Stem click$ (all stems)

120 papers:

CHI-2015-HaqueNV #named #using

Myopoint: Pointing and Clicking Using Forearm Mounted Electromyography and Inertial Motion Sensors (FH, MN, DV), pp. 3653–3656.

DUXU-IXD-2015-Posetti #comprehension #design #experience #user interface

Clicking Through Endless Seas: Understanding User Experience in the Design of Journalistic Websites (BP), pp. 82–93.

KDD-2015-LalmasLSST #experience

Promoting Positive Post-Click Experience for In-Stream Yahoo Gemini Users (ML, JL, GS, FS, GT), pp. 1929–1938.

KDD-2015-LiLMWP #predict #timeline #twitter

Click-through Prediction for Advertising in Twitter Timeline (CL, YL, QM, DW, SP), pp. 1959–1968.

MLDM-2015-SejalSTAVIP #graph #query #similarity

Query Click and Text Similarity Graph for Query Suggestions (DS, KGS, VT, DA, KRV, SSI, LMP), pp. 328–341.

SIGIR-2015-Barreda-Angeles #behaviour #latency

Unconscious Physiological Effects of Search Latency on Users and Their Click Behaviour (MBÁ, IA, XB, BBC, APB), pp. 203–212.

SIGIR-2015-ChuklinMR #information retrieval #modelling #tutorial

Advanced Click Models and their Applications to IR: SIGIR 2015 Tutorial (AC, IM, MdR), pp. 1111–1112.

SIGIR-2015-ChuklinMR15a #modelling #tutorial #web

An Introduction to Click Models for Web Search: SIGIR 2015 Tutorial (AC, IM, MdR), pp. 1113–1115.

SIGIR-2015-WangLWZNM #behaviour #modelling

Incorporating Non-sequential Behavior into Click Models (CW, YL, MW, KZ, JYN, SM), pp. 283–292.

CHI-2014-XiaoLH #smarttech

Expanding the input expressivity of smartwatches with mechanical pan, twist, tilt and click (RX, GL, CH), pp. 193–196.

CIKM-2014-MarkovKNSRC #effectiveness #metric #modelling

Vertical-Aware Click Model-Based Effectiveness Metrics (IM, EK, VN, PS, MdR, FC), pp. 1867–1870.

CIKM-2014-YeLQPM #generative

A Generative Model for Generating Relevance Labels from Human Judgments and Click-Logs (XY, JL, ZQ, BP, DM), pp. 1907–1910.

ECIR-2014-HofmannSBR #bias #evaluation #recommendation

Effects of Position Bias on Click-Based Recommender Evaluation (KH, AS, AB, MdR), pp. 624–630.

ECIR-2014-SchuthSWR #case study #optimisation #using

Optimizing Base Rankers Using Clicks — A Case Study Using BM25 (AS, FS, SW, MdR), pp. 75–87.

KDD-2014-TagamiHTOTT

Filling context-ad vocabulary gaps with click logs (YT, TH, YT, SO, KT, AT), pp. 1955–1964.

RecSys-2014-YiHZLR #personalisation

Beyond clicks: dwell time for personalization (XY, LH, EZ, NNL, SR), pp. 113–120.

SIGIR-2014-ChuklinZSSR #modelling

Evaluating intuitiveness of vertical-aware click models (AC, KZ, AS, FS, MdR), pp. 1075–1078.

SIGIR-2014-JiangHA #behaviour

Searching, browsing, and clicking in a search session: changes in user behavior by task and over time (JJ, DH, JA), pp. 607–616.

SIGIR-2014-KimHWZ #predict

Comparing client and server dwell time estimates for click-level satisfaction prediction (YK, AHA, RWW, IZ), pp. 895–898.

SIGIR-2014-LeeTC #behaviour #multi #risk management

Characterizing multi-click search behavior and the risks and opportunities of changing results during use (CJL, JT, SdlC), pp. 515–524.

SIGIR-2014-LiDWDCZ #2d #query

A two-dimensional click model for query auto-completion (YL, AD, HW, HD, YC, CZ), pp. 455–464.

SIGIR-2014-PanYMLNR #image #learning

Click-through-based cross-view learning for image search (YP, TY, TM, HL, CWN, YR), pp. 717–726.

