BibSLEIGH — Proceedings of the Seventh Conference on Recommender Systems

Qiang Yang, Irwin King, Qing Li, Pearl Pu, George Karypis
Proceedings of the Seventh Conference on Recommender Systems
RecSys, 2013.

KER

@proceedings{RecSys-2013,
	acmid         = "2507157",
	address       = "Hong Kong, China",
	editor        = "Qiang Yang and Irwin King and Qing Li and Pearl Pu and George Karypis",
	isbn          = "978-1-4503-2409-0",
	publisher     = "{ACM}",
	title         = "{Proceedings of the Seventh Conference on Recommender Systems}",
	year          = 2013,
}

Contents (92 items)

RecSys-2013-TangGHL #overview #predict #rating: Context-aware review helpfulness rating prediction (JT, HG, XH, HL), pp. 1–8.
RecSys-2013-HaririMB #recommendation: Query-driven context aware recommendation (NH, BM, RDB), pp. 9–16.
RecSys-2013-KaminskasRS #hybrid #music #recommendation #using: Location-aware music recommendation using auto-tagging and hybrid matching (MK, FR, MS), pp. 17–24.
RecSys-2013-HuE #modelling #online #recommendation #social #social media #topic: Spatial topic modeling in online social media for location recommendation (BH, ME), pp. 25–32.
RecSys-2013-VahabiALBL #orthogonal #query #recommendation: Orthogonal query recommendation (HV, MA, DL, RABY, ALO), pp. 33–40.
RecSys-2013-PuF #comprehension #matrix #recommendation #relational: Understanding and improving relational matrix factorization in recommender systems (LP, BF), pp. 41–48.
RecSys-2013-KoyejoAG #collaboration #matrix: Retargeted matrix factorization for collaborative filtering (OK, SA, JG), pp. 49–56.
RecSys-2013-Shi #approach #graph #recommendation #similarity: Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach (LS), pp. 57–64.
RecSys-2013-WestonWY #multi: Nonlinear latent factorization by embedding multiple user interests (JW, RJW, HY), pp. 65–68.
RecSys-2013-PanCCY #personalisation #recommendation #social: Diffusion-aware personalized social update recommendation (YP, FC, KC, YY), pp. 69–76.
RecSys-2013-ZhangP #recommendation #social #social media: Recommending branded products from social media (YZ, MP), pp. 77–84.
RecSys-2013-OstuniNSM #feedback #linked data #open data #recommendation: Top-N recommendations from implicit feedback leveraging linked open data (VCO, TDN, EDS, RM), pp. 85–92.
RecSys-2013-GaoTHL #network #recommendation #social: Exploring temporal effects for location recommendation on location-based social networks (HG, JT, XH, HL), pp. 93–100.
RecSys-2013-SaayaRSS #challenge #recommendation #web: The curated web: a recommendation challenge (ZS, RR, MS, BS), pp. 101–104.
RecSys-2013-GarcinDF #personalisation #recommendation: Personalized news recommendation with context trees (FG, CD, BF), pp. 105–112.
RecSys-2013-PeraN #personalisation #recommendation #what: What to read next?: making personalized book recommendations for K-12 users (MSP, YKN), pp. 113–120.
RecSys-2013-AzariaHKEWN #recommendation: Movie recommender system for profit maximization (AA, AH, SK, AE, OW, IN), pp. 121–128.
RecSys-2013-KoenigsteinP #embedded #feature model #matrix #recommendation: Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection (NK, UP), pp. 129–136.
RecSys-2013-WuLCHLCH #online #personalisation #recommendation: Personalized next-song recommendation in online karaokes (XW, QL, EC, LH, JL, CC, GH), pp. 137–140.
RecSys-2013-BelemSAG #recommendation #topic: Topic diversity in tag recommendation (FB, RLTS, JMA, MAG), pp. 141–148.
RecSys-2013-NguyenKWHEWR #experience #rating #recommendation #user interface: Rating support interfaces to improve user experience and recommender accuracy (TTN, DK, TYW, PMH, MDE, MCW, JR), pp. 149–156.
