BibSLEIGH — Proceedings of the Sixth Conference on Recommender Systems

Pádraig Cunningham, Neil J. Hurley, Ido Guy, Sarabjot Singh Anand
Proceedings of the Sixth Conference on Recommender Systems
RecSys, 2012.

KER

@proceedings{RecSys-2012,
	acmid         = "2365952",
	address       = "Dublin, Ireland",
	editor        = "Pádraig Cunningham and Neil J. Hurley and Ido Guy and Sarabjot Singh Anand",
	isbn          = "978-1-4503-1270-7",
	publisher     = "{ACM}",
	title         = "{Proceedings of the Sixth Conference on Recommender Systems}",
	year          = 2012,
}

Contents (71 items)

RecSys-2012-Kohavi #online #statistics: Online controlled experiments: introduction, learnings, and humbling statistics (RK), pp. 1–2.
RecSys-2012-Knijnenburg #recommendation: Conducting user experiments in recommender systems (BPK), pp. 3–4.
RecSys-2012-NunesH #overview #perspective #recommendation: Personality-based recommender systems: an overview (MASNN, RH), pp. 5–6.
RecSys-2012-Amatriain #recommendation: Building industrial-scale real-world recommender systems (XA), pp. 7–8.
RecSys-2012-SaidTH #challenge #recommendation: The challenge of recommender systems challenges (AS, DT, AH), pp. 9–10.
RecSys-2012-RodriguezPZ #multi #optimisation #recommendation: Multiple objective optimization in recommender systems (MR, CP, EZ), pp. 11–18.
RecSys-2012-RibeiroLVZ #multi #recommendation: Pareto-efficient hybridization for multi-objective recommender systems (MTR, AL, AV, NZ), pp. 19–26.
RecSys-2012-CremonesiGT #elicitation: User effort vs. accuracy in rating-based elicitation (PC, FG, RT), pp. 27–34.
RecSys-2012-BostandjievOH #hybrid #interactive #named #recommendation #visual notation: TasteWeights: a visual interactive hybrid recommender system (SB, JO, TH), pp. 35–42.
RecSys-2012-KnijnenburgBOK #recommendation #social: Inspectability and control in social recommenders (BPK, SB, JO, AK), pp. 43–50.
RecSys-2012-ShaQMD #roadmap: Spotting trends: the wisdom of the few (XS, DQ, PM, MD), pp. 51–58.
RecSys-2012-Diaz-AvilesDSN #realtime #recommendation #social: Real-time top-n recommendation in social streams (EDA, LD, LST, WN), pp. 59–66.
RecSys-2012-YangSGL #network #on the #recommendation #social #using: On top-k recommendation using social networks (XY, HS, YG, YL), pp. 67–74.
RecSys-2012-MolingBR #feedback #recommendation: Optimal radio channel recommendations with explicit and implicit feedback (OM, LB, FR), pp. 75–82.
RecSys-2012-TakacsT #personalisation #ranking: Alternating least squares for personalized ranking (GT, DT), pp. 83–90.
RecSys-2012-YangCZLY #feedback #mining #music #recommendation: Local implicit feedback mining for music recommendation (DY, TC, WZ, QL, YY), pp. 91–98.
RecSys-2012-KluverNESR #how #question #rating: How many bits per rating? (DK, TTN, MDE, SS, JR), pp. 99–106.
RecSys-2012-StrickrothP #community #network #quality #recommendation: High quality recommendations for small communities: the case of a regional parent network (SS, NP), pp. 107–114.
RecSys-2012-LeviMDT #recommendation: Finding a needle in a haystack of reviews: cold start context-based hotel recommender system (AL, OM, CD, NT), pp. 115–122.
RecSys-2012-RaghavanGG #collaboration #overview #quality: Review quality aware collaborative filtering (SR, SG, JG), pp. 123–130.
RecSys-2012-HaririMB #music #recommendation #topic: Context-aware music recommendation based on latenttopic sequential patterns (NH, BM, RDB), pp. 131–138.
RecSys-2012-ShiKBLOH #collaboration #learning #named #rank: CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (YS, AK, LB, ML, NO, AH), pp. 139–146.
RecSys-2012-PradelUG #evaluation #metric #ranking: Ranking with non-random missing ratings: influence of popularity and positivity on evaluation metrics (BP, NU, PG), pp. 147–154.
RecSys-2012-NingK #linear #recommendation: Sparse linear methods with side information for top-n recommendations (XN, GK), pp. 155–162.
RecSys-2012-SchelterBM #pipes and filters #scalability #similarity: Scalable similarity-based neighborhood methods with MapReduce (SS, CB, VM), pp. 163–170.
RecSys-2012-AntunesCG #approach #development #recommendation: An approach to context-based recommendation in software development (BA, JC, PG), pp. 171–178.
RecSys-2012-ZhangTSWY #approach #image #recommendation #semantics: A semantic approach to recommending text advertisements for images (WZ, LT, XS, HW, YY), pp. 179–186.
RecSys-2012-Saez-TrumperQC #distance: Ads and the city: considering geographic distance goes a long way (DST, DQ, JC), pp. 187–194.
RecSys-2012-WeinsbergBIT #gender #named #obfuscation: BlurMe: inferring and obfuscating user gender based on ratings (UW, SB, SI, NT), pp. 195–202.
RecSys-2012-Herbrich #distributed #learning #online #realtime: Distributed, real-time bayesian learning in online services (RH), pp. 203–204.
