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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31-40 订阅

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Pairwise cross-domain factor model for heterogeneous transfer ranking 12

Pairwise cross-domain factor model for heterogeneous transfe...

引用

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

作者： Long, Bo Chang, Yi Dong, Anlei He, Jianzhang Yahoo Labs United States

ISBN: (纸本)9781450307475

Learning to rank arises in many information retrieval applications, ranging from web search engine, online advertising to recommendation systems. Traditional ranking mainly focuses on one type of data source, and effective modeling relies on a sufficiently large number of labeled examples, which require expensive and time-consuming labeling process. However, in many real-world applications, ranking over multiple related heterogeneous domains becomes a common situation, where in some domains we may have a relatively large amount of training data while in some other domains we can only collect very little. theretofore, how to leverage labeled information from related heterogeneous domain to improve ranking in a target domain has become a problem of great interests. In this paper, we propose a novel probabilistic model, pairwise cross-domain factor model, to address this problem. the proposed model learns latent factors(features) for multi-domain data in partially-overlapped heterogeneous feature spaces. It is capable of learning homogeneous feature correlation, heterogeneous feature correlation, and pairwise preference correlation for cross-domain knowledge transfer. We also derive two PCDF variations to address two important special cases. Under the PCDF model, we derive a stochastic gradient based algorithm, which facilitates distributed optimization and is flexible to adopt different loss functions and regularization functions to accommodate different data distributions. the extensive experiments on real world data sets demonstrate the effectiveness of the proposed model and algorithm. Copyright 2012 acm.

关键词： search engines

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WSCD2013: Workshop on web search click data 2013 13

WSCD2013: Workshop on web search click data 2013

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6th acm international conference on web search and data mining, wsdm 2013

作者： Serdyukov, Pavel Dupret, Georges Craswell, Nick Yandex Leo Tolstoy 16 Moscow Russia Yahoo Labs. 701 First Avenue Sunnyvale United States Microsoft Corporation One Microsoft Way Redmond United States

ISBN: (纸本)9781450318693

WSCD 2013 is the third workshop on web search Click data, following WSCD 2009 and WSCD 2012. 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 workshop comes with a new dataset based on logged user search behaviour and an accompanying challenge to predict switches between search engines within a given search session. © 2013 Authors.

关键词： search engines

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Query suggestion by constructing term-transition graphs 12

Query suggestion by constructing term-transition graphs

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

作者： Song, Yang Zhou, Dengyong He, Li-Wei Microsoft Research One Microsoft Way Redmond WA 98052 United States

ISBN: (纸本)9781450307475

Query suggestion is an interactive approach for search engines to better understand users information need. In this paper, we propose a novel query suggestion framework which leverages user re-query feedbacks from search engine logs. Specifically, we mined user query reformulation activities where the user only modifies part of the query by (1) adding terms after the query, (2) deleting terms within the query, or (3) modifying terms to new terms. We build a term-transition graph based on the mined data. Two models are proposed which address topic-level and term-level query suggestions, respectively. In the first topic-based unsupervised Pagerank model, we perform random walk on each of the topic-based term-transition graph and calculate the Pagerank for each term within a topic. Given a new query, we suggest relevant queries based on its topic distribution and term-transition probability within each topic. Our second model resembles the supervised learning-to-rank (LTR) framework, in which term modifications are treated as documents so that each query reformulation is treated as a training instance. A rich set of features are constructed for each (query, document) pair from Pagerank, Wikipedia, Ngram, ODP and so on. this supervised model is capable of suggesting new queries on a term level which addresses the limitation of previous methods. Experiments are conducted on a large data set from a commercial search engine. By comparing the with state-of-the-art query suggestion methods [4, 2], our proposals exhibit significant performance increase for all categories of queries. Copyright 2012 acm.

关键词： search engines

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Incorporating revisiting behaviors into click models 12

Incorporating revisiting behaviors into click models

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

作者： Xu, Danqing Liu, Yiqun Zhang, Min Ma, Shaoping Ru, Liyun Department of Computer Science and Technology State Key Lab. of Intelligence Technology and Systems Tsinghua University Beijing 100084 China

ISBN: (纸本)9781450307475

Click-through behaviors are treated as invaluable sources of user feedback and they have been leveraged in several commercial search engines in recent years. However, estimating unbiased relevance is always a challenging task because of position bias. To solve this problem, many researchers have proposed a variety of assumptions to model click-through behaviors. Most of these models share the sequential examination hypothesis, which is that users examine search results from the top to the bottom. Nevertheless, this model cannot draw a complete picture of information-seeking behaviors. Many eye-tracking studies find that user interactions are not sequential but contain revisiting patterns. If a user clicks on a higher ranked document after having clicked on a lower-ranked one, we call this scenario a revisiting pattern, and we believe that the revisiting patterns are important signals regarding a user's click preferences. this paper incorporates revisiting behaviors into click models and introduces a novel click model named Temporal Hidden Click Model (thCM). this model dynamically models users' click behaviors with a temporal order. In our experiment, we collect over 115 million query sessions from a widely-used commercial search engine and then conduct a comparative analysis between our model and several state-of-the-art click models. the experimental results show that the thCM model achieves a significant improvement in the Normalized Discounted Cumulative Gain (NDCG), the click perplexity and click distributions metrics. Copyright 2012 acm.

