BibSLEIGH
BibSLEIGH corpus
BibSLEIGH tags
BibSLEIGH bundles
BibSLEIGH people
CC-BY
Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
email twitter
Used together with:
recommend (46)
base (33)
model (22)
user (18)
use (18)

Stem item$ (all stems)

152 papers:

SIGMODSIGMOD-2015-NaziDD #bibliography #web
The TagAdvisor: Luring the Lurkers to Review Web Items (AN, MD, GD), pp. 531–543.
ITiCSEITiCSE-2015-BergesH #evaluation #source code
Evaluation of Source Code with Item Response Theory (MB, PH), pp. 51–56.
STOCSTOC-2015-ColeG #approximate #nash #social
Approximating the Nash Social Welfare with Indivisible Items (RC, VG), pp. 371–380.
CSCWCSCW-2015-ChangHT #recommendation #using
Using Groups of Items to Bootstrap New Users in Recommender Systems (SC, FMH, LGT), pp. 1258–1269.
ECIRECIR-2015-HagenWS #corpus #topic #web
A Corpus of Realistic Known-Item Topics with Associated Web Pages in the ClueWeb09 (MH, DW, BS), pp. 513–525.
ECIRECIR-2015-WangHS0W0 #network #problem #recommendation #social #towards
Toward the New Item Problem: Context-Enhanced Event Recommendation in Event-Based Social Networks (ZW, PH, LS, KC, SW, GC), pp. 333–338.
KDDKDD-2015-VanchinathanMRK
Discovering Valuable items from Massive Data (HPV, AM, CAR, DK, AK), pp. 1195–1204.
KDDKDD-2015-WangYCSSZ #generative #named #recommendation
Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation (WW, HY, LC, YS, SWS, XZ), pp. 1255–1264.
RecSysRecSys-2015-AharonAADGS #named #recommendation
ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations (MA, OA, NAE, DDC, SG, OS), pp. 83–90.
RecSysRecSys-2015-BarjastehFMER #recommendation
Cold-Start Item and User Recommendation with Decoupled Completion and Transduction (IB, RF, FM, AHE, HR), pp. 91–98.
RecSysRecSys-2015-ChaneyBE #network #personalisation #probability #recommendation #social #using
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (AJBC, DMB, TER), pp. 43–50.
RecSysRecSys-2015-SeminarioW #collaboration #recommendation
Nuke ’Em Till They Go: Investigating Power User Attacks to Disparage Items in Collaborative Recommenders (CES, DCW), pp. 293–296.
SIGIRSIGIR-2015-GuyLDB #case study #enterprise #recommendation #social
Islands in the Stream: A Study of Item Recommendation within an Enterprise Social Stream (IG, RL, TD, EB), pp. 665–674.
SACSAC-2015-Chaudhary #experience #recommendation
Experience in item based recommender system (AC), pp. 1112–1114.
SACSAC-2015-DAddioM #approach #collaboration #sentiment
A sentiment-based item description approach for kNN collaborative filtering (RMD, MGM), pp. 1060–1065.
SACSAC-2015-LommatzschA #realtime #recommendation
Real-time recommendations for user-item streams (AL, SA), pp. 1039–1046.
DATEDATE-2014-ZhangLHCW #multi #performance #predict
Joint Virtual Probe: Joint exploration of multiple test items’ spatial patterns for efficient silicon characterization and test prediction (SZ, FL, CKH, KTC, HW), pp. 1–6.
VLDBVLDB-2015-ThirumuruganathanRAD14 #mining
Beyond Itemsets: Mining Frequent Featuresets over Structured Items (ST, HR, SA, GD), pp. 257–268.
HCIHIMI-DE-2014-HoriguchiASN #dependence #generative
Menu Hierarchy Generation Based on Syntactic Dependency Structures in Item Descriptions (YH, SA, TS, HN), pp. 157–166.
HCIHIMI-DE-2014-LinKT #analysis #collaboration #design #learning
A Learning Method for Product Analysis in Product Design — Learning Method of Product Analysis Utilizing Collaborative Learning and a List of Analysis Items (HL, HK, TT), pp. 503–513.
CIKMCIKM-2014-LimCK #data type #performance
Fast, Accurate, and Space-efficient Tracking of Time-weighted Frequent Items from Data Streams (YL, JC, UK), pp. 1109–1118.
CIKMCIKM-2014-XuLL #bibliography #clustering #collaboration #community
Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups (YX, WL, TL), pp. 251–260.