SIGIR-2014-SunL #towards

Towards context-aware search with right click (AS, CHL), pp. 847–850.

CHI-2013-KukkaOKGO #interactive #visual notation #what

What makes you click: exploring visual signals to entice interaction on public displays (HK, HO, VK, JG, TO), pp. 1699–1708.

HCI-UC-2013-BeldadS #experience #ll #online #trust

It Was Nice with the Brick So Now I’ll Click: The Effects of Offline and Online Experience, Perceived Benefits, and Trust on Dutch Consumers’ Online Repeat Purchase Intention (AB, MS), pp. 371–380.

ICEIS-v1-2013-LaporteDM #documentation #multi #web

Multiple Clicks Model for Web Search of Multi-clickable Documents (LL, SD, JM), pp. 298–303.

CIKM-2013-ChuklinSR #modelling

Modeling clicks beyond the first result page (AC, PS, MdR), pp. 1217–1220.

CIKM-2013-KharitonovMSO #using

Using historical click data to increase interleaving sensitivity (EK, CM, PS, IO), pp. 679–688.

CIKM-2013-XingLNZMZ #modelling

Incorporating user preferences into click models (QX, YL, JYN, MZ, SM, KZ), pp. 1301–1310.

ECIR-2013-SaadK #adaptation #intranet #navigation

Exploiting Click Logs for Adaptive Intranet Navigation (SZS, UK), pp. 792–795.

KDD-2013-BaiJS #automation #generative #set

Exploiting user clicks for automatic seed set generation for entity matching (XB, FPJ, SHS), pp. 980–988.

KDD-2013-McMahanHSYEGNPDGCLWHBK #predict

Ad click prediction: a view from the trenches (HBM, GH, DS, MY, DE, JG, LN, TP, ED, DG, SC, DL, MW, AMH, TB, JK), pp. 1222–1230.

KDD-2013-WangBLZL #predict

Psychological advertising: exploring user psychology for click prediction in sponsored search (TW, JB, SL, YZ, TYL), pp. 563–571.

SIGIR-2013-ChuklinSR #information retrieval #metric #modelling

Click model-based information retrieval metrics (AC, PS, MdR), pp. 493–502.

SIGIR-2013-Ekstrand-AbuegPKSYI #automation #evaluation

Exploring semi-automatic nugget extraction for Japanese one click access evaluation (MEA, VP, MPK, TS, TY, MI), pp. 749–752.

SIGIR-2013-JagarlamudiG #modelling #web

Modeling click-through based word-pairs for web search (JJ, JG), pp. 483–492.

SIGIR-2013-KatoSYI #bound #evaluation #robust

Report from the NTCIR-10 1CLICK-2 Japanese subtask: baselines, upperbounds and evaluation robustness (MPK, TS, TY, MI), pp. 753–756.

SIGIR-2013-WangLZMZQZ #modelling

Incorporating vertical results into search click models (CW, YL, MZ, SM, MZ, JQ, KZ), pp. 503–512.

DRR-2012-NagyT #named #performance #verification

VeriClick: an efficient tool for table format verification (GN, MT).

VLDB-2012-HallBBGN

Processing a Trillion Cells per Mouse Click (AH, OB, RB, SG, MN), pp. 1436–1446.

CHI-2012-FourneyLMT #documentation #exclamation #interface #online #quote

“Then click ok!”: extracting references to interface elements in online documentation (AF, BL, RM, MAT), pp. 35–38.

CIKM-2012-AshkanC #analysis #behaviour #modelling

Modeling browsing behavior for click analysis in sponsored search (AA, CLAC), pp. 2015–2019.

CIKM-2012-AzimiZZNMF #visual notation

Visual appearance of display ads and its effect on click through rate (JA, RZ, YZ, VN, JM, XF), pp. 495–504.

CIKM-2012-DuanKZ #empirical #query #representation

Click patterns: an empirical representation of complex query intents (HD, EK, CZ), pp. 1035–1044.

CIKM-2012-HofmannWR #comparison

Estimating interleaved comparison outcomes from historical click data (KH, SW, MdR), pp. 1779–1783.