关键词： search engines

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Beyond 100 million entities: Large-scale blocking-based resolution for heterogeneous data 12

Beyond 100 million entities: Large-scale blocking-based reso...

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

作者： Papadakis, George Ioannou, Ekaterini Niederée, Claudia Palpanas, themis Nejdl, Wolfgang National Technical University of Athens Greece Technical University of Crete Greece L3S Research Center Germany University of Trento Italy

ISBN: (纸本)9781450307475

A prerequisite for leveraging the vast amount of data available on the web is Entity Resolution, i.e., the process of identifying and linking data that describe the same real-world objects. To make this inherently quadratic process applicable to large data sets, blocking is typically employed: entities (records) are grouped into clusters - the blocks - of matching candidates and only entities of the same block are compared. However, novel blocking techniques are required for dealing with the noisy, heterogeneous, semi-structured, user-generated data in the web, as traditional blocking techniques are inapplicable due to their reliance on schema information. the introduction of redundancy, improves the robustness of blocking methods but comes at the price of additional computational cost. In this paper, we present methods for enhancing the eficiency of redundancy-bearing blocking methods, such as our attributeagnostic blocking approach. We introduce novel blocking schemes that build blocks based on a variety of evidences, including entity identifiers and relationships between entities;they significantly reduce the required number of comparisons, while maintaining blocking effectiveness at very high levels. We also introduce two theoretical measures that provide a reliable estimation of the performance of a blocking method, without requiring the analytical processing of its blocks. Based on these measures, we develop two techniques for improving the performance of blocking: combining individual, complementary blocking schemes, and purging blocks until given criteria are satisfied. We test our methods through an extensive experimental evaluation, using a voluminous data set with 182 million heterogeneous entities. the outcomes of our study show the applicability and the high performance of our approach. Copyright 2012 acm.

关键词： Redundancy

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Characterizing web content, user interests, and search behavior by reading level and topic 12

Characterizing Web content, user interests, and search behav...

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

作者： Kim, Jin Young Collins-thompson, Kevyn Bennett, Paul N. Dumais, Susan T. University of Massachusetts Amherst 140 Governors Drive Amherst MA 01003 United States Microsoft Research One Microsoft Way Redmond WA 98052 United States

ISBN: (纸本)9781450307475

A user's expertise or ability to understand a document on a given topic is an important aspect of that document's relevance. However, this aspect has not been well-explored in information retrieval systems, especially those at web scale where the great diversity of content, users, and tasks presents an especially challenging search problem. To help improve our modeling and understanding of this diversity, we apply automatic text classifiers, based on reading difficulty and topic prediction, to estimate a novel type of profile for important entities in web search - users, websites, and queries. these profiles capture topic and reading level distributions, which we then use in conjunction with search log data to characterize and compare different entities. We find that reading level and topic distributions provide an important new representation of web content and user interests, and that using both together is more effective than using either one separately. In particular we find that: 1) the reading level of web content and the diversity of visitors to a website can vary greatly by topic;2) the degree to which a user's profile matches with a site's profile is closely correlated with the user's preference of the website in search results, and 3) site or URL profiles can be used to predict 'expertness'- whether a given site or URL is oriented toward expert vs. non-expert users. Our findings provide strong evidence in favor of jointly incorporating reading level and topic distribution metadata into a variety of critical tasks in web information systems. Copyright 2012 acm.

关键词： websites

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Machine learning for query-document matching in search 12

Machine learning for query-document matching in search

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

作者： Li, Hang Xu, Jun Microsoft Research Asia No. 5 Danling street Beijing 100080 China

ISBN: (纸本)9781450307475

In web search, relevance is one of the most important factors to meet users' satisfaction, and the success of a web search engine heavily depends on its performance on relevance. It has been observed that many hard cases in search relevance are due to term mismatch between query and documnt (e.g., query 'ny times' does not match well with document only containing 'new york times'), and thus it is not exaggerated to say that dealing with mismatch between query and document is one of the most critical research problems in web search. Recently researchers have spent significant effort to address the grand challenge. the major approach is to conduct more query and document understanding, and perform better matching between enriched query and document representations. With the availability of large amount of log data and advanced machine learning techniques, this becomes more feasible and significant progress has been made recently. In this tutorial, we will give a systematic and detailed presentation on newly developed machine learning technologies for query document matching in search. We will focus on the fundamental problems, as well as the novel solutions for query document matching at word form level, word sense level, topic level, and structure level. We will talk about novel technologies about query spelling error correction [3, 13], query rewriting [1, 4, 6, 7], query classification [2], topic modeling of documents [5, 9], query document matching [8, 10, 11, 12], and query document-title translation. the ideas and solutions introduced in this tutorial may motivate industrial practitioners to turn the research fruits into product reality. the summary of the state-of-the-art methods and the discussions on the technical issues in this tutorial may stimulate academic researchers to find new research directions and solutions. Matching between query and document is not limited to search, and similar problems can be observed at online advertisement, recommendation syst

