Stem item$ (all stems)

152 papers:

SIGMOD-2015-NaziDD #bibliography #web

The TagAdvisor: Luring the Lurkers to Review Web Items (AN, MD, GD), pp. 531–543.

ITiCSE-2015-BergesH #evaluation #source code

Evaluation of Source Code with Item Response Theory (MB, PH), pp. 51–56.

STOC-2015-ColeG #approximate #nash #social

Approximating the Nash Social Welfare with Indivisible Items (RC, VG), pp. 371–380.

CSCW-2015-ChangHT #recommendation #using

Using Groups of Items to Bootstrap New Users in Recommender Systems (SC, FMH, LGT), pp. 1258–1269.

ECIR-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.

ECIR-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.

KDD-2015-VanchinathanMRK

Discovering Valuable items from Massive Data (HPV, AM, CAR, DK, AK), pp. 1195–1204.

KDD-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.

RecSys-2015-AharonAADGS #named #recommendation

ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations (MA, OA, NAE, DDC, SG, OS), pp. 83–90.

RecSys-2015-BarjastehFMER #recommendation

Cold-Start Item and User Recommendation with Decoupled Completion and Transduction (IB, RF, FM, AHE, HR), pp. 91–98.

RecSys-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.

RecSys-2015-SeminarioW #collaboration #recommendation

Nuke ’Em Till They Go: Investigating Power User Attacks to Disparage Items in Collaborative Recommenders (CES, DCW), pp. 293–296.

SIGIR-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.

SAC-2015-Chaudhary #experience #recommendation

Experience in item based recommender system (AC), pp. 1112–1114.

SAC-2015-DAddioM #approach #collaboration #sentiment

A sentiment-based item description approach for kNN collaborative filtering (RMD, MGM), pp. 1060–1065.

SAC-2015-LommatzschA #realtime #recommendation

Real-time recommendations for user-item streams (AL, SA), pp. 1039–1046.

DATE-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.

VLDB-2015-ThirumuruganathanRAD14 #mining

Beyond Itemsets: Mining Frequent Featuresets over Structured Items (ST, HR, SA, GD), pp. 257–268.

HIMI-DE-2014-HoriguchiASN #dependence #generative

Menu Hierarchy Generation Based on Syntactic Dependency Structures in Item Descriptions (YH, SA, TS, HN), pp. 157–166.

HIMI-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.

CIKM-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.

CIKM-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.

KDD-2014-LiL #quality #recommendation

Matching users and items across domains to improve the recommendation quality (CYL, SDL), pp. 801–810.

RecSys-2014-Liu0L #recommendation

Recommending user generated item lists (YL, MX, LVSL), pp. 185–192.

RecSys-2014-SaveskiM #learning #recommendation

Item cold-start recommendations: learning local collective embeddings (MS, AM), pp. 89–96.

RecSys-2014-SeminarioW #recommendation

Attacking item-based recommender systems with power items (CES, DCW), pp. 57–64.

RecSys-2014-Sharma #modelling #people #social

Modeling the effect of people’s preferences and social forces on adopting and sharing items (AS), pp. 421–424.

RecSys-2014-VargasC #recommendation

Improving sales diversity by recommending users to items (SV, PC), pp. 145–152.

SIGIR-2014-HeGKLS #predict #web

Predicting the popularity of web 2.0 items based on user comments (XH, MG, MYK, YL, KS), pp. 233–242.

SIGIR-2014-Ifada #modelling #personalisation #recommendation #topic #using

A tag-based personalized item recommendation system using tensor modeling and topic model approaches (NI), p. 1280.

SIGIR-2014-QiuCYLL #learning #personalisation #ranking

Item group based pairwise preference learning for personalized ranking (SQ, JC, TY, CL, HL), pp. 1219–1222.

SAC-2014-WangMLG #recommendation #social

Recommendation based on weighted social trusts and item relationships (DW, JM, TL, LG), pp. 254–259.

CASE-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.

