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1.
A growing body of research is beginning to explore the information-seeking behavior of Web users. The vast majority of these studies have concentrated on the area of textual information retrieval (IR). Little research has examined how people search for non-textual information on the Internet, and few large-scale studies has investigated visual information-seeking behavior with general-purpose Web search engines. This study examined visual information needs as expressed in users’ Web image queries. The data set examined consisted of 1,025,908 sequential queries from 211,058 users of Excite, a major Internet search service. Twenty-eight terms were used to identify queries for both still and moving images, resulting in a subset of 33,149 image queries by 9855 users. We provide data on: (1) image queries – the number of queries and the number of search terms per user, (2) image search sessions – the number of queries per user, modifications made to subsequent queries in a session, and (3) image terms – their rank/frequency distribution and the most highly used search terms. On average, there were 3.36 image queries per user containing an average of 3.74 terms per query. Image queries contained a large number of unique terms. The most frequently occurring image related terms appeared less than 10% of the time, with most terms occurring only once. We contrast this to earlier work by P.G.B. Enser, Journal of Documentation 51 (2) (1995) 126–170, who examined written queries for pictorial information in a non-digital environment. Implications for the development of models for visual information retrieval, and for the design of Web search engines are discussed.  相似文献   

2.
Both general and domain-specific search engines have adopted query suggestion techniques to help users formulate effective queries. In the specific domain of literature search (e.g., finding academic papers), the initial queries are usually based on a draft paper or abstract, rather than short lists of keywords. In this paper, we investigate phrasal-concept query suggestions for literature search. These suggestions explicitly specify important phrasal concepts related to an initial detailed query. The merits of phrasal-concept query suggestions for this domain are their readability and retrieval effectiveness: (1) phrasal concepts are natural for academic authors because of their frequent use of terminology and subject-specific phrases and (2) academic papers describe their key ideas via these subject-specific phrases, and thus phrasal concepts can be used effectively to find those papers. We propose a novel phrasal-concept query suggestion technique that generates queries by identifying key phrasal-concepts from pseudo-labeled documents and combines them with related phrases. Our proposed technique is evaluated in terms of both user preference and retrieval effectiveness. We conduct user experiments to verify a preference for our approach, in comparison to baseline query suggestion methods, and demonstrate the effectiveness of the technique with retrieval experiments.  相似文献   

3.
This article proposes a process to retrieve the URL of a document for which metadata records exist in a digital library catalog but a pointer to the full text of the document is not available. The process uses results from queries submitted to Web search engines for finding the URL of the corresponding full text or any related material. We present a comprehensive study of this process in different situations by investigating different query strategies applied to three general purpose search engines (Google, Yahoo!, MSN) and two specialized ones (Scholar and CiteSeer), considering five user scenarios. Specifically, we have conducted experiments with metadata records taken from the Brazilian Digital Library of Computing (BDBComp) and The DBLP Computer Science Bibliography (DBLP). We found that Scholar was the most effective search engine for this task in all considered scenarios and that simple strategies for combining and re-ranking results from Scholar and Google significantly improve the retrieval quality. Moreover, we study the influence of the number of query results on the effectiveness of finding missing information as well as the coverage of the proposed scenarios.  相似文献   

4.
Due to the proliferation and abundance of information on the web, ranking algorithms play an important role in web search. Currently, there are some ranking algorithms based on content and connectivity such as BM25 and PageRank. Unfortunately, these algorithms have low precision and are not always satisfying for users. In this paper, we propose an adaptive method, called A3CRank, based on the content, connectivity, and click-through data triple. Our method tries to aggregate ranking algorithms such as BM25, PageRank, and TF-IDF. We have used reinforcement learning to incorporate user behavior and find a measure of user satisfaction for each ranking algorithm. Furthermore, OWA, an aggregation operator is used for merging the results of the various ranking algorithms. A3CRank adapts itself with user needs and makes use of user clicks to aggregate the results of ranking algorithms. A3CRank is designed to overcome some of the shortcomings of existing ranking algorithms by combining them together and producing an overall better ranking criterion. Experimental results indicate that A3CRank outperforms other combinational ranking algorithms such as Ranking SVM in terms of P@n and NDCG metrics. We have used 130 queries on University of California at Berkeley’s web to train and evaluate our method.  相似文献   