KDDKDD-2014-LiL #quality #recommendation
Matching users and items across domains to improve the recommendation quality (CYL, SDL), pp. 801–810.
RecSysRecSys-2014-Liu0L #recommendation
Recommending user generated item lists (YL, MX, LVSL), pp. 185–192.
RecSysRecSys-2014-SaveskiM #learning #recommendation
Item cold-start recommendations: learning local collective embeddings (MS, AM), pp. 89–96.
RecSysRecSys-2014-SeminarioW #recommendation
Attacking item-based recommender systems with power items (CES, DCW), pp. 57–64.
RecSysRecSys-2014-Sharma #modelling #people #social
Modeling the effect of people’s preferences and social forces on adopting and sharing items (AS), pp. 421–424.
RecSysRecSys-2014-VargasC #recommendation
Improving sales diversity by recommending users to items (SV, PC), pp. 145–152.
SIGIRSIGIR-2014-HeGKLS #predict #web
Predicting the popularity of web 2.0 items based on user comments (XH, MG, MYK, YL, KS), pp. 233–242.
SIGIRSIGIR-2014-Ifada #modelling #personalisation #recommendation #topic #using
A tag-based personalized item recommendation system using tensor modeling and topic model approaches (NI), p. 1280.
SIGIRSIGIR-2014-QiuCYLL #learning #personalisation #ranking
Item group based pairwise preference learning for personalized ranking (SQ, JC, TY, CL, HL), pp. 1219–1222.
SACSAC-2014-WangMLG #recommendation #social
Recommendation based on weighted social trusts and item relationships (DW, JM, TL, LG), pp. 254–259.
CASECASE-2013-WangJLC #capacity #policy
Integrated capacity allocation policies for a production service system with two-class customers and items (KW, ZJ, GL, JC), pp. 83–88.
MSRMSR-2013-MukherjeeG #question
Which work-item updates need your response? (DM, MG), pp. 12–21.
HCIDUXU-WM-2013-OkazawaY #design #modelling
A Proposal of Design Method of Obtaining the Construction Items of Mental Models in Product Design (NO, TY), pp. 408–413.
KDDKDD-2013-KabburNK #modelling #named #recommendation #similarity
FISM: factored item similarity models for top-N recommender systems (SK, XN, GK), pp. 659–667.
RecSysRecSys-2013-AdamopoulosT #collaboration #predict #recommendation #using
Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems (PA, AT), pp. 351–354.
RecSysRecSys-2013-KoenigsteinK #recommendation #scalability #towards
Towards scalable and accurate item-oriented recommendations (NK, YK), pp. 419–422.
SIGIRSIGIR-2013-ZhangCWY #collaboration #optimisation
Optimizing top-n collaborative filtering via dynamic negative item sampling (WZ, TC, JW, YY), pp. 785–788.
SACSAC-2013-ZengC #data fusion #matrix #recommendation #semistructured data
Heterogeneous data fusion via matrix factorization for augmenting item, group and friend recommendations (WZ, LC), pp. 237–244.
ICEISICEIS-J-2012-GeJG #analysis #recommendation
Bringing Diversity to Recommendation Lists — An Analysis of the Placement of Diverse Items (MG, DJ, FG), pp. 293–305.
ICEISICEIS-v2-2012-GeJGH #recommendation
Effects of the Placement of Diverse Items in Recommendation Lists (MG, DJ, FG, MH), pp. 201–208.
CIKMCIKM-2012-ShenRS #categorisation #e-commerce #scalability
Large-scale item categorization for e-commerce (DS, JDR, BS), pp. 595–604.
ECIRECIR-2012-PolajnarGA #detection
Detection of News Feeds Items Appropriate for Children (TP, RG, LA), pp. 63–72.
ICPRICPR-2012-GranaCBC #image #learning #segmentation
Learning non-target items for interesting clothes segmentation in fashion images (CG, SC, DB, RC), pp. 3317–3320.
KDDKDD-2012-RoyTA #hardware #manycore #performance
Efficient frequent item counting in multi-core hardware (PR, JT, GA), pp. 1451–1459.
RecSysRecSys-2012-DeDGM #difference #learning #using
Local learning of item dissimilarity using content and link structure (AD, MSD, NG, PM), pp. 221–224.
RecSysRecSys-2012-LiuXCGXBZ #recommendation
Influential seed items recommendation (QL, BX, EC, YG, HX, TB, YZ), pp. 245–248.
SIGIRSIGIR-2012-QumsiyehN #multi #personalisation #predict #recommendation
Predicting the ratings of multimedia items for making personalized recommendations (RQ, YKN), pp. 475–484.