MSR-2013-MukherjeeG #question

Which work-item updates need your response? (DM, MG), pp. 12–21.

DUXU-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.

KDD-2013-KabburNK #modelling #named #recommendation #similarity

FISM: factored item similarity models for top-N recommender systems (SK, XN, GK), pp. 659–667.

RecSys-2013-AdamopoulosT #collaboration #predict #recommendation #using

Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems (PA, AT), pp. 351–354.

RecSys-2013-KoenigsteinK #recommendation #scalability #towards

Towards scalable and accurate item-oriented recommendations (NK, YK), pp. 419–422.

SIGIR-2013-ZhangCWY #collaboration #optimisation

Optimizing top-n collaborative filtering via dynamic negative item sampling (WZ, TC, JW, YY), pp. 785–788.

SAC-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.

ICEIS-J-2012-GeJG #analysis #recommendation

Bringing Diversity to Recommendation Lists — An Analysis of the Placement of Diverse Items (MG, DJ, FG), pp. 293–305.

ICEIS-v2-2012-GeJGH #recommendation

Effects of the Placement of Diverse Items in Recommendation Lists (MG, DJ, FG, MH), pp. 201–208.

CIKM-2012-ShenRS #categorisation #e-commerce #scalability

Large-scale item categorization for e-commerce (DS, JDR, BS), pp. 595–604.

ECIR-2012-PolajnarGA #detection

Detection of News Feeds Items Appropriate for Children (TP, RG, LA), pp. 63–72.

ICPR-2012-GranaCBC #image #learning #segmentation

Learning non-target items for interesting clothes segmentation in fashion images (CG, SC, DB, RC), pp. 3317–3320.

KDD-2012-RoyTA #hardware #manycore #performance

Efficient frequent item counting in multi-core hardware (PR, JT, GA), pp. 1451–1459.

RecSys-2012-DeDGM #difference #learning #using

Local learning of item dissimilarity using content and link structure (AD, MSD, NG, PM), pp. 221–224.

RecSys-2012-LiuXCGXBZ #recommendation

Influential seed items recommendation (QL, BX, EC, YG, HX, TB, YZ), pp. 245–248.

SIGIR-2012-QumsiyehN #multi #personalisation #predict #recommendation

Predicting the ratings of multimedia items for making personalized recommendations (RQ, YKN), pp. 475–484.

SAC-2012-HsiehC #social

Finding similar items by leveraging social tag clouds (CCH, JC), pp. 644–651.

SAC-2012-VerheijKFVH #framework #query #ranking

Querying and ranking news items in the hermes framework (AV, AK, FF, DV, FH), pp. 672–679.

ICSE-2012-TreudeGGS #development #interactive #named #using #visualisation

WorkItemExplorer: Visualizing software development tasks using an interactive exploration environment (CT, PG, LG, MADS), pp. 1399–1402.

STOC-2011-PapadimitriouP #on the

On optimal single-item auctions (CHP, GP), pp. 119–128.

CHI-2011-Miller #architecture

Item sampling for information architecture (CSM), pp. 2211–2214.

DUXU-v2-2011-ChangH #performance

Effects of Menu Types and Item Lengths on Operation Efficiency (YHC, TKPH), pp. 376–383.

CIKM-2011-HarveyCRC #collaboration #modelling #predict #rating

Bayesian latent variable models for collaborative item rating prediction (MH, MJC, IR, FC), pp. 699–708.

CIKM-2011-LiangXL

Adding structure to top-k: from items to expansions (XL, MX, LVSL), pp. 1699–1708.

CIKM-2011-ShenRSS #categorisation #e-commerce

Item categorization in the e-commerce domain (DS, JDR, MS, NS), pp. 1921–1924.

ECIR-2011-BelloginWC #collaboration #ranking #retrieval

Text Retrieval Methods for Item Ranking in Collaborative Filtering (AB, JW, PC), pp. 301–306.

