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:
detect (16)
divers (9)
inform (6)
recommend (5)
base (5)

Stem novelti$ (all stems)

39 papers:

RecSysRecSys-2015-KapoorKTKS #adaptation #quote
“I like to explore sometimes”: Adapting to Dynamic User Novelty Preferences (KK, VK, LGT, JAK, PRS), pp. 19–26.
SIGIRSIGIR-2015-BahCC #documentation
Document Comprehensiveness and User Preferences in Novelty Search Tasks (AB, PC, BC), pp. 735–738.
SIGIRSIGIR-2015-SchedlH #music #recommendation
Tailoring Music Recommendations to Users by Considering Diversity, Mainstreaminess, and Novelty (MS, DH), pp. 947–950.
CIKMCIKM-2014-McCreadieMO #adaptation #incremental #summary
Incremental Update Summarization: Adaptive Sentence Selection based on Prevalence and Novelty (RM, CM, IO), pp. 301–310.
SIGIRSIGIR-2014-ParkS #detection #higher-order #modelling #probability
Second order probabilistic models for within-document novelty detection in academic articles (LAFP, SS), pp. 1103–1106.
SIGIRSIGIR-2014-Vargas #evaluation #information retrieval #recommendation
Novelty and diversity enhancement and evaluation in recommender systems and information retrieval (SV), p. 1281.
ECIRECIR-2013-BelemMAG #recommendation
Exploiting Novelty and Diversity in Tag Recommendation (FB, EFM, JMA, MAG), pp. 380–391.
KDDKDD-2013-RautiainenSHYK #concept #mining #online
An online system with end-user services: mining novelty concepts from tv broadcast subtitles (MR, JS, AH, MY, VK), pp. 1486–1489.
RecSysRecSys-2013-TaramigkouBCAM #music
Escape the bubble: guided exploration of music preferences for serendipity and novelty (MT, EB, KC, DA, GM), pp. 335–338.
SIGIRSIGIR-2013-AnH #analysis #information retrieval #probability #semantics
Boosting novelty for biomedical information retrieval through probabilistic latent semantic analysis (XA, JXH), pp. 829–832.
SIGIRSIGIR-2013-Belem #recommendation
Beyond relevance: on novelty and diversity in tag recommendation (FB), p. 1140.
SIGIRSIGIR-2013-ChandarC #evaluation #metric
Preference based evaluation measures for novelty and diversity (PC, BC), pp. 413–422.
SIGIRSIGIR-2013-SantosCAC #information retrieval
Diversity and novelty in information retrieval (RLTS, PC, ISA, FC), p. 1130.
SACSAC-2013-FariaGC #algorithm #data type #detection #multi #problem
Novelty detection algorithm for data streams multi-class problems (ERF, JG, ACPLFC), pp. 795–800.
CASECASE-2012-ZhuSZZ #assessment #kernel #multi
A novelty degradation assessment method for equipment based on multi-kernel SVDD (YZ, YS, XZ, YZ), pp. 753–756.
CIKMCIKM-2012-LeelanupabZJ #analysis #cumulative #parametricity
A comprehensive analysis of parameter settings for novelty-biased cumulative gain (TL, GZ, JMJ), pp. 1950–1954.
CIKMCIKM-2011-BarbosaB #crawling #modelling
Focusing on novelty: a crawling strategy to build diverse language models (LB, SB), pp. 755–764.
RecSysRecSys-2011-VargasC #metric #rank #recommendation
Rank and relevance in novelty and diversity metrics for recommender systems (SV, PC), pp. 109–116.
CIKMCIKM-2010-CarmelRY #on the
On the relationship between novelty and popularity of user-generated content (DC, HR, EYT), pp. 1509–1512.
CIKMCIKM-2010-LiSDX #multi #summary #topic
Exploiting novelty, coverage and balance for topic-focused multi-document summarization (XL, YDS, LD, CYX), pp. 1765–1768.
SACSAC-2010-BraynerCS #approach #detection #energy #network #quality #query #requirements
Dealing with application requirements and energy consumption in wireless sensor networks: a novelty detection approach for quality of query services (AB, ALVC, KMdS), pp. 786–790.
MLDMMLDM-2009-CeciALCFVM #data type #detection #mining #relational
Relational Frequent Patterns Mining for Novelty Detection from Data Streams (MC, AA, CL, CC, FF, CV, DM), pp. 427–439.
SIGIRSIGIR-2009-Schanda #detection
Novelty detection across different source types and languages (JS), p. 854.
SIGIRSIGIR-2008-ClarkeKCVABM #evaluation #information retrieval
Novelty and diversity in information retrieval evaluation (CLAC, MK, GVC, OV, AA, SB, IM), pp. 659–666.
ECIRECIR-2007-SweeneyCL #summary
Summarisation and Novelty: An Experimental Investigation (SOS, FC, DEL), pp. 745–748.
SIGIRSIGIR-2007-FernandezL #analysis #detection #using
Novelty detection using local context analysis (RTF, DEL), pp. 725–726.
SIGIRSIGIR-2007-ShahM #information management #named
DiscoverInfo: a tool for discovering information with relevance and novelty (CS, GM), p. 902.
SACSAC-2007-AlbertiniM #detection #network #self
A self-organizing neural network for detecting novelties (MKA, RFdM), pp. 462–466.
SACSAC-2007-SpinosaCG #approach #clustering #concept #data type #detection #named
OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams (EJS, ACPdLFdC, JG), pp. 448–452.
CIKMCIKM-2006-LiC #detection #topic #using
Improving novelty detection for general topics using sentence level information patterns (XL, WBC), pp. 238–247.
SIGIRSIGIR-2006-OtterbacherR #detection
Fact-focused novelty detection: a feasibility study (JO, DRR), pp. 687–688.
CIKMCIKM-2005-LiC05a #detection
Novelty detection based on sentence level patterns (XL, WBC), pp. 744–751.
ICMLICML-2004-SimsekB #abstraction #identification #learning #using
Using relative novelty to identify useful temporal abstractions in reinforcement learning (ÖS, AGB).
KDDKDD-2003-MaP #detection #online #sequence
Online novelty detection on temporal sequences (JM, SP), pp. 613–618.
SIGIRSIGIR-2003-AllanWB #detection #retrieval
Retrieval and novelty detection at the sentence level (JA, CW, AB), pp. 314–321.
KDDKDD-2002-YangZCJ #detection #topic
Topic-conditioned novelty detection (YY, JZ, JGC, CJ), pp. 688–693.
SIGIRSIGIR-2002-ZhangCM #adaptation #detection
Novelty and redundancy detection in adaptive filtering (YZ, JPC, TPM), pp. 81–88.
KDDKDD-2001-BasuMPG #using
Evaluating the novelty of text-mined rules using lexical knowledge (SB, RJM, KVP, JG), pp. 233–238.
ICPRICPR-v2-2000-HickinbothamA #detection
Novelty Detection in Airframe Strain Data (SJH, JA), pp. 2536–2539.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.