关键词： search engines

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Relational click prediction for sponsored search 12

Relational click prediction for sponsored search

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

作者： Xiong, Chenyan Wang, Taifeng Ding, Wenkui Shen, Yidong Liu, Tie-Yan Graduate University Chinese Academy of Sciences Beijing China Microsoft Research Asia Beijing China Tsinghua University Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450307475

this paper is concerned with the prediction of clicking an ad in sponsored search. the accurate prediction of user's click on an ad plays an important role in sponsored search, because it is widely used in both ranking and pricing of the ads. Previous work on click prediction usually takes a single ad as input, and ignores its relationship to the other ads shown in the same page. this independence assumption here, however, might not be valid in the real scenario. In this paper, we first perform an analysis on this issue by looking at the click-through rates (CTR) of the same ad, in the same position and for the same query, but surrounded by different ads. We found that in most cases the CTR varies largely, which suggests that the relationship between ads is really an important factor in predicting click probability. Furthermore, our investigation shows that the more similar the surrounding ads are to an ad, the lower the CTR of the ad is. Based on this observation, we design a continuous conditional random fields (CRF) based model for click prediction, which considers both the features of an ad and its similarity to the surrounding ads. We show that the model can be effectively learned using maximum likelihood estimation, and can also be efficiently inferred due to its closed form solution. Our experimental results on the click-through log from a commercial search engine show that the proposed model can predict clicks more accurately than previous independent models. To our best knowledge this is the first work that predicts ad clicks by considering the relationship between ads. Copyright 2012 acm.

关键词： Forecasting

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Exploring social influence via posterior effect of word-of-mouth recommendations 12

Exploring social influence via posterior effect of word-of-m...

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

作者： Huang, Junming Cheng, Xue-Qi Shen, Hua-Wei Zhou, Tao Jin, Xiaolong Institute of Computing Technology Chinese Academy of Sciences Beijing China Web Sciences Center University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781450307475

Word-of-mouth has proven an effective strategy for promoting products through social relations. Particularly, existing studies have convincingly demonstrated that word-ofmouth recommendations can boost users' prior expectation and hence encourage them to adopt a certain innovation, such as buying a book or watching a movie. However, less attention has been paid to studying the posterior effect of word-of-mouth recommendations, i.e., whether or not wordof-mouth recommendations can influence users' posterior evaluation on the products or services recommended to them, the answer to which is critical to estimating user satisfaction when proposing a word-of-mouth marketing strategy. In order to fill this gap, in this paper we empirically study the above issue and verify that word-of-mouth recommendations are strongly associated with users' posterior evaluation. through elaborately designed statistical hypothesis tests we prove the causality that word-of-mouth recommendations directly prompt the posterior evaluation of receivers. Finally, we propose a method for investigating users' social influence, namely, their ability to affect followers' posterior evaluation via word-of-mouth recommendations, by examining the number of their followers and their sensitivity of discovering good items. the experimental results on real datasets show that our method can successfully identify 78% influential friends with strong social influence. Copyright 2012 acm.

关键词： Economic and social effects

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CharmBana: Progressive Responses with Real-Time Internet search for Knowledge-Powered Conversations 24

CharmBana: Progressive Responses with Real-Time Internet Sea...

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17th acm international conference on web search and data mining (wsdm)

作者： Reddy, Revanth Gangi Chandra, Sharath Bai, Hao Yao, Wentao Sidhu, Mankeerat Aggarwal, Karan Sonawane, Prathamesh Zhai, ChengXiang UIUC Champaign IL 61820 USA

ISBN: (纸本)9798400703713

Chatbots are often hindered by the latency associated with integrating real-time web search results, compromising user experience. To overcome this, we present CharmBana, an innovative social chatbot that introduces the use of progressive response generation to effortlessly blend search results into the bot's responses, while ensuring low response latency. the use of progressive responses is especially beneficial for voice-based chatbots, where the preliminary response buys time for a detailed follow-up, ensuring a smooth user interaction. As a result, our method not only cuts down user waiting times by 50% but also generates more relevant, precise, and engaging search inquiries. When tested in the Alexa Prize Socialbot Grand Challenge 5, our chatbot employing progressive responses consistently received higher user ratings.

关键词： Open-Domain Dialog search-Based Assistants Query Generation

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