KDD-2011-DasDH #collaboration #design #web

Leveraging collaborative tagging for web item design (MD, GD, VH), pp. 538–546.

RecSys-2011-AnandG #approach #problem

A market-based approach to address the new item problem (SSA, NG), pp. 205–212.

RecSys-2011-BarbieriCMO #approach #modelling #recommendation

Modeling item selection and relevance for accurate recommendations: a bayesian approach (NB, GC, GM, RO), pp. 21–28.

RecSys-2011-JojicSB #probability #similarity

A probabilistic definition of item similarity (OJ, MS, NB), pp. 229–236.

RecSys-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.

RecSys-2011-KorenS #named #personalisation #predict #rating

OrdRec: an ordinal model for predicting personalized item rating distributions (YK, JS), pp. 117–124.

RecSys-2011-Steck #recommendation

Item popularity and recommendation accuracy (HS), pp. 125–132.

SIGIR-2011-ChenC #recommendation #web

Recommending ephemeral items at web scale (YC, JFC), pp. 1013–1022.

SIGIR-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.

SIGIR-2011-LiLS #recommendation #social

Exploiting endorsement information and social influence for item recommendation (CTL, SDL, MKS), pp. 1131–1132.

SIGIR-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.

DocEng-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.

DocEng-2010-RivaSOMP #metric

Two new aesthetic measures for item alignment (ADR, AKS, JBSdO, IHM, RFBP), pp. 263–266.

HT-2010-LiangXLNT #personalisation #recommendation

Connecting users and items with weighted tags for personalized item recommendations (HL, YX, YL, RN, XT), pp. 51–60.

SIGMOD-2010-RoyACDY

Constructing and exploring composite items (SBR, SAY, AC, GD, CY), pp. 843–854.

STOC-2010-BhattacharyaGGM

Budget constrained auctions with heterogeneous items (SB, GG, SG, KM), pp. 379–388.

ICALP-v2-2010-ChenD #multi

Envy-Free Pricing in Multi-item Markets (NC, XD), pp. 418–429.

ICEIS-SAIC-2010-Foster10a #adaptation #guidelines #industrial #multi

Adapting Multiple-Choice Item-writing Guidelines to an Industrial Context (RMF), pp. 71–74.

CIKM-2010-LiangXLN #folksonomy #personalisation #recommendation #taxonomy

Personalized recommender system based on item taxonomy and folksonomy (HL, YX, YL, RN), pp. 1641–1644.

CIKM-2010-PengZZW #collaboration #recommendation #social

Collaborative filtering in social tagging systems based on joint item-tag recommendations (JP, DDZ, HZ, FYW), pp. 809–818.

CIKM-2010-SeseSF #mining #network

Mining networks with shared items (JS, MS, MF), pp. 1681–1684.

ECIR-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.

ICML-2010-ZhuGJRHK #learning #modelling

Cognitive Models of Test-Item Effects in Human Category Learning (XZ, BRG, KSJ, TTR, JH, CK), pp. 1247–1254.

KDD-2010-LamC #data type #flexibility #mining

Mining top-k frequent items in a data stream with flexible sliding windows (HTL, TC), pp. 283–292.

RecSys-2010-FreyneBDG #network #recommendation #social

Social networking feeds: recommending items of interest (JF, SB, EMD, WG), pp. 277–280.

RecSys-2010-XieLW #recommendation

Breaking out of the box of recommendations: from items to packages (MX, LVSL, PTW), pp. 151–158.

SIGIR-2010-HwangKPSL #interactive #named

Si-Fi: interactive similar item finder (IH, MK, SEP, JS, SgL), p. 704.

SAC-2010-GrozaHB #automation #towards

Towards automatic extraction of epistemic items from scientific publications (TG, SH, GB), pp. 1341–1348.

SAC-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.

CASE-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.

CHI-2009-HansenG #recommendation

Mixing it up: recommending collections of items (DLH, JG), pp. 1217–1226.

CIKM-2009-WuB #predict #probability

Predicting the conversion probability for items on C2C ecommerce sites (XW, AB), pp. 1377–1386.

