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1.
Ali Mohammad Zareh Bidoki Pedram Ghodsnia Nasser Yazdani Farhad Oroumchian 《Information processing & management》2010
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
2.
A fast and efficient page ranking mechanism for web crawling and retrieval remains as a challenging issue. Recently, several link based ranking algorithms like PageRank, HITS and OPIC have been proposed. In this paper, we propose a novel recursive method based on reinforcement learning which considers distance between pages as punishment, called “DistanceRank” to compute ranks of web pages. The distance is defined as the number of “average clicks” between two pages. The objective is to minimize punishment or distance so that a page with less distance to have a higher rank. Experimental results indicate that DistanceRank outperforms other ranking algorithms in page ranking and crawling scheduling. Furthermore, the complexity of DistanceRank is low. We have used University of California at Berkeley’s web for our experiments. 相似文献
3.
对科研项目进行合理排序,是科研项目评审和筛选的前提,也是科研管理的重要内容。被评科研项目数量有限情形下采用专家排序方式更为合理,而有序投票模型是处理此类问题的经典方法。提出了基于公共权重的有序投票模型,给出了基于交叉评价与总体评价的公共权重确定方法,弥补了传统有序投票模型中权重不一致、区分度不高的缺陷。 相似文献
4.
《Information processing & management》2001,37(3):435-458
We are interested in how ideas from document clustering can be used to improve the retrieval accuracy of ranked lists in interactive systems. In particular, we are interested in ways to evaluate the effectiveness of such systems to decide how they might best be constructed. In this study, we construct and evaluate systems that present the user with ranked lists and a visualization of inter-document similarities. We first carry out a user study to evaluate the clustering/ranked list combination on instance-oriented retrieval, the task of the TREC-6 Interactive Track. We find that although users generally prefer the combination, they are not able to use it to improve effectiveness. In the second half of this study, we develop and evaluate an approach that more directly combines the ranked list with information from inter-document similarities. Using the TREC collections and relevance judgments, we show that it is possible to realize substantial improvements in effectiveness by doing so, and that although users can use the combined information effectively, the system can provide hints that substantially improve on the user's solo effort. The resulting approach shares much in common with an interactive application of incremental relevance feedback. Throughout this study, we illustrate our work using two prototype systems constructed for these evaluations. The first, AspInQuery, is a classic information retrieval system augmented with a specialized tool for recording information about instances of relevance. The other system, Lighthouse, is a Web-based application that combines a ranked list with a portrayal of inter-document similarity. Lighthouse can work with collections such as TREC, as well as the results of Web search engines. 相似文献
5.
《Information processing & management》2023,60(4):103403
Most of the existing GNN-based recommender system models focus on learning users’ personalized preferences from these (explicit/implicit) positive feedback to achieve personalized recommendations. However, in the real-world recommender system, the users’ feedback behavior also includes negative feedback behavior (e.g., click dislike button), which also reflects users’ personalized preferences. How to utilize negative feedback is a challenging research problem. In this paper, we first qualitatively and quantitatively analyze the three kinds of negative feedback that widely existed in real-world recommender systems and investigate the role of negative feedback in recommender systems. We found that it is different from what we expected — not all negative items are ranked low, and some negative items are even ranked high in the overall items. Then, we propose a novel Signed Graph Neural Network Recommendation model (SiGRec) to encode the users’ negative feedback behavior. Our SiGRec can learn positive and negative embeddings of users and items via positive and negative graph neural network encoders, respectively. Besides, we also define a new Sign Cosine (SiC) loss function to adaptively mine the information of negative feedback for different types of negative feedback. Extensive experiments on four datasets demonstrate the proposed model outperforms several existing models. Specifically, on the Zhihu dataset, SiGRec outperforms the unsigned GNN model (i.e., LightGCN), 27.58% 29.81%, and 31.21% in P@20, R@20, and nDCG@20, respectively. We hope our work can open the door to further exploring the negative feedback in recommendations. 相似文献
6.
Graph-based recommendation approaches use a graph model to represent the relationships between users and items, and exploit the graph structure to make recommendations. Recent graph-based recommendation approaches focused on capturing users’ pairwise preferences and utilized a graph model to exploit the relationships between different entities in the graph. In this paper, we focus on the impact of pairwise preferences on the diversity of recommendations. We propose a novel graph-based ranking oriented recommendation algorithm that exploits both explicit and implicit feedback of users. The algorithm utilizes a user-preference-item tripartite graph model and modified resource allocation process to match the target user with users who share similar preferences, and make personalized recommendations. The principle of the additional preference layer is to capture users’ pairwise preferences, provide detailed information of users for further recommendations. Empirical analysis of four benchmark datasets demonstrated that our proposed algorithm performs better in most situations than other graph-based and ranking-oriented benchmark algorithms. 相似文献
7.