5.
In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.  相似文献   

6.
The relevance feedback process uses information obtained from a user about a set of initially retrieved documents to improve subsequent search formulations and retrieval performance. In extended Boolean models, the relevance feedback implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with Boolean And/Or operators properly. Salton et al. proposed a relevance feedback method, called DNF (disjunctive normal form) method, for a well established extended Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulated Boolean queries. In this study, we investigate the problems of the DNF method and propose a relevance feedback method using hierarchical clustering techniques to solve those problems. We also propose a neural network model in which the term weights used in extended Boolean queries can be adjusted by the users’ relevance feedbacks.  相似文献   

7.
Expert finding addresses the task of retrieving and ranking talented people on the subject of user query. It is a practical issue in the Community Question Answering networks. Recruiters looking for knowledgeable people for their job positions are the most important clients of expert finding systems. In addition to employee expertise, the cost of hiring new staff is another significant concern for organizations. An efficient solution to cope with this concern is to hire T-shaped experts that are cost-effective. In this study, we have proposed a new deep model for T-shaped experts finding based on Convolutional Neural Networks. The proposed model tries to match queries and users by extracting local and position-invariant features from their corresponding documents. In other words, it detects users’ shape of expertise by learning patterns from documents of users and queries simultaneously. The proposed model contains two parallel CNN’s that extract latent vectors of users and queries based on their corresponding documents and join them together in the last layer to match queries with users. Experiments on a large subset of Stack Overflow documents indicate the effectiveness of the proposed method against baselines in terms of NDCG, MRR, and ERR evaluation metrics.  相似文献   

8.
Author co-citation analysis (ACA) has been widely used in bibliometrics as an analytical method in analyzing the intellectual structure of science studies. It can be used to identify authors from the same or similar research fields. However, such analysis method relies heavily on statistical tools to perform the analysis and requires human interpretation. Web Citation Database is a data warehouse used for storing citation indices of Web publications. In this paper, we propose a mining process to automate the ACA based on the Web Citation Database. The mining process uses agglomerative hierarchical clustering (AHC) as the mining technique for author clustering and multidimensional scaling (MDS) for displaying author cluster maps. The clustering results and author cluster map have been incorporated into a citation-based retrieval system known as PubSearch to support author retrieval of Web publications.  相似文献   

9.
Recreational queries from users searching for places to go and things to do or see are very common in web and mobile search. Users specify constraints for what they are looking for, like suitability for kids, romantic ambiance or budget. Queries like “restaurants in New York City” are currently served by static local results or the thumbnail carousel. More complex queries like “things to do in San Francisco with kids” or “romantic places to eat in Seattle” require the user to click on every element of the search engine result page to read articles from Yelp, TripAdvisor, or WikiTravel to satisfy their needs. Location data, which is an essential part of web search, is even more prevalent with location-based social networks and offers new opportunities for many ways of satisfying information seeking scenarios.In this paper, we address the problem of recreational queries in information retrieval and propose a solution that combines search query logs with LBSNs data to match user needs and possible options. At the core of our solution is a framework that combines social, geographical, and temporal information for a relevance model centered around the use of semantic annotations on Points of Interest with the goal of addressing these recreational queries. A central part of the framework is a taxonomy derived from behavioral data that drives the modeling and user experience. We also describe in detail the complexity of assessing and evaluating Point of Interest data, a topic that is usually not covered in related work, and propose task design alternatives that work well.We demonstrate the feasibility and scalability of our methods using a data set of 1B check-ins and a large sample of queries from the real-world. Finally, we describe the integration of our techniques in a commercial search engine.  相似文献   

10.
在信息检索中对基于用户兴趣的检索结果进行重排得到广泛关注。为了构建用户兴趣的知识库,本文对用户的登录细节和点击数据进行综合分析,提出了定制用户访问信息的方法,同时采用开放式目录项目Dmoz自动进行用户兴趣主题映射,对搜索结果进行个性化分类,并根据用户兴趣对检索结果重排,比正常的搜索引擎更容易找到相关的信息。联机实验结果表明,本文提出的方法可有效地提高用户检索精度。  相似文献   

11.
百度国学和CNKI是国内两个重要的中文学术搜索引擎,Google Scholar和Scirus是国际公认的专业学术搜索引擎,介绍了它们各自的特点及其功能,分别从数据库、检索功能、检索结果与用户界面3个方面来比较分析了它们各自的异同点,并对学术搜索引擎的发展趋势做了展望。  相似文献   