CIKM-2012-KolesnikovLT #predict

Predicting CTR of new ads via click prediction (AK, YL, VT), pp. 2547–2550.

CIKM-2012-MendesMZB #graph #similarity #using

Measuring website similarity using an entity-aware click graph (PNM, PM, HZ, RB), pp. 1697–1701.

CIKM-2012-WangC #learning #predict #word

Learning to predict the cost-per-click for your ad words (CJW, HHC), pp. 2291–2294.

CIKM-2012-WuJZ #behaviour

Serial position effects of clicking behavior on result pages returned by search engines (MW, SJ, YZ), pp. 2411–2414.

CIKM-2012-XinKALH #design #modelling

Do ads compete or collaborate?: designing click models with full relationship incorporated (XX, IK, RA, MRL, HH), pp. 1839–1843.

ECIR-2012-LungleyKS #adaptation #domain model #interactive #learning #modelling #web

Learning Adaptive Domain Models from Click Data to Bootstrap Interactive Web Search (DL, UK, DS), pp. 527–530.

KDD-2012-ChengZAMZZN #multi #predict

Multimedia features for click prediction of new ads in display advertising (HC, RvZ, JA, EM, RZ, YZ, VN), pp. 777–785.

KDD-2012-ChenY #predict

Position-normalized click prediction in search advertising (YC, TWY), pp. 795–803.

SIGIR-2012-AgarwalCEW #online #personalisation #recommendation

Personalized click shaping through lagrangian duality for online recommendation (DA, BCC, PE, XW), pp. 485–494.

SIGIR-2012-ChinS #analysis

Survival analysis of click logs (SCC, WNS), pp. 1149–1150.

SIGIR-2012-KoumpouriS #effectiveness #feedback #query #retrieval

Queries without clicks: evaluating retrieval effectiveness based on user feedback (AK, VS), pp. 1133–1134.

SIGIR-2012-Sakai #evaluation #information retrieval #mobile #towards #what

Towards zero-click mobile IR evaluation: knowing what and knowing when (TS), pp. 1157–1158.

SIGIR-2012-WeiLZMRZ #novel #web

Fighting against web spam: a novel propagation method based on click-through data (CW, YL, MZ, SM, LR, KZ), pp. 395–404.

CHI-2011-HuangWD #problem #using

No clicks, no problem: using cursor movements to understand and improve search (JH, RWW, STD), pp. 1225–1234.

CIKM-2011-CuiLYJJHGCD #framework #multi #random

Multi-view random walk framework for search task discovery from click-through log (JC, HL, JY, LJ, RJ, JH, YG, ZC, XD), pp. 135–140.

CIKM-2011-HofmannWR #probability

A probabilistic method for inferring preferences from clicks (KH, SW, MdR), pp. 249–258.

CIKM-2011-KimQYL #approach #behaviour

Advertiser-centric approach to understand user click behavior in sponsored search (SK, TQ, HY, TYL), pp. 2121–2124.

CIKM-2011-KiselevaAB #case study #mining #query

Mining query structure from click data: a case study of product queries (JK, EA, DB), pp. 2217–2220.

CIKM-2011-LeeC #collaboration #generative

Collaborative blacklist generation via searches-and-clicks (LHL, HHC), pp. 2153–2156.

CIKM-2011-LymberopoulosZKBL #mobile #predict

Location-aware click prediction in mobile local search (DL, PZ, ACK, KB, JL), pp. 413–422.

CIKM-2011-SakaiKS #information management

Click the search button and be happy: evaluating direct and immediate information access (TS, MPK, YIS), pp. 621–630.

CIKM-2011-YiA #query #web

Discovering missing click-through query language information for web search (XY, JA), pp. 153–162.

ECIR-2011-SeoCKL

Smoothing Click Counts for Aggregated Vertical Search (JS, WBC, KHK, JHL), pp. 387–398.

KDD-2011-AgarwalCEW #multi

Click shaping to optimize multiple objectives (DA, BCC, PE, XW), pp. 132–140.

KDD-2011-ZhangCWY #behaviour #comprehension #modelling #predict

User-click modeling for understanding and predicting search-behavior (YZ, WC, DW, QY), pp. 1388–1396.

