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检索条件"任意字段=5th ACM International Conference on Web Search and Data Mining, WSDM 2012"

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Domain bias in web search 12

Domain bias in web search

引用

5th acm international conference on web search and data mining, wsdm 2012

作者： Ieong, Samuel Mishra, Nina Sadikov, Eldar Zhang, Li Microsoft Research United States Stanford University United States

ISBN: (纸本)9781450307475

this paper uncovers a new phenomenon in web search that we call domain bias - a user's propensity to believe that a page is more relevant just because it comes from a particular domain. We provide evidence of the existence of domain bias in click activity as well as in human judgments via a comprehensive collection of experiments. We begin by studying the difference between domains that a search engine surfaces and that users click. Surprisingly, we find that despite changes in the overall distribution of surfaced domains, there has not been a comparable shift in the distribution of clicked domains. Users seem to have learned the landscape of the internet and their click behavior has thus become more predictable over time. Next, we run a blind domain test, akin to a Pepsi/Coke taste test, to determine whether domains can shift a user's opinion of which page is more relevant. We find that domains can actually flip a user's preference about 25% of the time. Finally, we demonstrate the existence of systematic domain preferences, even after factoring out confounding issues such as position bias and relevance, two factors that have been used extensively in past work to explain user behavior. the existence of domain bias has numerous consequences including, for example, the importance of discounting click activity from reputable domains. Copyright 2012 acm.

关键词： search engines

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Extracting search-focused key n-grams for relevance ranking in web search 12

Extracting search-focused key n-grams for relevance ranking ...

引用

5th acm international conference on web search and data mining, wsdm 2012

作者： Wang, Chen Bi, Keping Hu, Yunhua Li, Hang Cao, Guihong Fudan University Shanghai China Peking University Beijing China Microsoft Research Asia Beijing China Microsoft Corporation Redmond WA United States

ISBN: (纸本)9781450307475

In web search, relevance ranking of popular pages is relatively easy, because of the inclusion of strong signals such as anchor text and search log data. In contrast, with less popular pages, relevance ranking becomes very challenging due to a lack of information. In this paper the former is referred to as head pages, and the latter tail pages. We address the challenge by learning a model that can extract search-focused key n-grams from web pages, and using the key n-grams for searches of the pages, particularly, the tail pages. To the best of our knowledge, this problem has not been previously studied. Our approach has four characteristics. First, key n-grams are search-focused in the sense that they are defined as those which can compose "good queries" for searching the page. Second, key n-grams are learned in a relative sense using learning to rank techniques. third, key n-grams are learned using search log data, such that the characteristics of key n-grams in the search log data, particularly in the heads;can be applied to the other data, particularly to the tails. Fourth, the extracted key n-grams are used as features of the relevance ranking model also trained with learning to rank techniques. Experiments validate the effectiveness of the proposed approach with large-scale web search datasets. the results show that our approach can significantly improve relevance ranking performance on both heads and tails;and particularly tails, compared with baseline approaches. Characteristics of our approach have also been fully investigated through comprehensive experiments. Copyright 2012 acm.

关键词： websites

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Large-scale analysis of individual and task differences in search result page examination strategies 12

Large-scale analysis of individual and task differences in s...

引用

5th acm international conference on web search and data mining, wsdm 2012

作者： Buscher, Georg White, Ryen W. Dumais, Susan T. Huang, Jeff Microsoft Bing Bellevue WA 98004 United States Microsoft Research Redmond WA 98052 United States University of Washington Seattle WA 98195 United States

ISBN: (纸本)9781450307475

Understanding the impact of individual and task differences on search result page examination strategies is important in developing improved search engines. Characterizing these effects using query and click data alone is common but insufficient since they provide an incomplete picture of result examination behavior. Cursor- or gaze-tracking studies reveal richer interaction patterns but are often done in small-scale laboratory settings. In this paper we leverage large-scale rich behavioral log data in a naturalistic setting. We examine queries, clicks, cursor movements, scrolling, and text highlighting for millions of queries on the Bing commercial search engine to better understand the impact of user, task, and user-task interactions on user behavior on search result pages (SERPs). By clustering users based on cursor features, we identify individual, task, and user-task differences in how users examine results which are similar to those observed in small-scale studies. Our findings have implications for developing search support for behaviorally-similar searcher cohorts, modeling search behavior, and designing search systems that leverage implicit feedback. Copyright 2012 acm.

关键词： search engines

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From chatter to headlines: Harnessing the real-time web for personalized news recommendation 12

From chatter to headlines: Harnessing the real-time web for ...