KDD-2009-JamaliE #named #random #recommendation #trust

TrustWalker: a random walk model for combining trust-based and item-based recommendation (MJ, ME), pp. 397–406.

KDIR-2009-FujimotoHM #modelling #visualisation

Item-user Preference Mapping with Mixture Models — Data Visualization for Item Preference (YF, HH, NM), pp. 105–111.

RecSys-2009-BaltrunasR #collaboration

Context-based splitting of item ratings in collaborative filtering (LB, FR), pp. 245–248.

RecSys-2009-GreenLAMKHBM #generative #recommendation

Generating transparent, steerable recommendations from textual descriptions of items (SJG, PL, JA, FM, SK, JH, JB, XWM), pp. 281–284.

RecSys-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.

RecSys-2009-ShiLH #collaboration #similarity

Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering (YS, ML, AH), pp. 125–132.

SAC-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.

SIGMOD-2008-ZhangLY #probability

Finding frequent items in probabilistic data (QZ, FL, KY), pp. 819–832.

VLDB-2008-CormodeH #data type

Finding frequent items in data streams (GC, MH), pp. 1530–1541.

CHI-2008-SchmettowV #process #usability

Introducing item response theory for measuring usability inspection processes (MS, WV), pp. 893–902.

SOFTVIS-2008-TheronGG #comprehension #evolution

Supporting the understanding of the evolution of software items (RT, AGT, FJGP), pp. 189–192.

ICEIS-ISAS1-2008-LoewensternS #lifecycle #using

IT Service Management of Using Heterogeneous, Dynamically Alterable Configuration Item Lifecycles (DL, LS), pp. 155–160.

ICEIS-J-2008-WengXLN08b #quality #recommendation #taxonomy

Improve Recommendation Quality with Item Taxonomic Information (LTW, YX, YL, RN), pp. 265–279.

ICEIS-SAIC-2008-WengXLLN #recommendation #taxonomy #web

Web Information Recommendation Making Based on Item Taxonomy (LTW, YX, YL, RN), pp. 20–28.

ECIR-2008-YahyaeiM #email #retrieval

Applying Maximum Entropy to Known-Item Email Retrieval (SY, CM), pp. 406–413.

RecSys-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.

ICEIS-SAIC-2007-GallardoCHG #e-commerce

Improving the Search and Cataloguing of Items in C2C E-Commerce Portals (AG, JJCS, MH, JMG), pp. 13–19.

ECIR-2007-Nanopoulos #collaboration #correlation #transitive

Collaborative Filtering Based on Transitive Correlations Between Items (AN), pp. 368–380.

MLDM-2007-FullerK #data type #distributed #monitoring #named

FIDS: Monitoring Frequent Items over Distributed Data Streams (RF, MMK), pp. 464–478.

RecSys-2007-LiDEL #probability #recommendation

A probabilistic model for item-based recommender systems (ML, MBD, WED, PJGL), pp. 129–132.

RecSys-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.

SIGIR-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.

PODS-2006-LeeT #performance

A simpler and more efficient deterministic scheme for finding frequent items over sliding windows (LKL, HFT), pp. 290–297.

CHI-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.

ECIR-2006-WangVR #collaboration

A User-Item Relevance Model for Log-Based Collaborative Filtering (JW, APdV, MJTR), pp. 37–48.

SIGIR-2006-AzzopardiR #automation

Automatic construction of known-item finding test beds (LA, MdR), pp. 603–604.

SIGIR-2006-MacdonaldO #email

Combining fields in known-item email search (CM, IO), pp. 675–676.

SIGIR-2006-WangVR #collaboration #similarity

Unifying user-based and item-based collaborative filtering approaches by similarity fusion (JW, APdV, MJTR), pp. 501–508.

SAC-2006-HungHHC #scheduling

Scheduling dependent items in data broadcasting environments (HPH, JWH, JLH, MSC), pp. 1177–1181.