HT-2010-MeissGRFM #navigation #topic #web

Agents, bookmarks and clicks: a topical model of web navigation (MM, BG, JJR, AF, FM), pp. 229–234.

HT-2010-MitzlaffBSH #analysis #network

Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy (FM, DB, GS, AH), pp. 265–270.

CIKM-2010-PuneraM #behaviour #information management #modelling #web

The anatomy of a click: modeling user behavior on web information systems (KP, SM), pp. 989–998.

CIKM-2010-SushmitaJLV #behaviour #interface

Factors affecting click-through behavior in aggregated search interfaces (SS, HJ, ML, RV), pp. 519–528.

CIKM-2010-WangCWZH #modelling #ranking

Explore click models for search ranking (DW, WC, GW, YZ, BH), pp. 1417–1420.

CIKM-2010-ZhangWWCZHZ #learning #modelling

Learning click models via probit bayesian inference (YZ, DW, GW, WC, ZZ, BH, LZ), pp. 439–448.

ICML-2010-GraepelCBH #predict

Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine (TG, JQC, TB, RH), pp. 13–20.

RecSys-2010-ZhengWZLY #case study #empirical #recommendation

Do clicks measure recommendation relevancy?: an empirical user study (HZ, DW, QZ, HL, TY), pp. 249–252.

SIGIR-2010-BillerbeckDFIK #retrieval

Exploiting click-through data for entity retrieval (BB, GD, CSF, TI, RK), pp. 803–804.

SIGIR-2010-DaveV #learning

Learning the click-through rate for rare/new ads from similar ads (KSD, VV), pp. 897–898.

SIGIR-2010-HillardL #documentation #retrieval

Clicked phrase document expansion for sponsored search ad retrieval (DH, CL), pp. 799–800.

SIGIR-2010-HofmannHBR #evaluation #retrieval

Comparing click-through data to purchase decisions for retrieval evaluation (KH, BH, MB, MdR), pp. 761–762.

SIGIR-2010-XuMC

Temporal click model for sponsored search (WX, EM, ECP), pp. 106–113.

SIGIR-2010-YueGCZJ #evaluation #learning #retrieval #statistics

Learning more powerful test statistics for click-based retrieval evaluation (YY, YG, OC, YZ, TJ), pp. 507–514.

SIGIR-2010-ZhongWWCZCW #behaviour

Incorporating post-click behaviors into a click model (FZ, DW, GW, WC, YZ, ZC, HW), pp. 355–362.

CIKM-2009-AnastasakosHKR #approach #collaboration #graph #recommendation #using

A collaborative filtering approach to ad recommendation using the query-ad click graph (TA, DH, SK, HR), pp. 1927–1930.

CIKM-2009-BeckerBGJP #web #what

What happens after an ad click?: quantifying the impact of landing pages in web advertising (HB, AZB, EG, VJ, BP), pp. 57–66.

CIKM-2009-CenLZZRM

Exploring relevance for clicks (RC, YL, MZ, BZ, LR, SM), pp. 1847–1850.

CIKM-2009-KimPDG #classification #graph #web

Improving web page classification by label-propagation over click graphs (SMK, PP, LD, SG), pp. 1077–1086.

ECIR-2009-BidokiT #documentation

Combination of Documents Features Based on Simulated Click-through Data (AMZB, JAT), pp. 538–545.

KDD-2009-XuYL #mining #using

Named entity mining from click-through data using weakly supervised latent dirichlet allocation (GX, SHY, HL), pp. 1365–1374.

SIGIR-2009-ChangDLZ #ranking #topic

Enhancing topical ranking with preferences from click-through data (YC, AD, CL, ZZ), pp. 666–667.

SIGIR-2009-DengKL #graph #modelling #query #representation

Entropy-biased models for query representation on the click graph (HD, IK, MRL), pp. 339–346.

SIGIR-2009-JiZLZXCSZ #ranking

Global ranking by exploiting user clicks (SJ, KZ, CL, ZZ, GRX, OC, GS, HZ), pp. 35–42.

SIGIR-2009-KonigGW #predict #query

Click-through prediction for news queries (ACK, MG, QW), pp. 347–354.

SIGIR-2009-MacdonaldW

Usefulness of click-through data in expert search (CM, RWW), pp. 816–817.