SACSAC-2012-HsiehC #social
Finding similar items by leveraging social tag clouds (CCH, JC), pp. 644–651.
SACSAC-2012-VerheijKFVH #framework #query #ranking
Querying and ranking news items in the hermes framework (AV, AK, FF, DV, FH), pp. 672–679.
ICSEICSE-2012-TreudeGGS #development #interactive #named #using #visualisation
WorkItemExplorer: Visualizing software development tasks using an interactive exploration environment (CT, PG, LG, MADS), pp. 1399–1402.
STOCSTOC-2011-PapadimitriouP #on the
On optimal single-item auctions (CHP, GP), pp. 119–128.
CHICHI-2011-Miller #architecture
Item sampling for information architecture (CSM), pp. 2211–2214.
HCIDUXU-v2-2011-ChangH #performance
Effects of Menu Types and Item Lengths on Operation Efficiency (YHC, TKPH), pp. 376–383.
CIKMCIKM-2011-HarveyCRC #collaboration #modelling #predict #rating
Bayesian latent variable models for collaborative item rating prediction (MH, MJC, IR, FC), pp. 699–708.
CIKMCIKM-2011-LiangXL
Adding structure to top-k: from items to expansions (XL, MX, LVSL), pp. 1699–1708.
CIKMCIKM-2011-ShenRSS #categorisation #e-commerce
Item categorization in the e-commerce domain (DS, JDR, MS, NS), pp. 1921–1924.
ECIRECIR-2011-BelloginWC #collaboration #ranking #retrieval
Text Retrieval Methods for Item Ranking in Collaborative Filtering (AB, JW, PC), pp. 301–306.
KDDKDD-2011-DasDH #collaboration #design #web
Leveraging collaborative tagging for web item design (MD, GD, VH), pp. 538–546.
RecSysRecSys-2011-AnandG #approach #problem
A market-based approach to address the new item problem (SSA, NG), pp. 205–212.
RecSysRecSys-2011-BarbieriCMO #approach #modelling #recommendation
Modeling item selection and relevance for accurate recommendations: a bayesian approach (NB, GC, GM, RO), pp. 21–28.
RecSysRecSys-2011-JojicSB #probability #similarity
A probabilistic definition of item similarity (OJ, MS, NB), pp. 229–236.
RecSysRecSys-2011-KoenigsteinDK #exclamation #modelling #music #recommendation #taxonomy
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy (NK, GD, YK), pp. 165–172.
RecSysRecSys-2011-KorenS #named #personalisation #predict #rating
OrdRec: an ordinal model for predicting personalized item rating distributions (YK, JS), pp. 117–124.
RecSysRecSys-2011-Steck #recommendation
Item popularity and recommendation accuracy (HS), pp. 125–132.
SIGIRSIGIR-2011-ChenC #recommendation #web
Recommending ephemeral items at web scale (YC, JFC), pp. 1013–1022.
SIGIRSIGIR-2011-CuiWLOYS #predict #ranking #social #what
Who should share what?: item-level social influence prediction for users and posts ranking (PC, FW, SL, MO, SY, LS), pp. 185–194.
SIGIRSIGIR-2011-LiLS #recommendation #social
Exploiting endorsement information and social influence for item recommendation (CTL, SDL, MKS), pp. 1131–1132.
SIGIRSIGIR-2011-ZhangMLM #approach #how #information retrieval #ranking
How to count thumb-ups and thumb-downs?: an information retrieval approach to user-rating based ranking of items (DZ, RM, HL, JM), pp. 1223–1224.
DocEngDocEng-2010-CahierMZ #documentation #hybrid #modelling #semantics #web
Document and item-based modeling: a hybrid method for a socio-semantic web (JPC, XM, LZ), pp. 243–246.
DocEngDocEng-2010-RivaSOMP #metric
Two new aesthetic measures for item alignment (ADR, AKS, JBSdO, IHM, RFBP), pp. 263–266.
HTHT-2010-LiangXLNT #personalisation #recommendation
Connecting users and items with weighted tags for personalized item recommendations (HL, YX, YL, RN, XT), pp. 51–60.
SIGMODSIGMOD-2010-RoyACDY
Constructing and exploring composite items (SBR, SAY, AC, GD, CY), pp. 843–854.
STOCSTOC-2010-BhattacharyaGGM
Budget constrained auctions with heterogeneous items (SB, GG, SG, KM), pp. 379–388.