RecSys-2013-GraschFR #interactive #named #recommendation #speech #towards: ReComment: towards critiquing-based recommendation with speech interaction (PG, AF, FR), pp. 157–164.
RecSys-2013-McAuleyL #comprehension #overview #rating #topic: Hidden factors and hidden topics: understanding rating dimensions with review text (JJM, JL), pp. 165–172.
RecSys-2013-ZhangSKH #artificial reality #recommendation #using: Improving augmented reality using recommender systems (ZZ, SS, SRK, PH), pp. 173–176.
RecSys-2013-MouraoRKM #hybrid #recommendation: Exploiting non-content preference attributes through hybrid recommendation method (FM, LCdR, JAK, WMJ), pp. 177–184.
RecSys-2013-KhroufT #hybrid #linked data #open data #recommendation #using: Hybrid event recommendation using linked data and user diversity (HK, RT), pp. 185–192.
RecSys-2013-SharmaY #community #learning #recommendation: Pairwise learning in recommendation: experiments with community recommendation on linkedin (AS, BY), pp. 193–200.
RecSys-2013-NatarajanSD #collaboration: Which app will you use next?: collaborative filtering with interactional context (NN, DS, ISD), pp. 201–208.
RecSys-2013-PessemierDM13a #recommendation: A food recommender for patients in a care facility (TDP, SD, LM), pp. 209–212.
RecSys-2013-Steck #evaluation #predict #ranking #recommendation: Evaluation of recommendations: rating-prediction and ranking (HS), pp. 213–220.
RecSys-2013-BabasCT #personalisation #recommendation #what: You are what you consume: a bayesian method for personalized recommendations (KB, GC, ET), pp. 221–228.
RecSys-2013-ZhangWCZ #personalisation #perspective #risk management: To personalize or not: a risk management perspective (WZ, JW, BC, XZ), pp. 229–236.
RecSys-2013-WangHZL #collaboration #multi #on the fly #online #recommendation: Online multi-task collaborative filtering for on-the-fly recommender systems (JW, SCHH, PZ, ZL), pp. 237–244.
RecSys-2013-WestonYW #learning #rank #recommendation #statistics: Learning to rank recommendations with the k-order statistic loss (JW, HY, RJW), pp. 245–248.
RecSys-2013-ZhuangCJL #matrix #memory management #parallel #performance: A fast parallel SGD for matrix factorization in shared memory systems (YZ, WSC, YCJ, CJL), pp. 249–256.
RecSys-2013-Kyrola #named #random: DrunkardMob: billions of random walks on just a PC (AK), pp. 257–264.
RecSys-2013-HammarKN #e-commerce #recommendation #using: Using maximum coverage to optimize recommendation systems in e-commerce (MH, RK, BJN), pp. 265–272.
RecSys-2013-Aiolli #dataset #performance #recommendation #scalability: Efficient top-n recommendation for very large scale binary rated datasets (FA), pp. 273–280.
RecSys-2013-SchelterBSAM #distributed #matrix #pipes and filters #using: Distributed matrix factorization with mapreduce using a series of broadcast-joins (SS, CB, MS, AA, VM), pp. 281–284.
RecSys-2013-XuBATMK #recommendation: Catch-up TV recommendations: show old favourites and find new ones (MX, SB, SA, ST, AM, IK), pp. 285–294.
RecSys-2013-LiuAYB #generative #using: Generating supplemental content information using virtual profiles (HL, MSA, BY, AB), pp. 295–302.
RecSys-2013-AlanaziB #markov #modelling #recommendation #using: A people-to-people content-based reciprocal recommender using hidden markov models (AA, MB), pp. 303–306.
RecSys-2013-BlancoR #feedback #recommendation: Acquiring user profiles from implicit feedback in a conversational recommender system (HB, FR), pp. 307–310.
