BibSLEIGH — Proceedings of the Fifth Conference on Recommender Systems

Bamshad Mobasher, Robin D. Burke, Dietmar Jannach, Gediminas Adomavicius
Proceedings of the Fifth Conference on Recommender Systems
RecSys, 2011.

KER

@proceedings{RecSys-2011,
	address       = "Chicago, Illinois, USA",
	editor        = "Bamshad Mobasher and Robin D. Burke and Dietmar Jannach and Gediminas Adomavicius",
	isbn          = "978-1-4503-0683-6",
	publisher     = "{ACM}",
	title         = "{Proceedings of the Fifth Conference on Recommender Systems}",
	year          = 2011,
}

Contents (67 items)

RecSys-2011-Sundaresan #recommendation: Recommender systems at the long tail (NS), pp. 1–6.
RecSys-2011-CelmaL #music #recommendation #revisited: Music recommendation and discovery revisited (ÒC, PL), pp. 7–8.
RecSys-2011-Hurley #recommendation #robust: Robustness of recommender systems (NJH), pp. 9–10.
RecSys-2011-Tunkelang #recommendation: Recommendations as a conversation with the user (DT), pp. 11–12.
RecSys-2011-ZhangAC #flexibility #matrix: Generalizing matrix factorization through flexible regression priors (LZ, DA, BCC), pp. 13–20.
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-ZhaoFLL #collaboration: Shared collaborative filtering (YZ, XF, JL, BL), pp. 29–36.
RecSys-2011-LiuMLY #elicitation #rating #recommendation: Wisdom of the better few: cold start recommendation via representative based rating elicitation (NNL, XM, CL, QY), pp. 37–44.
RecSys-2011-KimE #personalisation #rank #recommendation: Personalized PageRank vectors for tag recommendations: inside FolkRank (HNK, AES), pp. 45–52.
RecSys-2011-JamaliHE #network #probability #rating #recommendation #social: A generalized stochastic block model for recommendation in social rating networks (MJ, TH, ME), pp. 53–60.
RecSys-2011-SymeonidisTM #multi #network #predict #rating #recommendation #social: Product recommendation and rating prediction based on multi-modal social networks (PS, ET, YM), pp. 61–68.
RecSys-2011-IsaacmanICM #distributed #predict #rating: Distributed rating prediction in user generated content streams (SI, SI, AC, MM), pp. 69–76.
RecSys-2011-LiuMX #multi #recommendation: Multi-criteria service recommendation based on user criteria preferences (LL, NM, DLX), pp. 77–84.
RecSys-2011-GorgoglionePT #behaviour #recommendation #trust: The effect of context-aware recommendations on customer purchasing behavior and trust (MG, UP, AT), pp. 85–92.
RecSys-2011-LeeSKLL #graph #multi #random #ranking #recommendation: Random walk based entity ranking on graph for multidimensional recommendation (SL, SiS, MK, DL, SgL), pp. 93–100.
RecSys-2011-SekoYMM #behaviour #recommendation #representation #using: Group recommendation using feature space representing behavioral tendency and power balance among members (SS, TY, MM, SyM), pp. 101–108.
RecSys-2011-VargasC #metric #rank #recommendation: Rank and relevance in novelty and diversity metrics for recommender systems (SV, PC), pp. 109–116.
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.
RecSys-2011-EkstrandLKR #ecosystem #recommendation #research: Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit (MDE, ML, JAK, JR), pp. 133–140.
RecSys-2011-KnijnenburgRW #how #interactive #recommendation: Each to his own: how different users call for different interaction methods in recommender systems (BPK, NJMR, MCW), pp. 141–148.
RecSys-2011-SparlingS #how #named #question #rating: Rating: how difficult is it? (EIS, SS), pp. 149–156.
RecSys-2011-PuCH #evaluation #framework #recommendation: A user-centric evaluation framework for recommender systems (PP, LC, RH), pp. 157–164.
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-TayebiJEGF #named #recommendation: CrimeWalker: a recommendation model for suspect investigation (MAT, MJ, ME, UG, RF), pp. 173–180.
RecSys-2011-GuyRR #personalisation #process: Personalized activity streams: sifting through the “river of news” (IG, IR, AR), pp. 181–188.
RecSys-2011-Makrehchi #learning #recommendation #social #topic: Social link recommendation by learning hidden topics (MM), pp. 189–196.
RecSys-2011-HuP #collaboration: Enhancing collaborative filtering systems with personality information (RH, PP), pp. 197–204.
RecSys-2011-AnandG #approach #problem: A market-based approach to address the new item problem (SSA, NG), pp. 205–212.
RecSys-2011-LeeL #analysis #behaviour #music #recommendation: My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation (KL, KL), pp. 213–220.
RecSys-2011-XinS #matrix #multi #probability #recommendation: Multi-value probabilistic matrix factorization for IP-TV recommendations (YX, HS), pp. 221–228.
RecSys-2011-JojicSB #probability #similarity: A probabilistic definition of item similarity (OJ, MS, NB), pp. 229–236.
RecSys-2011-PraweshP #recommendation: The “top N” news recommender: count distortion and manipulation resistance (SP, BP), pp. 237–244.
RecSys-2011-YuanCZ #recommendation #social: Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation (QY, LC, SZ), pp. 245–252.
RecSys-2011-BraunhoferKR #mobile #music #recommendation: Recommending music for places of interest in a mobile travel guide (MB, MK, FR), pp. 253–256.