引用

5th acm international conference on web search and data mining, wsdm 2012

作者： De Francisci Morales, Gianmarco Gionis, Aristides Lucchese, Claudio IMT Lucca Italy ISTI-CNR Pisa Italy Yahoo Research Barcelona Spain

ISBN: (纸本)9781450307475

We propose a new methodology for recommending interesting news to users by exploiting the information in their twitter persona. We model relevance between users and news articles using a mix of signals drawn from the news stream and from twitter: the profile of the social neighborhood of the users, the content of their own tweet stream, and topic popularity in the news and in the whole twitter-land. We validate our approach on a real-world dataset of approximately 40k articles coming from Yahoo! News and one month of crawled twitter data. We train our model using a learning-to-rank approach and support-vector machines. the train and test set are drawn from Yahoo! toolbar log data. We heuristically identify 3 214 users of twitter in the log and use their clicks on news articles to train our system. Our methodology is able to predict with good accuracy the news articles clicked by the users and rank them higher than other news articles. the results show that the combination of various signals from real-time web and microblogging platforms can be a useful resource to understand user behavior. Copyright 2012 acm.

关键词： Blogs

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Topical clustering of search results 12

Topical clustering of search results

引用

5th acm international conference on web search and data mining, wsdm 2012

作者： Scaiella, Ugo Ferragina, Paolo Marino, Andrea Ciaramita, Massimiliano Dipartimento di Informatica University of Pisa Italy Dip. di Sistemi e Informatica University of Florence Italy Google Research Zürich Switzerland

ISBN: (纸本)9781450307475

search results clustering (SRC) is a challenging algorithmic problem that requires grouping together the results returned by one or more search engines in topically coherent clusters, and labeling the clusters with meaningful phrases describing the topics of the results included in them. In this paper we propose to solve SRC via an innovative approach that consists of modeling the problem as the la-beled clustering of the nodes of a newly introduced graph of topics. the topics are Wikipedia-pages identified by means of recently proposed topic annotators [9, 11, 16, 20] applied to the search results, and the edges denote the relatedness among these topics computed by taking into account the linkage of the Wikipedia-graph. We tackle this problem by designing a novel algorithm that exploits the spectral properties and the labels of that graph of topics. We show the superiority of our approach with respect to academic state-of-the-art work [6] and wellknown commercial systems (Clusty and Lingo3G) by performing an extensive set of experiments on standard datasets and user studies via Amazon Mechanical Turk. We test several standard measures for evaluating the performance of all systems and show a relative improvement of up to 20%. Copyright 2012 acm.

关键词： search engines

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Tapping into knowledge base for concept feedback: Leveraging ConceptNet to improve search results for difficult queries 12

Tapping into knowledge base for concept feedback: Leveraging...

引用

5th acm international conference on web search and data mining, wsdm 2012

作者： Kotov, Alexander Zhai, Cheng Xiang Department of Computer Science University of Illinois at Urbana-Champaign 201 N Goodwin Ave. Urbana IL 61801 United States

ISBN: (纸本)9781450307475

Query expansion is an important and commonly used technique for improving web search results. Existing methods for query expansion have mostly relied on global or local analysis of document collection, click-through data, or simple ontologies such as WordNet. In this paper, we present the results of a systematic study of the methods leveraging the ConceptNet knowledge base, an emerging new web resource, for query expansion. Specifically, we focus on the methods leveraging ConceptNet to improve the search results for poorly performing (or difficult) queries. Unlike other lexico-semantic resources, such as WordNet and Wikipedia, which have been extensively studied in the past, ConceptNet features a graph-based representation model of commonsense knowledge, in which the terms are conceptually related through rich relational ontology. Such representation structure enables complex, multi-step inferences between the concepts, which can be applied to query expansion. We first demonstrate through simulation experiments that expanding queries with the related concepts from ConceptNet has great potential for improving the search results for difficult queries. We then propose and study several supervised and unsupervised methods for selecting the concepts from ConceptNet for automatic query expansion. the experimental results on multiple data sets indicate that the proposed methods can effectively leverage ConceptNet to improve the retrieval performance of difficult queries both when used in isolation as well as in combination with pseudo-relevance feedback. Copyright 2012 acm.

关键词： Ontology

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DL-wsdm'15: Workshop on Deep Learning for web search and data mining 15

DL-WSDM'15: Workshop on Deep Learning for Web Search and Dat...

引用

8th acm international conference on web search and data mining (wsdm)

作者： Gao, Bin Bian, Jiang Microsoft Res 13FBldg 25 Danling St Beijing 100080 Peoples R China

ISBN: (纸本)9781450333177

In recent years, deep learning has been a very hot topic in the machine learning community. It has brought break-through results in image classification and speech recognition. Most recently, researchers have also got many promising results in natural language processing using deep learning techniques. As machine learning techniques are widely used in the web search and data mining applications, many researchers and practitioners are studying the possibility of applying the recently-developed deep learning techniques into these applications. Some of them have made very promising progress, and thus it is a good time to hold a workshop to discuss and share the problems and progress in using deep learning techniques to improve web search and data mining tasks.

