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

共 80 条记录，以下是11-20 订阅

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wsdm'14 Workshop Summary Log-based Personalization: the 4th web search Click data (WSCD) workshop

WSDM'14 Workshop Summary Log-based Personalization: the 4th ...

引用

7th acm international conference on web search and data mining (wsdm)

作者： Serdyukov, Pavel Dupret, Georges Craswell, Nick Yandex Leo Tolstoy 16 Moscow Russia Yahoo Labs Sunnyvale CA USA Microsoft Corp Redmond WA USA

ISBN: (纸本)9781450323512

WSCD 2014 1 is the fourth workshop on web search Click data, following WSCD 2009, WSCD 2011, WSCD 2012 and WSCD 2013. It is a forum for new research relating to web search usage logs and for discussing desirable properties of publicly released search log datasets. Research relating to search logs has been hampered by the limited availability of click datasets. this series of workshops comes with new datasets based on logged user search behaviour and accompanying data mining challenges. this year the challenge and the workshop are focused on the tasks of personalization using logs.

关键词： web search web search, click data

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Sequence clustering and labeling for unsupervised query intent discovery 12

Sequence clustering and labeling for unsupervised query inte...

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

作者： Cheung, Jackie Chi Kit Li, Xiao Department of Computer Science University of Toronto Toronto ON Canada Facebook Inc. Palo Alto CA United States

ISBN: (纸本)9781450307475

One popular form of semantic search observed in several modern search engines is to recognize query patterns that trigger instant answers or domain-specific search, producing semantically enriched search results. this often requires understanding the query intent in addition to the meaning of the query terms in order to access structured data sources. A major challenge in intent understanding is to construct a domain-dependent schema and to annotate search queries based on such a schema, a process that to date has required much manual annotation effort. We present an unsupervised method for clustering queries with similar intent and for producing a pattern consisting of a sequence of semantic concepts and/or lexical items for each intent. Furthermore, we leverage the discovered intent patterns to automatically annotate a large number of queries beyond those used in clustering. We evaluated our method on 10 selected domains, discovering over 1400 intent patterns and automatically annotating 125K (and potentially many more) queries. We found that over 90% of patterns and 80% of instance annotations tested are judged to be correct by a majority of annotators. Copyright 2012 acm.

关键词： search engines

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Answers, not links: Extracting tips from Yahoo! Answers to address how-to web queries 12

Answers, not links: Extracting tips from Yahoo! Answers to a...

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

作者： weber, Ingmar Ukkonen, Antti Gionis, Aris Yahoo Research Barcelona Avda. Diagonal 177 E-08018 Barcelona Spain

ISBN: (纸本)9781450307475

We investigate the problem of mining "tips" from Yahoo! Answers and displaying those tips in response to related web queries. Here, a "tip" is a short, concrete and self-contained bit of non-obvious advice such as "To zest a lime if you don't have a zester : use a cheese grater." First, we estimate the volume of web queries with"how- to" intent, which could be potentially addressed by a tip. Second, we analyze how to detect such queries automatically without solely relying on literal "how to *" patterns. third, we describe how to derive potential tips automatically from Yahoo! Answers, and we develop machine-learning techniques to remove low-quality tips. Finally, we discuss how to match web queries with "how-to" intent to tips. We evaluate both the quality of these direct displays as well as the size of the query volume that can be addressed by serving tips. Copyright 2012 acm.

关键词： Lime

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Language models for keyword search over data graphs 12

Language models for keyword search over data graphs

引用

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

作者： Mass, Yosi Sagiv, Yehoshua IBM Haifa Research Lab. Haifa 31905 Israel Hebrew University Jerusalem 91904 Israel

ISBN: (纸本)9781450307475

In keyword search over data graphs, an answer is a nonredundant subtree that includes the given keywords. this paper focuses on improving the effectiveness of that type of search. A novel approach that combines language models with structural relevance is described. the proposed approach consists of three steps. First, language models are used to assign dynamic, query-dependent weights to the graph. those weights complement static weights that are pre-assigned to the graph. Second, an existing algorithm returns candidate answers based on their weights. third, the candidate answers are re-ranked by creating a language model for each one. the effectiveness of the proposed approach is verified on a benchmark of three datasets: IMDB, Wikipedia and Mondial. the proposed approach outperforms all existing systems on the three datasets, which is a testament to its robustness. It is also shown that the effectiveness can be further improved by augmenting keyword queries with very basic knowledge about the structure. Copyright 2012 acm.

关键词： Semantics

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MTrust: Discerning multi-faceted trust in a connected world 12

MTrust: Discerning multi-faceted trust in a connected world

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

作者： Tang, Jiliang Gao, Huiji Liu, Huan Computer Science and Engineering Arizona State University Tempe AZ 85281 United States

ISBN: (纸本)9781450307475

Traditionally, research about trust assumes a single type of trust between users. However, trust, as a social concept, inherently has many facets indicating multiple and heterogeneous trust relationships between users. Due to the presence of a large trust network for an online user, it is necessary to discern multi-faceted trust as there are naturally experts of different types. Our study in product review sites reveals that people place trust differently to different people. Since the widely used adjacency matrix cannot capture multi-faceted trust relationships between users, we propose a novel approach by incorporating these relationships into traditional rating prediction algorithms to reliably estimate their strengths. Our work results in interesting findings such as heterogeneous pairs of reciprocal links. Experimental results on real-world data from Epinions and Ciao show that our work of discerning multi-faceted trust can be applied to improve the performance of tasks such as rating prediction, facet-sensitive ranking, and status theory. Copyright 2012 acm.