ICALPICALP-v2-2010-ChenD #multi
Envy-Free Pricing in Multi-item Markets (NC, XD), pp. 418–429.
ICEISICEIS-SAIC-2010-Foster10a #adaptation #guidelines #industrial #multi
Adapting Multiple-Choice Item-writing Guidelines to an Industrial Context (RMF), pp. 71–74.
CIKMCIKM-2010-LiangXLN #folksonomy #personalisation #recommendation #taxonomy
Personalized recommender system based on item taxonomy and folksonomy (HL, YX, YL, RN), pp. 1641–1644.
CIKMCIKM-2010-PengZZW #collaboration #recommendation #social
Collaborative filtering in social tagging systems based on joint item-tag recommendations (JP, DDZ, HZ, FYW), pp. 809–818.
CIKMCIKM-2010-SeseSF #mining #network
Mining networks with shared items (JS, MS, MF), pp. 1681–1684.
ECIRECIR-2010-AlyDHS #concept #modelling #retrieval #using
Beyond Shot Retrieval: Searching for Broadcast News Items Using Language Models of Concepts (RA, ARD, DH, AFS), pp. 241–252.
ICMLICML-2010-ZhuGJRHK #learning #modelling
Cognitive Models of Test-Item Effects in Human Category Learning (XZ, BRG, KSJ, TTR, JH, CK), pp. 1247–1254.
KDDKDD-2010-LamC #data type #flexibility #mining
Mining top-k frequent items in a data stream with flexible sliding windows (HTL, TC), pp. 283–292.
RecSysRecSys-2010-FreyneBDG #network #recommendation #social
Social networking feeds: recommending items of interest (JF, SB, EMD, WG), pp. 277–280.
RecSysRecSys-2010-XieLW #recommendation
Breaking out of the box of recommendations: from items to packages (MX, LVSL, PTW), pp. 151–158.
SIGIRSIGIR-2010-HwangKPSL #interactive #named
Si-Fi: interactive similar item finder (IH, MK, SEP, JS, SgL), p. 704.
SACSAC-2010-GrozaHB #automation #towards
Towards automatic extraction of epistemic items from scientific publications (TG, SH, GB), pp. 1341–1348.
SACSAC-2010-LvLLC #multi #on-demand #realtime #scheduling
Profit-based on-demand broadcast scheduling of real-time multi-item requests (JL, VCSL, ML, EC), pp. 580–584.
CASECASE-2009-HariharanB #markov #process #using
Misplaced item search in a warehouse using an RFID-based Partially Observable Markov Decision Process (POMDP) model (SH, STSB), pp. 443–448.
CHICHI-2009-HansenG #recommendation
Mixing it up: recommending collections of items (DLH, JG), pp. 1217–1226.
CIKMCIKM-2009-WuB #predict #probability
Predicting the conversion probability for items on C2C ecommerce sites (XW, AB), pp. 1377–1386.
KDDKDD-2009-JamaliE #named #random #recommendation #trust
TrustWalker: a random walk model for combining trust-based and item-based recommendation (MJ, ME), pp. 397–406.
KDIRKDIR-2009-FujimotoHM #modelling #visualisation
Item-user Preference Mapping with Mixture Models — Data Visualization for Item Preference (YF, HH, NM), pp. 105–111.
RecSysRecSys-2009-BaltrunasR #collaboration
Context-based splitting of item ratings in collaborative filtering (LB, FR), pp. 245–248.
RecSysRecSys-2009-GreenLAMKHBM #generative #recommendation
Generating transparent, steerable recommendations from textual descriptions of items (SJG, PL, JA, FM, SK, JH, JB, XWM), pp. 281–284.
RecSysRecSys-2009-GuyZCRUYO #personalisation #recommendation #social
Personalized recommendation of social software items based on social relations (IG, NZ, DC, IR, EU, SY, SOK), pp. 53–60.
RecSysRecSys-2009-ShiLH #collaboration #similarity
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering (YS, ML, AH), pp. 125–132.
SACSAC-2009-KoongLCCS #authoring #component #interactive #visual notation
The visual authoring tool of flash-based component for interactive item template (CSK, CML, DJC, CHC, CS), pp. 547–548.
SIGMODSIGMOD-2008-ZhangLY #probability
Finding frequent items in probabilistic data (QZ, FL, KY), pp. 819–832.
VLDBVLDB-2008-CormodeH #data type
Finding frequent items in data streams (GC, MH), pp. 1530–1541.
CHICHI-2008-SchmettowV #process #usability
Introducing item response theory for measuring usability inspection processes (MS, WV), pp. 893–902.
