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Smoothing Click Counts for Aggregated Vertical Search [chapter]

Jangwon Seo, W. Bruce Croft, Kwang Hyun Kim, Joon Ho Lee
2011 Lecture Notes in Computer Science  
In this work, we focus on mitigating the negative effect of rank cut for aggregated vertical searches. We introduce a technique for smoothing click counts based on spectral graph analysis.  ...  Using real clickthrough data from a vertical recorded in an aggregated search environment, we show empirically that clickthrough data smoothed by this technique is effective for improving the vertical  ...  Acknowledgments This work was supported in part by the Center for Intelligent Information Retrieval, in part by NHN Corp. and in part by NSF grant #IIS-0711348.  ... 
doi:10.1007/978-3-642-20161-5_39 fatcat:3j74o4muc5d2nnez6cxighblty

Detecting Multilingual and Multi-Regional Query Intent in Web Search

Yi Chang, Ruiqiang Zhang, Srihari Reddy, Yan Liu
We introduce a query intent probabilistic model, whose input is the number of clicks on documents from different regions and in different language, while the output of this model is a smoothed probabilistic  ...  detection for 18%.  ...  (Li, Wang, and Acero 2008) use click graphs to improve coverage of query intent classifiers for vertical search applications, such as, product search and job search.  ... 
doi:10.1609/aaai.v25i1.8074 fatcat:mdifxctkmnh6xamhii7oy3dp5y

Evaluating aggregated search using interleaving

Aleksandr Chuklin, Anne Schuth, Katja Hofmann, Pavel Serdyukov, Maarten de Rijke
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
All this makes our proposed interleaving algorithm an essential tool for comparing IR systems with complex aggregated pages.  ...  Such a system is called an aggregated or federated search system. Because search engines evolve over time, their results need to be constantly evaluated.  ...  We thank Filip Radlinski for his detailed feedback on a draft of this paper.  ... 
doi:10.1145/2505515.2505698 dblp:conf/cikm/ChuklinSHSR13 fatcat:xpd5elvxvncllcygw7v67xwmwy

Improving local search ranking through external logs

Klaus Berberich, Arnd Christian König, Dimitrios Lymberopoulos, Peixiang Zhao
2011 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11  
We evaluate our techniques on both human-judged relevance data as well as click-through data from a commercial local search engine.  ...  The signals used for ranking in local search are very different from web search: in addition to (textual) relevance, measures of (geographic) distance between the user and the search result, as well as  ...  To assess these signals directly on the basis of click information derived from the local search vertical is very difficult, in part due to the position bias of the click signal [21] .  ... 
doi:10.1145/2009916.2010021 dblp:conf/sigir/BerberichKLZ11 fatcat:xoxuidl3bjbdfcvslusxm56ham

KDEIM at NTCIR-12 IMine-2 Search Intent Mining Task: Query Understanding through Diversified Ranking of Subtopics

Md Zia Ullah, Md Shajalal, Masaki Aono
2016 NTCIR Conference on Evaluation of Information Access Technologies  
We propose a method that extracts subtopics by leveraging the query suggestions from search engines.  ...  The best performance of our method achieves an I-rec of 0.7557, a D-nDCG of 0.6644, a D#-nDCG of 0.7100, and a QU-score of 0.5057 at the cutoff rank 10 for query understanding task.  ...  According to this intuition, we make use of search engine hit count to estimate features including normalized hit count (NHC), point-wise mutual information (PMI), and word cooccurrence (WC).  ... 
dblp:conf/ntcir/UllahSA16 fatcat:2kdz5biiwjfb5iawyzqgitxpau

Learning to Rank for Educational Search Engines

Arif Usta, Ismail Sengor Altingovde, Rifat Ozcan, Ozgur Ulusoy
2021 IEEE Transactions on Learning Technologies  
click count for a given query.  ...  models neither for a general Web search engine nor for a vertical.  ... 
doi:10.1109/tlt.2021.3075196 fatcat:buud677r3basli6e7qdkpevoqm

Learning to aggregate vertical results into web search results

Jaime Arguello, Fernando Diaz, Jamie Callan
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals, into the Web search results.  ...  Learning models to aggregate results from multiple verticals is associated with two major challenges.  ...  PROBLEM DEFINITION At query time, the aggregated search system issues the query to the Web search engine and to those verticals selected, which we refer to as the candidate verticals.  ... 
doi:10.1145/2063576.2063611 dblp:conf/cikm/ArguelloDC11 fatcat:x4bi7owrdbd65arqv4o643q6tu

Manual S01: Manual and example application to model 'Core-Areas' (Optimally Describing Isolines) using MapInfo & Vertical Mapper from Approaching prehistoric demography: proxies, scales and scope of the Cologne Protocol in European contexts