DAC-2008-SauerGL #framework #functional #modelling #named #performance #using

SystemClick: a domain-specific framework for early exploration using functional performance models (CS, MG, HPL), pp. 480–485.

VLDB-2008-AntonellisGC #analysis #graph #query

Simrank++: query rewriting through link analysis of the click graph (IA, HGM, CCC), pp. 408–421.

CIKM-2008-DouSYW #learning #question #ranking #web

Are click-through data adequate for learning web search rankings? (ZD, RS, XY, JRW), pp. 73–82.

CIKM-2008-PobleteCG #graph

Dr. Searcher and Mr. Browser: a unified hyperlink-click graph (BP, CC, AG), pp. 1123–1132.

ECIR-2008-ScholerSBT #using

Using Clicks as Implicit Judgments: Expectations Versus Observations (FS, MS, BB, AT), pp. 28–39.

KDD-2008-CaoJPHLCL #mining #query

Context-aware query suggestion by mining click-through and session data (HC, DJ, JP, QH, ZL, EC, HL), pp. 875–883.

SIGIR-2008-DupretP #predict

A user browsing model to predict search engine click data from past observations (GD, BP), pp. 331–338.

SIGIR-2008-LiWA #graph #learning #query

Learning query intent from regularized click graphs (XL, YYW, AA), pp. 339–346.

SIGIR-2008-WuTZ #community

Aggregated click-through data in a homogeneous user community (MW, AT, JZ), pp. 731–732.

CHI-2007-ObendorfWHM #web

Web page revisitation revisited: implications of a long-term click-stream study of browser usage (HO, HW, EH, MM), pp. 597–606.

HCI-IDU-2007-FujiokaTKO #coordination #detection #problem #usability

Tool for Detecting Webpage Usability Problems from Mouse Click Coordinate Logs (RF, RT, YK, HO), pp. 438–445.

OCSC-2007-Shen #interface #multi #social

From Clicks to Touches: Enabling Face-to-Face Shared Social Interface on Multi-touch Tabletops (CS), pp. 169–175.

CIKM-2007-PiwowarskiZ #modelling #predict

Predictive user click models based on click-through history (BP, HZ), pp. 175–182.

SIGIR-2007-CraswellS #graph #random

Random walks on the click graph (NC, MS), pp. 239–246.

KDD-2006-ZhaoLBM #detection #evolution

Event detection from evolution of click-through data (QZ, TYL, SSB, WYM), pp. 484–493.

CHI-2005-KaurH #behaviour #comparison #predict

A comparison of LSA, wordNet and PMI-IR for predicting user click behavior (IK, AJH), pp. 51–60.

KDD-2005-ZakiPAS #algorithm #category theory #clustering #dataset #effectiveness #mining #named

CLICKS: an effective algorithm for mining subspace clusters in categorical datasets (MJZ, MP, IA, TS), pp. 736–742.

CIKM-2004-XueZCYMXF #optimisation #using #web

Optimizing web search using web click-through data (GRX, HJZ, ZC, YY, WYM, WX, WF), pp. 118–126.

ECIR-2003-HeeschR #feedback #image #question #retrieval #what

Relevance Feedback for Content-Based Image Retrieval: What Can Three Mouse Clicks Achieve? (DH, SMR), pp. 363–376.

KDD-2003-GunduzO #behaviour #modelling #predict #representation #web

A Web page prediction model based on click-stream tree representation of user behavior (SG, MTÖ), pp. 535–540.

CHI-1999-ByrneADM #eye tracking #visual notation

Eye Tracking the Visual Search of Click-Down Menus (MDB, JRA, SD, MM), pp. 402–409.

SOSP-1999-MorrisKJK #composition

The Click modular router (RM, EK, JJ, MFK), pp. 217–231.

ICPR-1998-ReesASCSBB #interactive #named

CLICK-IT: interactive television highlighter for sports action replay (DR, JIA, NS, FC, SS, MdB, AB), pp. 1484–1487.

HCI-CC-1997-MamiyaHSK

A New Way to Overcome the Uneasy Operation of Touch-Sensitive Displays by Incorporating “Click” Mechanism CC Switch (MM, HH, YS, MK), pp. 619–622.