SOFTVISSOFTVIS-2008-TheronGG #comprehension #evolution
Supporting the understanding of the evolution of software items (RT, AGT, FJGP), pp. 189–192.
ICEISICEIS-ISAS1-2008-LoewensternS #lifecycle #using
IT Service Management of Using Heterogeneous, Dynamically Alterable Configuration Item Lifecycles (DL, LS), pp. 155–160.
ICEISICEIS-J-2008-WengXLN08b #quality #recommendation #taxonomy
Improve Recommendation Quality with Item Taxonomic Information (LTW, YX, YL, RN), pp. 265–279.
ICEISICEIS-SAIC-2008-WengXLLN #recommendation #taxonomy #web
Web Information Recommendation Making Based on Item Taxonomy (LTW, YX, YL, RN), pp. 20–28.
ECIRECIR-2008-YahyaeiM #email #retrieval
Applying Maximum Entropy to Known-Item Email Retrieval (SY, CM), pp. 406–413.
RecSysRecSys-2008-KagieWG #difference #using
Choosing attribute weights for item dissimilarity using clikstream data with an application to a product catalog map (MK, MCvW, PJFG), pp. 195–202.
ICEISICEIS-SAIC-2007-GallardoCHG #e-commerce
Improving the Search and Cataloguing of Items in C2C E-Commerce Portals (AG, JJCS, MH, JMG), pp. 13–19.
ECIRECIR-2007-Nanopoulos #collaboration #correlation #transitive
Collaborative Filtering Based on Transitive Correlations Between Items (AN), pp. 368–380.
MLDMMLDM-2007-FullerK #data type #distributed #monitoring #named
FIDS: Monitoring Frequent Items over Distributed Data Streams (RF, MMK), pp. 464–478.
RecSysRecSys-2007-LiDEL #probability #recommendation
A probabilistic model for item-based recommender systems (ML, MBD, WED, PJGL), pp. 129–132.
RecSysRecSys-2007-NathansonBG #adaptation #clustering #recommendation #using
Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering (TN, EB, KYG), pp. 149–152.
SIGIRSIGIR-2007-AzzopardiRB #analysis #query #topic #using
Building simulated queries for known-item topics: an analysis using six european languages (LA, MdR, KB), pp. 455–462.
PODSPODS-2006-LeeT #performance
A simpler and more efficient deterministic scheme for finding frequent items over sliding windows (LKL, HFT), pp. 290–297.
CHICHI-2006-DrennerHFRT #web
Insert movie reference here: a system to bridge conversation and item-oriented web sites (SD, FMH, DF, JR, LGT), pp. 951–954.
ECIRECIR-2006-WangVR #collaboration
A User-Item Relevance Model for Log-Based Collaborative Filtering (JW, APdV, MJTR), pp. 37–48.
SIGIRSIGIR-2006-AzzopardiR #automation
Automatic construction of known-item finding test beds (LA, MdR), pp. 603–604.
SIGIRSIGIR-2006-MacdonaldO #email
Combining fields in known-item email search (CM, IO), pp. 675–676.
SIGIRSIGIR-2006-WangVR #collaboration #similarity
Unifying user-based and item-based collaborative filtering approaches by similarity fusion (JW, APdV, MJTR), pp. 501–508.
SACSAC-2006-HungHHC #scheduling
Scheduling dependent items in data broadcasting environments (HPH, JWH, JLH, MSC), pp. 1177–1181.
SPLCSPL-BOOK-2006-Engelsma #diagrams #evolution #incremental #integration #multi #product line #using
Incremental Systems Integration within Multidisciplinary Product Line Engineering Using Configuration Item Evolution Diagrams (EE), pp. 523–555.
VLDBVLDB-2005-HuSCS #data type #using
Supporting RFID-based Item Tracking Applications in Oracle DBMS Using a Bitmap Datatype (YH, SS, TC, JS), pp. 1140–1151.
ICALPICALP-2005-ChanLW
Dynamic Bin Packing of Unit Fractions Items (WTC, TWL, PWHW), pp. 614–626.
ICEISICEIS-v4-2005-DavulcuNR #semantic gap
Boosting Item Findability: Bridging the Semantic Gap between Search Phrases and Item Information (HD, HVN, VR), pp. 48–55.
MLDMMLDM-2005-HamanoS #analysis #semantics
Semantic Analysis of Association Rules via Item Response Theory (SH, MS), pp. 641–650.