关键词： data mining

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Probabilistic models for personalizing web search 12

Probabilistic models for personalizing Web search

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

作者： Sontag, David Collins-thompson, Kevyn Bennett, Paul N. White, Ryen W. Dumais, Susan Billerbeck, Bodo New York University New York United States Microsoft Research Redmond United States Bing Microsoft Melbourne Australia

ISBN: (纸本)9781450307475

We present a new approach for personalizing web search results to a specific user. Ranking functions for web search engines are typically trained by machine learning algorithms using either direct human relevance judgments or indirect judgments obtained from click-through data from millions of users. the rankings are thus optimized to this generic population of users, not to any specific user. We propose a generative model of relevance which can be used to infer the relevance of a document to a specific user for a search query. the user-specific parameters of this generative model constitute a compact user profile. We show how to learn these profiles from a user's long-term search history. Our algorithm for computing the personalized ranking is simple and has little computational overhead. We evaluate our personalization approach using historical search data from thousands of users of a major web search engine. Our findings demonstrate gains in retrieval performance for queries with high ambiguity, with particularly large improvements for acronym queries. Copyright 2012 acm.

关键词： search engines

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the 5th international workshop on social web for disaster management (SWDM'18): Collective sensing, trust, and resilience in global crises 18

The 5th international workshop on social web for disaster ma...

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11th acm international conference on web search and data mining, wsdm 2018

作者： Lin, Yu-Ru Castillo, Carlos Yin, Jie School of Computing and Information University of Pittsburgh PA United States Universitat Pompeu Fabra Barcelona Catalonia Spain CSIRO Epping NSW Australia

ISBN: (纸本)9781450355810

During large-scale emergencies such as natural and man-made disasters, a massive amount of information is posted by the public in social media. Collecting, aggregating, and presenting this information to stakeholders can be extremely challenging, particularly if an understanding of the "big picture" is sought. this international workshop, the fifth in the series, is a key venue for researchers and practitioners to discuss research challenges and technical issues around the usage of social media in disaster management. Workshop's website: https://***/site/swdm2018/. © 2018 Copyright held by the owner/author(s).

关键词： Social networking (online)

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Multi-relational matrix factorization using Bayesian personalized ranking for social network data 12

Multi-relational matrix factorization using Bayesian persona...

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

作者： Krohn-Grimberghe, Artus Drumond, Lucas Freudenthaler, Christoph Schmidt-thieme, Lars Information Systems and Machine Learning Lab. University of Hildesheim Germany

ISBN: (纸本)9781450307475

A key element of the social networks on the internet such as Facebook and Flickr is that they encourage users to create connections between themselves, other users and objects. One important task that has been approached in the literature that deals with such data is to use social graphs to predict user behavior (e.g. joining a group of interest). More specifically, we study the cold-start problem, where users only participate in some relations, which we will call social relations, but not in the relation on which the predictions are made, which we will refer to as target relations. We propose a formalization of the problem and a principled approach to it based on multi-relational factorization techniques. Furthermore, we derive a principled feature extraction scheme from the social data to extract predictors for a classifier on the target relation. Experiments conducted on real world datasets show that our approach outperforms current methods. Copyright 2012 acm.

关键词： Forecasting

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Learning to rank with multi-aspect relevance for vertical search 12

Learning to rank with multi-aspect relevance for vertical se...

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

作者： Kang, Changsung Wang, Xuanhui Chang, Yi Tseng, Belle Yahoo Labs Sunnyvale CA United States

ISBN: (纸本)9781450307475

Many vertical search tasks such as local search focus on specific domains. the meaning of relevance in these verticals is domain-specific and usually consists of multiple well-defined aspects (e.g., text matching and distance in local search). thus the overall relevance between a query and a document is a tradeoff between multiple relevance aspects. Such a tradeoff can vary for different types of queries or in different contexts. In this paper, we explore these vertical-specific aspects in the learning to rank setting. We propose a novel formulation in which the relevance between a query and a document is assessed with respect to each aspect, forming the multi-aspect relevance. In order to compute a ranking function, we study two types of learning-based approaches to estimate the tradeoff between these relevance aspects: a label aggregation method and a model aggregation method. Since there are only a few aspects, a minimal amount of training data is needed to learn the tradeoff. We conduct both offline and online test experiments on a local search engine and the experimental results show that our proposed multi-aspect relevance formulation is very promising. the two types of aggregation methods perform more effectively than a set of baseline methods including a conventional learning to rank method. Copyright 2012 acm.

关键词： search engines

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No search result left behind: Branching behavior with browser tabs 12

No search result left behind: Branching behavior with browse...

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

作者： Huang, Jeff Lin, thomas White, Ryen W. University of Washington Seattle WA 98195 United States Microsoft Research Redmond WA 98052 United States

ISBN: (纸本)9781450307475

Today's web browsers allow users to open links in new windows or tabs. this action, which we call 'branching', is sometimes performed on search results when the user plans to eventually visit multiple results. We detect branching behavior on a large commercial search engine with a client-side script on the results page. Two-fifths of all users spawned new tabs on search results in the timeframe of our study;branching usage varied with different query types and vertical. Both branching and backtracking are viable methods for visiting multiple search results. To understand user search strategies, we treat multiple result clicks following a query as ordered events to understand user search strategies. Users branching in a query are more likely to click search results from top to bottom, while users who backtrack are less likely to do so;this is especially true for queries involving more than two clicks. these findings inform an experiment in which we take a popular click model and modify it to account for the differing user behavior when branching. By understanding that users continue examining search results before viewing a branched result, we can improve the click model for branching queries. Copyright 2012 acm.

关键词： web browsers

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