RecSys-2011-YuPL #adaptation #recommendation #social: Adaptive social similarities for recommender systems (LY, RP, ZL), pp. 257–260.
RecSys-2011-ForbesZ #matrix #recommendation: Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation (PF, MZ), pp. 261–264.
RecSys-2011-GuzziRB #interactive #multi #recommendation: Interactive multi-party critiquing for group recommendation (FG, FR, RDB), pp. 265–268.
RecSys-2011-WoerndlHBG #mobile #recommendation: A model for proactivity in mobile, context-aware recommender systems (WW, JH, RB, DGV), pp. 273–276.
RecSys-2011-DalyG #effectiveness #recommendation #social #using: Effective event discovery: using location and social information for scoping event recommendations (EMD, WG), pp. 277–280.
RecSys-2011-GeXTL #collaboration: Collaborative filtering with collective training (YG, HX, AT, QL), pp. 281–284.
RecSys-2011-KatzOSRS #collaboration #using #wiki: Using Wikipedia to boost collaborative filtering techniques (GK, NO, BS, LR, GS), pp. 285–288.
RecSys-2011-WuCMW #detection #learning #named: Semi-SAD: applying semi-supervised learning to shilling attack detection (ZW, JC, BM, YW), pp. 289–292.
RecSys-2011-LopsGSNM #network #social: Leveraging the linkedin social network data for extracting content-based user profiles (PL, MdG, GS, FN, CM), pp. 293–296.
RecSys-2011-TakacsPT #collaboration #feedback: Applications of the conjugate gradient method for implicit feedback collaborative filtering (GT, IP, DT), pp. 297–300.
RecSys-2011-BaltrunasLR #matrix #recommendation: Matrix factorization techniques for context aware recommendation (LB, BL, FR), pp. 301–304.
RecSys-2011-GantnerRFS #library #named #recommendation: MyMediaLite: a free recommender system library (ZG, SR, CF, LST), pp. 305–308.
RecSys-2011-CamposDS #evaluation #matrix #predict #recommendation #testing #towards: Towards a more realistic evaluation: testing the ability to predict future tastes of matrix factorization-based recommenders (PGC, FD, MASM), pp. 309–312.
RecSys-2011-Karatzoglou #collaboration #modelling #order: Collaborative temporal order modeling (AK), pp. 313–316.
RecSys-2011-LiZL #integration #named #personalisation #recommendation: LOGO: a long-short user interest integration in personalized news recommendation (LL, LZ, TL), pp. 317–320.
RecSys-2011-KnijnenburgWK #evaluation #recommendation: A pragmatic procedure to support the user-centric evaluation of recommender systems (BPK, MCW, AK), pp. 321–324.
RecSys-2011-PaparrizosCG #recommendation: Machine learned job recommendation (IKP, BBC, AG), pp. 325–328.
RecSys-2011-WangSS #e-commerce #recommendation: Utilizing related products for post-purchase recommendation in e-commerce (JW, BS, NS), pp. 329–332.
RecSys-2011-BelloginCC #algorithm #comparison #evaluation #recommendation: Precision-oriented evaluation of recommender systems: an algorithmic comparison (AB, PC, IC), pp. 333–336.
RecSys-2011-BourkeMS #people #recommendation #social: Power to the people: exploring neighbourhood formations in social recommender system (SB, KM, BS), pp. 337–340.
RecSys-2011-PizzatoS #collaboration #people #probability #recommendation: Stochastic matching and collaborative filtering to recommend people to people (LASP, CS), pp. 341–344.
RecSys-2011-WuRR #monitoring #recommendation #social #social media: Recommendations in social media for brand monitoring (SW, WR, LR), pp. 345–348.
RecSys-2011-EkstrandLKR11a #composition #framework #named #recommendation: LensKit: a modular recommender framework (MDE, ML, JK, JR), pp. 349–350.
RecSys-2011-SabinC #named #online #recommendation: myMicSound: an online sound-based microphone recommendation system (ATS, CLC), pp. 351–352.
RecSys-2011-DayanKBRSASF #benchmark #framework #metric #recommendation: Recommenders benchmark framework (AD, GK, NB, LR, BS, AA, RS, RF), pp. 353–354.
RecSys-2011-Faridani #analysis #canonical #correlation #recommendation #sentiment #using: Using canonical correlation analysis for generalized sentiment analysis, product recommendation and search (SF), pp. 355–358.
RecSys-2011-Tschersich #design #guidelines #mobile #recommendation: Design guidelines for mobile group recommender systems to handle inaccurate or missing location data (MT), pp. 359–362.
RecSys-2011-Chen #design #interactive #interface #recommendation #social: Interface and interaction design for group and social recommender systems (YC), pp. 363–366.
RecSys-2011-Alam #clustering #recommendation #web: Intelligent web usage clustering based recommender system (SA), pp. 367–370.
RecSys-2011-Bellogin #performance #predict #recommendation: Predicting performance in recommender systems (AB), pp. 371–374.
RecSys-2011-Zhang #recommendation: Anchoring effects of recommender systems (JZ), pp. 375–378.
RecSys-2011-SaidBLH #challenge #recommendation: Challenge on context-aware movie recommendation: CAMRa2011 (AS, SB, EWDL, JH), pp. 385–386.

Bamshad Mobasher, Robin D. Burke, Dietmar Jannach, Gediminas AdomaviciusProceedings of the Fifth Conference on Recommender SystemsRecSys, 2011.

Contents (67 items)

Bamshad Mobasher, Robin D. Burke, Dietmar Jannach, Gediminas Adomavicius
Proceedings of the Fifth Conference on Recommender Systems
RecSys, 2011.