SIGIRSIGIR-2005-BennettC #detection #email
Detecting action-items in e-mail (PNB, JGC), pp. 585–586.
SACSAC-2005-Hara #ad hoc #mobile #network
Location management of data items in mobile ad hoc networks (TH), pp. 1174–1175.
ICEISICEIS-v4-2004-LohGLBRSAP #chat #library #recommendation #web
Analyzing Web Chat Messages for Recommending Items from a Digital Library (SL, RSG, DL, TB, RR, GS, LA, TP), pp. 41–48.
PODSPODS-2003-CormodeM #what
What’s hot and what’s not: tracking most frequent items dynamically (GC, SM), pp. 296–306.
ICEISICEIS-v1-2003-ChengTKRWYH #approach #e-commerce #enterprise #integration #modelling #scalability
A Model-Driven Approach for Item Synchronization and Uccnet Integration in Large E-Commerce Enterprise Systems (SC, MT, SK, AR, FYW, YY, YH), pp. 128–135.
CIKMCIKM-2003-JinQSYZ #data type #maintenance
Dynamically maintaining frequent items over a data stream (CJ, WQ, CS, JXY, AZ), pp. 287–294.
KDDKDD-2003-El-HajjZ #dataset #interactive #matrix #mining #performance #scalability
Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining (MEH, ORZ), pp. 109–118.
KDDKDD-2003-WuBY #modelling #multi
Screening and interpreting multi-item associations based on log-linear modeling (XW, DB, YY), pp. 276–285.
SIGIRSIGIR-2003-BeitzelJCGF #automation #evaluation #retrieval #using #web
Using manually-built web directories for automatic evaluation of known-item retrieval (SMB, ECJ, AC, DAG, OF), pp. 373–374.
SIGIRSIGIR-2003-OgilvieC #documentation
Combining document representations for known-item search (PO, JPC), pp. 143–150.
ICALPICALP-2002-CharikarCF #data type
Finding Frequent Items in Data Streams (MC, KCC, MFC), pp. 693–703.
ICALPICALP-2002-GuhaIMS #data type #performance
Histogramming Data Streams with Fast Per-Item Processing (SG, PI, SM, MS), pp. 681–692.
KDDKDD-2002-LiuPWH #mining #set
Mining frequent item sets by opportunistic projection (JL, YP, KW, JH), pp. 229–238.
KDDKDD-2002-WangS #ranking
Item selection by “hub-authority” profit ranking (KW, MYTS), pp. 652–657.
CIKMCIKM-2001-Karypis #algorithm #evaluation #recommendation
Evaluation of Item-Based Top-N Recommendation Algorithms (GK), pp. 247–254.
KDDKDD-2001-DuMouchelP #empirical #multi
Empirical bayes screening for multi-item associations (WD, DP), pp. 67–76.
KDDKDD-2000-KittsFV #independence #named #performance #recommendation
Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities (BK, DF, MV), pp. 437–446.
CHICHI-1999-HornofK #how #modelling #people
Cognitive Modeling Demonstrates How People Use Anticipated Location Knowledge of Menu Items (AJH, DEK), pp. 410–417.
CHICHI-1999-KurtenbachFOB #performance #scalability
The Hotbox: Efficient Access to a Large Number of Menu-Items (GK, GWF, RNO, TB), pp. 231–237.
CIKMCIKM-1999-WangXL #clustering #scalability #transaction #using
Clustering Transactions Using Large Items (KW, CX, BL), pp. 483–490.
TOOLSTOOLS-ASIA-1999-WangDXL #design #multi #object-oriented
An Object-Oriented Design of a Multimedia Item Pool (YW, TD, LX, RL), pp. 471–476.
ICDARICDAR-1997-DjeziriNP
Extraction of Items From Checks (SD, FN, RP), pp. 749–752.
KDDKDD-1997-SrikantVA #constraints #mining
Mining Association Rules with Item Constraints (RS, QV, RA), pp. 67–73.
ICPRICPR-1996-LiuSN #automation #image #recognition
Automatic extraction of items from cheque images for payment recognition (KL, CYS, CPN), pp. 798–802.
FSEFSE-1996-Gunter #dependence
Abstracting Dependencies between Software Configuration Items (CAG), pp. 167–178.
SIGMODSIGMOD-1993-AgrawalIS #database #mining #scalability #set
Mining Association Rules between Sets of Items in Large Databases (RA, TI, ANS), pp. 207–216.
ICSEICSE-1993-KitchenhamK #correlation
Inter-item Correlations among Function Points (BK, KK), pp. 477–480.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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