Isabell Schmidt, Johanna Hilpert, Inga Kretschmer, Robin Peters, Manuel Broich, Sara Schiesberg, Oliver Vogels, Karl Peter Wendt, Andreas Zimmermann, Andreas Maier
Set Nugget to 0.To facilitate the search for the first plateau, we can enlarge the Semivariogram View by clicking right on the diagram, select Maximize and zoom in.The maximised Semivariogram Views should  ...  Site aggregation follows the same procedure as described in Step 4: "Aggregation of vertices".  ... 
doi:10.6084/m9.figshare.13142915.v1 fatcat:42sbnzx4e5gqbdkk7kqv63lox4

Mining the search trails of surfing crowds

Mikhail Bilenko, Ryen W. White
2008 Proceeding of the 17th international conference on World Wide Web - WWW '08  
in learning to rank for Web search.  ...  We present heuristic and probabilistic algorithms that rely on such datasets for suggesting authoritative websites for search queries.  ...  Figure 1 : 1 Search trail example Figure 2 : 2 Random walks for search trailswhere n(di,tj) = q d i s.t.t j ∈q f (q di)is again the aggregated count for document di reached from queries containing term  ... 
doi:10.1145/1367497.1367505 dblp:conf/www/BilenkoW08 fatcat:4oi3iayanjh2bnoya4cw3ewn3u

On composition of a federated web search result page

Ashok Kumar Ponnuswami, Kumaresh Pattabiraman, Qiang Wu, Ran Gilad-Bachrach, Tapas Kanungo
2011 Proceedings of the fourth ACM international conference on Web search and data mining - WSDM '11  
these engines are then aggregated and composed into a search result page (SERP) and presented to the user.  ...  Modern web search engines are federated -a user query is sent to the numerous specialized search engines called verticals like web (text documents), News, Image, Video, etc. and the results returned by  ...  Perhaps some of the query classifier features also help in smoothing the models by aggregating over tail queries in specific categories.  ... 
doi:10.1145/1935826.1935922 dblp:conf/wsdm/PonnuswamiPWGK11 fatcat:bfyp4vic2zf5pka7f35o3stgte

Incorporating the surfing behavior of web users into pagerank

Shatlyk Ashyralyyev, B. Barla Cambazoglu, Cevdet Aykanat
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
Our empirical results indicate that the proposed model leads to better estimation of page importance according to an evaluation metric that relies on user click feedback obtained from web search query  ...  In large-scale commercial web search engines, estimating the importance of a web page is a crucial ingredient in ranking web search results.  ...  Figure 1 : 1 Visit count of a URL in the browsing data versus its click count in search results. Figure 2 : 2 Distribution of URL visit counts in toolbar data.  ... 
doi:10.1145/2505515.2505668 dblp:conf/cikm/AshyralyyevCA13 fatcat:m7iz6pnc35dbvaqwga466eabq4

Optimal rare query suggestion with implicit user feedback

Yang Song, Li-wei He
2010 Proceedings of the 19th international conference on World wide web - WWW '10  
Unlike the Rocchio algorithm, our learning process does not involve the content of the URLs but simply leverages the click and skip counts in the query-URL bipartite graphs.  ...  Consequently, our model is capable of scaling up to the need of commercial search engines.  ...  Essentially, the backward model can be treated as a nor- malization on the document clicks instead of query counts.  ... 
doi:10.1145/1772690.1772782 dblp:conf/www/SongH10 fatcat:b2m6gsugdfajddkzdaialokb2m

Extracting Events from Spatial Time Series

Gennady Andrienko, Natalia Andrienko, Martin Mladenov, Michael Mock, Christian Poelitz
2010 2010 14th International Conference Information Visualisation  
For example, data about visits of Web sites or use of various terms in Web searches are available in the form of counts by time intervals.  ...  The first one contains weekly aggregates of the Google search data relevant to flu 1 by the states of the USA starting from 28/09/2003.  ... 
doi:10.1109/iv.2010.17 dblp:conf/iv/AndrienkoAMMP10 fatcat:kua75hw7arhxfl7jgfr4cduh3i

A semi-supervised approach to modeling web search satisfaction

Ahmed Hassan
2012 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12  
We show that the proposed model outperforms previous methods for modeling search success using labeled data.  ...  Web search is an interactive process that involves actions from Web search users and responses from the search engine.  ...  Page views included query submission, search result clicks, navigation beyond the search results page originating from clicks on links in a search result, and clicks on other search engine features (e.g  ... 
doi:10.1145/2348283.2348323 dblp:conf/sigir/Hassan12 fatcat:3afjszmsjncixe5qoflhmcndjq

A task level metric for measuring web search satisfaction and its application on improving relevance estimation

Ahmed Hassan, Yang Song, Li-wei He
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
Understanding the behavior of satisfied and unsatisfied Web search users is very important for improving users search experience.  ...  We use our user satisfaction model to distinguish between clicks that lead to satisfaction and clicks that do not.  ...  We used smoothing to account for any data scarcity. We build two such models.  ... 
doi:10.1145/2063576.2063599 dblp:conf/cikm/HassanSH11 fatcat:pwvqpinlkjhuzhb3nxb7zosawy
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