RecSys-2013-AzariaKR #multi #problem: A system for advice provision in multiple prospectselection problems (AA, SK, AR), pp. 311–314.
RecSys-2013-MirbakhshL #clustering #collaboration: Clustering-based factorized collaborative filtering (NM, CXL), pp. 315–318.
RecSys-2013-TiroshiBKCK #network #predict #social: Cross social networks interests predictions based ongraph features (AT, SB, MAK, TC, TK), pp. 319–322.
RecSys-2013-ChowJKS #data analysis #difference #recommendation: Differential data analysis for recommender systems (RC, HJ, BPK, GS), pp. 323–326.
RecSys-2013-ZhengI #effectiveness: Effectiveness of the data generated on different time in latent factor model (QZ, HHSI), pp. 327–330.
RecSys-2013-HuY #learning #process #recommendation: Interview process learning for top-n recommendation (FH, YY), pp. 331–334.
RecSys-2013-TaramigkouBCAM #music: Escape the bubble: guided exploration of music preferences for serendipity and novelty (MT, EB, KC, DA, GM), pp. 335–338.
RecSys-2013-CremonesiGQ #recommendation: Evaluating top-n recommendations “when the best are gone” (PC, FG, MQ), pp. 339–342.
RecSys-2013-DoerfelJ #analysis #evaluation #recommendation: An analysis of tag-recommender evaluation procedures (SD, RJ), pp. 343–346.
RecSys-2013-YuRSSKGNH #feedback #network #recommendation: Recommendation in heterogeneous information networks with implicit user feedback (XY, XR, YS, BS, UK, QG, BN, JH), pp. 347–350.
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-GaoHBLLZ #predict #social #social media: Improving user profile with personality traits predicted from social media content (RG, BH, SB, LL, AL, TZ), pp. 355–358.
RecSys-2013-BlankRS #graph #keyword #recommendation: Leveraging the citation graph to recommend keywords (IB, LR, GS), pp. 359–362.
RecSys-2013-CodinaRC #modelling #semantics: Local context modeling with semantic pre-filtering (VC, FR, LC), pp. 363–366.
RecSys-2013-BugaychenkoD #network #personalisation #recommendation #social: Musical recommendations and personalization in a social network (DB, AD), pp. 367–370.
RecSys-2013-DzyaburaT #how #recommendation: Not by search alone: how recommendations complement search results (DD, AT), pp. 371–374.
RecSys-2013-AharonABLABLRS #named #online #persistent #recommendation #set: OFF-set: one-pass factorization of feature sets for online recommendation in persistent cold start settings (MA, NA, EB, RL, RA, TB, LL, RR, OS), pp. 375–378.
RecSys-2013-Hurley #personalisation #ranking: Personalised ranking with diversity (NJH), pp. 379–382.
RecSys-2013-GuoZTY #e-commerce #recommendation: Prior ratings: a new information source for recommender systems in e-commerce (GG, JZ, DT, NYS), pp. 383–386.
RecSys-2013-BelloginPC #collaboration #probability: Probabilistic collaborative filtering with negative cross entropy (AB, JP, PC), pp. 387–390.
RecSys-2013-SavirBS #recommendation: Recommending improved configurations for complex objects with an application in travel planning (AS, RIB, GS), pp. 391–394.
RecSys-2013-KrestelS #recommendation #topic: Recommending patents based on latent topics (RK, PS), pp. 395–398.
RecSys-2013-TianJ #graph #recommendation #using: Recommending scientific articles using bi-relational graph-based iterative RWR (GT, LJ), pp. 399–402.
RecSys-2013-SilbermannBR #recommendation: Sample selection for MCMC-based recommender systems (TS, IB, SR), pp. 403–406.
RecSys-2013-RonenKZN #collaboration #recommendation: Selecting content-based features for collaborative filtering recommenders (RR, NK, EZ, NN), pp. 407–410.