12.
Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.  相似文献   

13.
The advancements of search engines for traditional text documents have enabled the effective retrieval of massive textual information in a resource-efficient manner. However, such conventional search methodologies often suffer from poor retrieval accuracy especially when documents exhibit unique properties that behoove specialized and deeper semantic extraction. Recently, AlgorithmSeer, a search engine for algorithms has been proposed, that extracts pseudo-codes and shallow textual metadata from scientific publications and treats them as traditional documents so that the conventional search engine methodology could be applied. However, such a system fails to facilitate user search queries that seek to identify algorithm-specific information, such as the datasets on which algorithms operate, the performance of algorithms, and runtime complexity, etc. In this paper, a set of enhancements to the previously proposed algorithm search engine are presented. Specifically, we propose a set of methods to automatically identify and extract algorithmic pseudo-codes and the sentences that convey related algorithmic metadata using a set of machine-learning techniques. In an experiment with over 93,000 text lines, we introduce 60 novel features, comprising content-based, font style based and structure-based feature groups, to extract algorithmic pseudo-codes. Our proposed pseudo-code extraction method achieves 93.32% F1-score, outperforming the state-of-the-art techniques by 28%. Additionally, we propose a method to extract algorithmic-related sentences using deep neural networks and achieve an accuracy of 78.5%, outperforming a Rule-based model and a support vector machine model by 28% and 16%, respectively.  相似文献   

14.
In the whole world, the internet is exercised by millions of people every day for information retrieval. Even for a small to smaller task like fixing a fan, to cook food or even to iron clothes persons opt to search the web. To fulfill the information needs of people, there are billions of web pages, each having a different degree of relevance to the topic of interest (TOI), scattered throughout the web but this huge size makes manual information retrieval impossible. The page ranking algorithm is an integral part of search engines as it arranges web pages associated with a queried TOI in order of their relevance level. It, therefore, plays an important role in regulating the search quality and user experience for information retrieval. PageRank, HITS, and SALSA are well-known page ranking algorithm based on link structure analysis of a seed set, but ranking given by them has not yet been efficient. In this paper, we propose a variant of SALSA to give sNorm(p) for the efficient ranking of web pages. Our approach relies on a p-Norm from Vector Norm family in a novel way for the ranking of web pages as Vector Norms can reduce the impact of low authority weight in hub weight calculation in an efficient way. Our study, then compares the rankings given by PageRank, HITS, SALSA, and sNorm(p) to the same pages in the same query. The effectiveness of the proposed approach over state of the art methods has been shown using performance measurement technique, Mean Reciprocal Rank (MRR), Precision, Mean Average Precision (MAP), Discounted Cumulative Gain (DCG) and Normalized DCG (NDCG). The experimentation is performed on a dataset acquired after pre-processing of the results collected from initial few pages retrieved for a query by the Google search engine. Based on the type and amount of in-hand domain expertise 30 queries are designed. The extensive evaluation and result analysis are performed using MRR, [email protected], MAP, DCG, and NDCG as the performance measuring statistical metrics. Furthermore, results are statistically verified using a significance test. Findings show that our approach outperforms state of the art methods by attaining 0.8666 as MRR value, 0.7957 as MAP value. Thus contributing to the improvement in the ranking of web pages more efficiently as compared to its counterparts.  相似文献   

15.
Recent research in the human computer interaction and information retrieval areas has revealed that search response latency exhibits a clear impact on the user behavior in web search. Such impact is reflected both in users’ subjective perception of the usability of a search engine and in their interaction with the search engine in terms of the number of search results they engage with. However, a similar impact analysis has been missing so far in the context of sponsored search. Since the predominant business model for commercial search engines is advertising via sponsored search results (i.e., search advertisements), understanding how response latency influences the user interaction with the advertisements displayed on the search engine result pages is crucial to increase the revenue of a commercial search engine. To this end, we conduct a large-scale analysis using query logs obtained from a commercial web search. We analyze the short-term and long-term impact of search response latency on the querying and clicking behaviors of users using desktop and mobile devices to access the search engine, as well as the corresponding impact on the revenue of the search engine. This analysis demonstrates the importance of serving sponsored search results with low latency and provides insight into the ad serving policy of commercial search engines to ensure long-term user engagement and search revenue.  相似文献   

