topics (G-WSTs) from a query log can support a variety of downstream web applications such as finding commonality between locations and profiling search engine users. However, the task of discovering G-WSTs is nontrivial, not only because of the diversity of the information in web search but also due to the sheer size of query log. In this paper, we propose a new framework, Scalable Geographic Web Search Topic Discovery (SG-WSTD), which contains highly scalable functionalities such as search session derivation, geographic information extraction and geographic web search topic discovery to discover G-WSTs from query log. Within SG-WSTD, two probabilistic topic models are proposed to discover G-WSTs from two complementary perspectives. The first one is the Discrete Search Topic Model (DSTM), which discovers G-WSTs that capture the commonalities between discrete locations. The second is the Regional Search Topic Model (RSTM), which focuses on a specific geographic region on the map and discovers G-WSTs that demonstrate geographic locality. Since query log is typically voluminous, we implement the functionalities in SG-WSTD based on the MapReduce paradigm to solve the efficiency bottleneck. We evaluate SG-WSTD against several strong baselines on a real-life query log from AOL. The proposed framework demonstrates significantly improved data interpretability, better prediction performance, higher topic distinctiveness and superior scalability in the experimentation.