关键词： Machine learning deep learning information retrieval data mining

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To each his own: Personalized content selection based on text comprehensibility 12

To each his own: Personalized content selection based on tex...

引用

5th acm international conference on web search and data mining, wsdm 2012

作者： Tan, Chenhao Gabrilovich, Evgeniy Pang, Bo Cornell University Ithaca NY 14853 United States Yahoo Research 4301 Great America Parkway Santa Clara CA 95054 United States

ISBN: (纸本)9781450307475

Imagine a physician and a patient doing a search on antibiotic resistance. Or a chess amateur and a grandmaster conducting a search on Alekhine's Defence. Although the topic is the same, arguably the two users in each case will satisfy their information needs with very different texts. Yet today search engines mostly adopt the one-size-fits-all solution, where personalization is restricted to topical preference. We found that users do not uniformly prefer simple texts, and that the text comprehensibility level should match the user's level of preparedness. Consequently, we propose to model the comprehensibility of texts as well as the users' reading proficiency in order to better explain how different users choose content for further exploration. We also model topic-specific reading proficiency, which allows us to better explain why a physician might choose to read sophisticated medical articles yet simple descriptions of SLR cameras. We explore different ways to build user profiles, and use collaborative filtering techniques to overcome data sparsity. We conducted experiments on large-scale datasets from a major web search engine and a community question answering forum. Our findings confirm that explicitly modeling text comprehensibility can significantly improve content ranking (search results or answers, respectively). Copyright 2012 acm.

关键词： search engines

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A Noise-aware click model for web search 12

A Noise-aware click model for web search

引用

5th acm international conference on web search and data mining, wsdm 2012

作者： Chen, Weizhu Wang, Dong Zhang, Yuchen Chen, Zheng Singla, Adish Yang, Qiang Microsoft Research Asia China Microsoft BING Bellevue United States Hong Kong University of Science and Technolgy Hong Kong

ISBN: (纸本)9781450307475

Recent advances in click model have established it as an attractive approach to infer document relevance. Most of these advances consider the user click/skip behavior as binary events but neglect the context in which a click happens. We show that real click behavior in industrial search engines is often noisy and not always a good indication of relevance. For a considerable percentage of clicks, users select what turn out to be irrelevant documents and these clicks should not be directly used as evidence for relevance inference. thus in this paper, we put forward an observation that the relevance indication degree of a click is not a constant, but can be differentiated by user preferences and the context in which the user makes her click decision. In particular, to interpret the click behavior discriminatingly, we propose a Noise-aware Click Model (NCM) by characterizing the noise degree of a click, which indicates the quality of the click for inferring relevance. Specifically, the lower the click noise is, the more important the click is in its role for relevance inference. To verify the necessity of explicitly accounting for the uninformative noise in a user click, we conducted experiments on a billion-scale dataset. Extensive experimental results demonstrate that as compared with two state-of-the-art click models in web search, NCM can better interpret user click behavior and achieve significant improvements in terms of both perplexity and NDCG. Copyright 2012 acm.

关键词： search engines

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Beyond ten blue links: Enabling user click modeling in federated web search 12

Beyond ten blue links: Enabling user click modeling in feder...

引用

5th acm international conference on web search and data mining, wsdm 2012

作者： Chen, Danqi Chen, Weizhu Wang, Haixun Chen, Zheng Yang, Qiang Institute for Interdisciplinary Information Sciences Tsinghua University China Microsoft Research Asia Beijing China Hong Kong University of Science and Technolgy Hong Kong Hong Kong

ISBN: (纸本)9781450307475

Click models have been positioned as an effective approach to interpret user click behavior in search engines. Existing click models mostly focus on traditional web search that considers only ten homogeneous web HTML documents that appear on the first search-result page. However, in modern commercial search engines, more and more web search results are federated from multiple sources and contain non-HTML results returned by other heterogeneous vertical engines, such as video or image search engines. In this paper, we study user click behavior in federated search. We observed that user click behavior in federated search is highly different from that in traditional web search, making it difficult to interpret using existing click models. In response, we propose a novel federated click model (FCM) to interpret user click behavior in federated search. In particular, we take into considerations two new biases in FCM. the first comes from the observation that users tend to be attracted by vertical results and their visual attention on them may increase the examination probability of other nearby web results. the other illustrates that user click behavior on vertical results may lead to more clues of search relevance due to their presentation style in federated search. With these biases and an effective model to correct them, FCM is more accurate in characterizing user click behavior in federated search. Our extensive experimental results show that FCM can outperform other click models in interpreting user click behavior in federated search and achieve significant improvements in terms of both perplexity and log-likelihood. Copyright 2012 acm.

关键词： search engines

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