RecSys-2013-DongOSMS #recommendation #sentiment: Sentimental product recommendation (RD, MPO, MS, KM, BS), pp. 411–414.
RecSys-2013-SuYCY #personalisation #ranking #recommendation: Set-oriented personalized ranking for diversified top-n recommendation (RS, LY, KC, YY), pp. 415–418.
RecSys-2013-KoenigsteinK #recommendation #scalability #towards: Towards scalable and accurate item-oriented recommendations (NK, YK), pp. 419–422.
RecSys-2013-OstrikovRS #collaboration #metadata #using: Using geospatial metadata to boost collaborative filtering (AO, LR, BS), pp. 423–426.
RecSys-2013-WilsonS #collaboration #recommendation: When power users attack: assessing impacts in collaborative recommender systems (DCW, CES), pp. 427–430.
RecSys-2013-ShiKBLH #multi #named #optimisation #rank: xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance (YS, AK, LB, ML, AH), pp. 431–434.
RecSys-2013-BartelD #evolution #network #social: Evolving friend lists in social networks (JWB, PD), pp. 435–438.
RecSys-2013-LacerdaVZ #interactive #recommendation: Exploratory and interactive daily deals recommendation (AL, AV, NZ), pp. 439–442.
RecSys-2013-Dooms #generative #hybrid #personalisation #recommendation: Dynamic generation of personalized hybrid recommender systems (SD), pp. 443–446.
RecSys-2013-Seminario #collaboration #recommendation #robust: Accuracy and robustness impacts of power user attacks on collaborative recommender systems (CES), pp. 447–450.
RecSys-2013-Guo #recommendation #similarity #trust: Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems (GG), pp. 451–454.
RecSys-2013-TaghaviBS #recommendation: Agent-based computational investing recommender system (MT, KB, ES), pp. 455–458.
RecSys-2013-Adamopoulos #predict #rating #recommendation: Beyond rating prediction accuracy: on new perspectives in recommender systems (PA), pp. 459–462.
RecSys-2013-Ben-Shimon #algorithm #recommendation: Anytime algorithms for top-N recommenders (DBS), pp. 463–466.
RecSys-2013-KucharK #case study #learning #named #web #web service: GAIN: web service for user tracking and preference learning — a smart TV use case (JK, TK), pp. 467–468.
RecSys-2013-GarcinF #framework #personalisation #recommendation: PEN RecSys: a personalized news recommender systems framework (FG, BF), pp. 469–470.
RecSys-2013-AhnPLL #graph #recommendation: A heterogeneous graph-based recommendation simulator (YA, SP, SL, SgL), pp. 471–472.
RecSys-2013-NewellM #design #evaluation #recommendation: Design and evaluation of a client-side recommender system (CN, LM), pp. 473–474.
RecSys-2013-RonenKZSYH #named #recommendation: Sage: recommender engine as a cloud service (RR, NK, EZ, MS, RY, NHW), pp. 475–476.
RecSys-2013-BlomoEF #challenge: RecSys challenge 2013 (JB, ME, MF), pp. 489–490.
RecSys-2013-Ester #network #recommendation #social: Recommendation in social networks (ME), pp. 491–492.
RecSys-2013-KaratzoglouBS #learning #rank #recommendation: Learning to rank for recommender systems (AK, LB, YS), pp. 493–494.
RecSys-2013-PizzatoB #network #people #recommendation #social: Beyond friendship: the art, science and applications of recommending people to people in social networks (LAP, AB), pp. 495–496.
RecSys-2013-TsoukiasV #tutorial: Tutorial on preference handling (AT, PV), pp. 497–498.

Qiang Yang, Irwin King, Qing Li, Pearl Pu, George KarypisProceedings of the Seventh Conference on Recommender SystemsRecSys, 2013.

Contents (92 items)

Qiang Yang, Irwin King, Qing Li, Pearl Pu, George Karypis
Proceedings of the Seventh Conference on Recommender Systems
RecSys, 2013.