Chia-Liang HungAuthor Vitae Jerome Chih-Lung ChouAuthor Vitae 《International Journal of Information Management》2011,31(4):317-326
The study focuses on which users to target and why and how to inspire their participation by applying combination of von Hippel's lead user and user innovation toolkits with Rogers’ innovation diffusion theories. After an investigation of a social networking website, this study finds that individuals with large number of hits are highly active users of new functions. Moreover, they are likely to use toolkits to customize their personal uses and respond to others’ problems. Therefore, they garner appreciation from others in return, achieve higher ranks in the top hit parade, and obtain better-expected benefits from the website's incentive compensation. This study also evaluates the toolkits’ efficacy in the Web 2.0 context and finds that they are not equivalents. This research offers insights useful for web service providers to target innovative users and create an environment using web toolkits to induce user-generated innovation and achieve better effect of innovation communication. 相似文献
8.
The goal of the study presented in this article is to investigate to what extent the classification of a web page by a single genre matches the users’ perspective. The extent of agreement on a single genre label for a web page can help understand whether there is a need for a different classification scheme that overrides the single-genre labelling. My hypothesis is that a single genre label does not account for the users’ perspective. In order to test this hypothesis, I submitted a restricted number of web pages (25 web pages) to a large number of web users (135 subjects) asking them to assign only a single genre label to each of the web pages. Users could choose from a list of 21 genre labels, or select one of the two ‘escape’ options, i.e. ‘Add a label’ and ‘I don’t know’. The rationale was to observe the level of agreement on a single genre label per web page, and draw some conclusions about the appropriateness of limiting the assignment to only a single label when doing genre classification of web pages. Results show that users largely disagree on the label to be assigned to a web page. 相似文献
9.
通过对网上3个免费专利数据库的收录范围、检索技巧等方面的介绍和分析,提出检索时应注意的问题,为用户使用专利文献提供参考。 相似文献
10.
The study investigated how users’ emotion control and search tasks interact and influence the Web search behavior and performance among experienced Web users. Sixty-seven undergraduate students with substantial Web experience participated in the study. Effects of emotion control and tasks were found significant on the search behavior but not on the search performance. The interaction effect between emotion control and tasks on the search behavior was also significant: effects of users’ emotion control on the search behavior varied depending on search tasks. Profile analyses of search behaviors identified and contrasted the most commonly occurring profiles of search activities in different search tasks. Suggestions were made to improve information literacy programs, and implications for future research were discussed. 相似文献
11.
网络舆情作为一种重要的舆情形式,具有形成速度快,受众人群广等特点,对国家和社会的影响越来越重大。互联网用户可以自由地在微博、论坛、博客等中发表有关社会中各类现实问题的态度和意见。监测网络舆情的主要手段就是利用网络爬虫对目标网络的页面数据进行挖掘,然后对挖掘的数据进行分类处理,并科学地统计舆情信息。本文主要分析网络舆情的特征和处理对策,并利用网络爬虫、全文检索、关键词评分、以及科学数理统计等手段对网络舆情监测系统的原理进行探索与系统实现。 相似文献
12.
Lori Lorigo Bing Pan Helene Hembrooke Thorsten Joachims Laura Granka Geri Gay 《Information processing & management》2006
To improve search engine effectiveness, we have observed an increased interest in gathering additional feedback about users’ information needs that goes beyond the queries they type in. Adaptive search engines use explicit and implicit feedback indicators to model users or search tasks. In order to create appropriate models, it is essential to understand how users interact with search engines, including the determining factors of their actions. Using eye tracking, we extend this understanding by analyzing the sequences and patterns with which users evaluate query result returned to them when using Google. We find that the query result abstracts are viewed in the order of their ranking in only about one fifth of the cases, and only an average of about three abstracts per result page are viewed at all. We also compare search behavior variability with respect to different classes of users and different classes of search tasks to reveal whether user models or task models may be greater predictors of behavior. We discover that gender and task significantly influence different kinds of search behaviors discussed here. The results are suggestive of improvements to query-based search interface designs with respect to both their use of space and workflow. 相似文献
13.
A critical challenge for Web search engines concerns how they present relevant results to searchers. The traditional approach is to produce a ranked list of results with title and summary (snippet) information, and these snippets are usually chosen based on the current query. Snippets play a vital sensemaking role, helping searchers to efficiently make sense of a collection of search results, as well as determine the likely relevance of individual results. Recently researchers have begun to explore how snippets might also be adapted based on searcher preferences as a way to better highlight relevant results to the searcher. In this paper we focus on the role of snippets in collaborative web search and describe a technique for summarizing search results that harnesses the collaborative search behaviour of communities of like-minded searchers to produce snippets that are more focused on the preferences of the searchers. We go on to show how this so-called social summarization technique can generate summaries that are significantly better adapted to searcher preferences and describe a novel personalized search interface that combines result recommendation with social summarization. 相似文献
14.