16.
In the web environment, most of the queries issued by users are implicit by nature. Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results. Previous works tackling this problem usually focused on news queries, where the retrieval of the most recent results related to the query are usually sufficient to meet the user's information needs. However, few works have studied the importance of time in queries such as “Philip Seymour Hoffman” where the results may require no recency at all. In this work, we focus on this type of queries named “time-sensitive queries” where the results are preferably from a diversified time span, not necessarily the most recent one. Unlike related work, we follow a content-based approach to identify the most important time periods of the query and integrate time into a re-ranking model to boost the retrieval of documents whose contents match the query time period. For that purpose, we define a linear combination of topical and temporal scores, which reflects the relevance of any web document both in the topical and temporal dimensions, thus contributing to improve the effectiveness of the ranked results across different types of queries. Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query, while filtering out the non-relevant ones. Through extensive experimental evaluation over web corpora, we show that our model offers promising results compared to baseline approaches. As a result of our investigation, we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community.  相似文献   

17.
Real time search is an increasingly important area of information seeking on the Web. In this research, we analyze 1,005,296 user interactions with a real time search engine over a 190 day period. Using query log analysis, we investigate searching behavior, categorize search topics, and measure the economic value of this real time search stream. We examine aggregate usage of the search engine, including number of users, queries, and terms. We then classify queries into subject categories using the Google Directory topical hierarchy. We next estimate the economic value of the real time search traffic using the Google AdWords keyword advertising platform. Results shows that 30% of the queries were unique (used only once in the entire dataset), which is low compared to traditional Web searching. Also, 60% of the search traffic comes from the search engine’s application program interface, indicating that real time search is heavily leveraged by other applications. There are many repeated queries over time via these application program interfaces, perhaps indicating both long term interest in a topic and the polling nature of real time queries. Concerning search topics, the most used terms dealt with technology, entertainment, and politics, reflecting both the temporal nature of the queries and, perhaps, an early adopter user-based. However, 36% of the queries indicate some geographical affinity, pointing to a location-based aspect to real time search. In terms of economic value, we calculate this real time search stream to be worth approximately US $33,000,000 (US $33 M) on the online advertising market at the time of the study. We discuss the implications for search engines and content providers as real time content increasingly enters the main stream as an information source.  相似文献   

18.
To address the inability of current ranking systems to support subtopic retrieval, two main post-processing techniques of search results have been investigated: clustering and diversification. In this paper we present a comparative study of their performance, using a set of complementary evaluation measures that can be applied to both partitions and ranked lists, and two specialized test collections focusing on broad and ambiguous queries, respectively. The main finding of our experiments is that diversification of top hits is more useful for quick coverage of distinct subtopics whereas clustering is better for full retrieval of single subtopics, with a better balance in performance achieved through generating multiple subsets of diverse search results. We also found that there is little scope for improvement over the search engine baseline unless we are interested in strict full-subtopic retrieval, and that search results clustering methods do not perform well on queries with low divergence subtopics, mainly due to the difficulty of generating discriminative cluster labels.  相似文献   

19.
Search sessions consist of a person presenting a query to a search engine, followed by that person examining the search results, selecting some of those search results for further review, possibly following some series of hyperlinks, and perhaps backtracking to previously viewed pages in the session. The series of pages selected for viewing in a search session, sometimes called the click data, is intuitively a source of relevance feedback information to the search engine. We are interested in how that relevance feedback can be used to improve the search results quality for all users, not just the current user. For example, the search engine could learn which documents are frequently visited when certain search queries are given.  相似文献   

20.
The performance and capabilities of Web search engines is an important and significant area of research. Millions of people world wide use Web search engines very day. This paper reports the results of a major study examining the overlap among results retrieved by multiple Web search engines for a large set of more than 10,000 queries. Previous smaller studies have discussed a lack of overlap in results returned by Web search engines for the same queries. The goal of the current study was to conduct a large-scale study to measure the overlap of search results on the first result page (both non-sponsored and sponsored) across the four most popular Web search engines, at specific points in time using a large number of queries. The Web search engines included in the study were MSN Search, Google, Yahoo! and Ask Jeeves. Our study then compares these results with the first page results retrieved for the same queries by the metasearch engine Dogpile.com. Two sets of randomly selected user-entered queries, one set was 10,316 queries and the other 12,570 queries, from Infospace’s Dogpile.com search engine (the first set was from Dogpile, the second was from across the Infospace Network of search properties were submitted to the four single Web search engines). Findings show that the percent of total results unique to only one of the four Web search engines was 84.9%, shared by two of the three Web search engines was 11.4%, shared by three of the Web search engines was 2.6%, and shared by all four Web search engines was 1.1%. This small degree of overlap shows the significant difference in the way major Web search engines retrieve and rank results in response to given queries. Results point to the value of metasearch engines in Web retrieval to overcome the biases of individual search engines.  相似文献   

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