SPL-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.

VLDB-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.

ICALP-2005-ChanLW

Dynamic Bin Packing of Unit Fractions Items (WTC, TWL, PWHW), pp. 614–626.

ICEIS-v4-2005-DavulcuNR #semantic gap

Boosting Item Findability: Bridging the Semantic Gap between Search Phrases and Item Information (HD, HVN, VR), pp. 48–55.

MLDM-2005-HamanoS #analysis #semantics

Semantic Analysis of Association Rules via Item Response Theory (SH, MS), pp. 641–650.

SIGIR-2005-BennettC #detection #email

Detecting action-items in e-mail (PNB, JGC), pp. 585–586.

SAC-2005-Hara #ad hoc #mobile #network

Location management of data items in mobile ad hoc networks (TH), pp. 1174–1175.

ICEIS-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.

PODS-2003-CormodeM #what

What’s hot and what’s not: tracking most frequent items dynamically (GC, SM), pp. 296–306.

ICEIS-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.

CIKM-2003-JinQSYZ #data type #maintenance

Dynamically maintaining frequent items over a data stream (CJ, WQ, CS, JXY, AZ), pp. 287–294.

KDD-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.

KDD-2003-WuBY #modelling #multi

Screening and interpreting multi-item associations based on log-linear modeling (XW, DB, YY), pp. 276–285.

SIGIR-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.

SIGIR-2003-OgilvieC #documentation

Combining document representations for known-item search (PO, JPC), pp. 143–150.

ICALP-2002-CharikarCF #data type

Finding Frequent Items in Data Streams (MC, KCC, MFC), pp. 693–703.

ICALP-2002-GuhaIMS #data type #performance

Histogramming Data Streams with Fast Per-Item Processing (SG, PI, SM, MS), pp. 681–692.

KDD-2002-LiuPWH #mining #set

Mining frequent item sets by opportunistic projection (JL, YP, KW, JH), pp. 229–238.

KDD-2002-WangS #ranking

Item selection by “hub-authority” profit ranking (KW, MYTS), pp. 652–657.

CIKM-2001-Karypis #algorithm #evaluation #recommendation

Evaluation of Item-Based Top-N Recommendation Algorithms (GK), pp. 247–254.

KDD-2001-DuMouchelP #empirical #multi

Empirical bayes screening for multi-item associations (WD, DP), pp. 67–76.

KDD-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.

CHI-1999-HornofK #how #modelling #people

Cognitive Modeling Demonstrates How People Use Anticipated Location Knowledge of Menu Items (AJH, DEK), pp. 410–417.

CHI-1999-KurtenbachFOB #performance #scalability

The Hotbox: Efficient Access to a Large Number of Menu-Items (GK, GWF, RNO, TB), pp. 231–237.

CIKM-1999-WangXL #clustering #scalability #transaction #using

Clustering Transactions Using Large Items (KW, CX, BL), pp. 483–490.

TOOLS-ASIA-1999-WangDXL #design #multi #object-oriented

An Object-Oriented Design of a Multimedia Item Pool (YW, TD, LX, RL), pp. 471–476.

ICDAR-1997-DjeziriNP

Extraction of Items From Checks (SD, FN, RP), pp. 749–752.

KDD-1997-SrikantVA #constraints #mining

Mining Association Rules with Item Constraints (RS, QV, RA), pp. 67–73.

ICPR-1996-LiuSN #automation #image #recognition

Automatic extraction of items from cheque images for payment recognition (KL, CYS, CPN), pp. 798–802.

FSE-1996-Gunter #dependence

Abstracting Dependencies between Software Configuration Items (CAG), pp. 167–178.

SIGMOD-1993-AgrawalIS #database #mining #scalability #set

Mining Association Rules between Sets of Items in Large Databases (RA, TI, ANS), pp. 207–216.

ICSE-1993-KitchenhamK #correlation

Inter-item Correlations among Function Points (BK, KK), pp. 477–480.