Yogesh Sankarasubramaniam Krishnan Ramanathan Subhankar Ghosh 《Information processing & management》2014
Automatic text summarization has been an active field of research for many years. Several approaches have been proposed, ranging from simple position and word-frequency methods, to learning and graph based algorithms. The advent of human-generated knowledge bases like Wikipedia offer a further possibility in text summarization – they can be used to understand the input text in terms of salient concepts from the knowledge base. In this paper, we study a novel approach that leverages Wikipedia in conjunction with graph-based ranking. Our approach is to first construct a bipartite sentence–concept graph, and then rank the input sentences using iterative updates on this graph. We consider several models for the bipartite graph, and derive convergence properties under each model. Then, we take up personalized and query-focused summarization, where the sentence ranks additionally depend on user interests and queries, respectively. Finally, we present a Wikipedia-based multi-document summarization algorithm. An important feature of the proposed algorithms is that they enable real-time incremental summarization – users can first view an initial summary, and then request additional content if interested. We evaluate the performance of our proposed summarizer using the ROUGE metric, and the results show that leveraging Wikipedia can significantly improve summary quality. We also present results from a user study, which suggests that using incremental summarization can help in better understanding news articles. 相似文献
15.
晒客是网络环境下新兴的信息用户群体,其信息行为方式对传统的信息交流方式产生了重要影响。文章利用“沉默的螺旋”理论对Web2.0环境下晒客的信息行为进行探析,总结了晒客信息行为的特点,旨在引起图书情报工作者对Web2.0环境下信息交流方式及晒客人群的信息行为做进一步研究,完善信息服务。 相似文献
16.
The Web and especially major Web search engines are essential tools in the quest to locate online information for many people. This paper reports results from research that examines characteristics and changes in Web searching from nine studies of five Web search engines based in the US and Europe. We compare interactions occurring between users and Web search engines from the perspectives of session length, query length, query complexity, and content viewed among the Web search engines. The results of our research shows (1) users are viewing fewer result pages, (2) searchers on US-based Web search engines use more query operators than searchers on European-based search engines, (3) there are statistically significant differences in the use of Boolean operators and result pages viewed, and (4) one cannot necessary apply results from studies of one particular Web search engine to another Web search engine. The wide spread use of Web search engines, employment of simple queries, and decreased viewing of result pages may have resulted from algorithmic enhancements by Web search engine companies. We discuss the implications of the findings for the development of Web search engines and design of online content. 相似文献
17.
《Information processing & management》2001,37(4):593-601
There are several recent studies that propose search output clustering as an alternative representation method to ranked output. Users are provided with cluster representations instead of lists of titles and invited to make decisions on groups of documents. This paper discusses the difficulties involved in representing clusters for users’ evaluation in a concise but easily interpretable form. The discussion is based on findings and user feedback from a user study investigating the effectiveness of search output clustering. The overall impression created by the experiment results and users’ feedback is that clusters cannot be relied on to consistently produce meaningful document groups that can easily be recognised by the users. They also seem to lead to unrealistic user expectations. 相似文献
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19.
Web 2.0 and folksonomies in a library context 总被引：1，自引：0，他引：1
Libraries have a societal purpose and this role has become increasingly important as new technologies enable organizations to support, enable and enhance the participation of users in assuming an active role in the creation and communication of information. Folksonomies, a Web 2.0 technology, represent such an example. Folksonomies result from individuals freely tagging resources available to them on a computer network. In a library environment folksonomies have the potential of overcoming certain limitations of traditional classification systems such as the Library of Congress Subject Headings (LCSH). Typical limitations of this type of classification systems include, for example, the rigidity of the underlying taxonomical structures and the difficulty of introducing change in the categories. Folksonomies represent a supporting technology to existing classification systems helping to describe library resources more flexibly, dynamically and openly. As a review of the current literature shows, the adoption of folksonomies in libraries is novel and limited research has been carried out in the area. This paper presents research into the adoption of folksonomies for a University library. A Web 2.0 system was developed, based on the requirements collected from library stakeholders, and integrated with the existing library computer system. An evaluation of the work was carried out in the form of a survey in order to understand the possible reactions of users to folksonomies as well as the effects on their behavior. The broad conclusion of this work is that folksonomies seem to have a beneficial effect on users’ involvement as active library participants as well as encourage users to browse the catalogue in more depth. 相似文献
20.
随着Web2.0的出现,就业信息环境和用户需求正发生着深刻的变化,传统的就业信息服务方式已经不能满足用户的需求。针对上述问题,文章提出了Web2.0环境下高校就业信息服务平台建设的策略。 相似文献