RecSys-2012-Lempel #challenge #recommendation #web: Recommendation challenges in web media settings (RL), pp. 205–206.
RecSys-2012-Lamere #question #what: I’ve got 10 million songs in my pocket: now what? (PL), pp. 207–208.
RecSys-2012-AharonKLK #elicitation #personalisation #recommendation: Dynamic personalized recommendation of comment-eliciting stories (MA, AK, RL, YK), pp. 209–212.
RecSys-2012-BelloginP #clustering #collaboration #graph #using: Using graph partitioning techniques for neighbour selection in user-based collaborative filtering (AB, JP), pp. 213–216.
RecSys-2012-BollenGW #memory management #retrieval: Remembering the stars?: effect of time on preference retrieval from memory (DGFMB, MPG, MCW), pp. 217–220.
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-PessemierDM #design #evaluation #recommendation: Design and evaluation of a group recommender system (TDP, SD, LM), pp. 225–228.
RecSys-2012-Diaz-AvilesGN #rank #recommendation: Swarming to rank for recommender systems (EDA, MG, WN), pp. 229–232.
RecSys-2012-EkstrandR #algorithm #predict #recommendation: When recommenders fail: predicting recommender failure for algorithm selection and combination (MDE, JR), pp. 233–236.
RecSys-2012-HuangXP #matrix: Constrained collective matrix factorization (YJH, EWX, RP), pp. 237–240.
RecSys-2012-JiangJFZ #recommendation: Recommending academic papers via users’ reading purposes (YJ, AJ, YF, DZ), pp. 241–244.
RecSys-2012-LiuXCGXBZ #recommendation: Influential seed items recommendation (QL, BX, EC, YG, HX, TB, YZ), pp. 245–248.
RecSys-2012-Manzato #recommendation: Discovering latent factors from movies genres for enhanced recommendation (MGM), pp. 249–252.
RecSys-2012-NoiaMOR #modelling #recommendation #web: Exploiting the web of data in model-based recommender systems (TDN, RM, VCO, DR), pp. 253–256.
RecSys-2012-PraweshP #feedback #probability #recommendation: Probabilistic news recommender systems with feedback (SP, BP), pp. 257–260.
RecSys-2012-SalimansPG #collaboration #learning #ranking: Collaborative learning of preference rankings (TS, UP, TG), pp. 261–264.
RecSys-2012-WuGRR #microblog #recommendation: Making recommendations in a microblog to improve the impact of a focal user (SW, LG, WR, LR), pp. 265–268.
RecSys-2012-Zanker #information management #recommendation: The influence of knowledgeable explanations on users’ perception of a recommender system (MZ), pp. 269–272.
RecSys-2012-AminYSBP #delivery #network #recommendation #social: Social referral: leveraging network connections to deliver recommendations (MSA, BY, SS, AB, CP), pp. 273–276.
RecSys-2012-BellufXG #case study #online #personalisation #recommendation #scalability: Case study on the business value impact of personalized recommendations on a large online retailer (TB, LX, RG), pp. 277–280.
RecSys-2012-KoenigsteinNPS #recommendation: The Xbox recommender system (NK, NN, UP, NS), pp. 281–284.
RecSys-2012-LiuCCY #named #recommendation: Enlister: baidu’s recommender system for the biggest chinese Q&A website (QL, TC, JC, DY), pp. 285–288.
RecSys-2012-SmythCB #deployment #named #social: HeyStaks: a real-world deployment of social search (BS, MC, PB), pp. 289–292.
RecSys-2012-BrigadirGC #twitter: A system for twitter user list curation (IB, DG, PC), pp. 293–294.
RecSys-2012-ChhabraR #named #recommendation: CubeThat: news article recommender (SC, PR), pp. 295–296.
RecSys-2012-DongSOMS: The demonstration of the reviewer’s assistant (RD, MS, MPO, KM, BS), pp. 297–298.
RecSys-2012-GertnerLW #enterprise #recommendation: Recommenders for the enterprise: event, contact, and group (ASG, BL, JW), pp. 299–300.
RecSys-2012-Griffin: Integrated content marketing (JG), pp. 301–302.
RecSys-2012-Lathia #health #using: Using ratings to profile your health (NL), pp. 303–304.
RecSys-2012-LeviMDT12a #recommendation: Finding a needle in a haystack of reviews: cold start context-based hotel recommender system demo (AL, OM, CD, NT), pp. 305–306.
RecSys-2012-PhelanMS #named #realtime #twitter #using: Yokie: explorations in curated real-time search & discovery using twitter (OP, KM, BS), pp. 307–308.
RecSys-2012-SheehanP #named #personalisation #predict #student: pGPA: a personalized grade prediction tool to aid student success (MS, YP), pp. 309–310.
RecSys-2012-SklarSH #realtime #recommendation: Recommending interesting events in real-time with foursquare check-ins (MS, BS, AH), pp. 311–312.
RecSys-2012-Heitmann #framework #graph #multi #personalisation #semantics: An open framework for multi-source, cross-domain personalisation with semantic interest graphs (BH), pp. 313–316.
RecSys-2012-KarimiFNS #learning #matrix #recommendation: Exploiting the characteristics of matrix factorization for active learning in recommender systems (RK, CF, AN, LST), pp. 317–320.
RecSys-2012-KramarB #personalisation: Dynamically selecting an appropriate context type for personalisation (TK, MB), pp. 321–324.
RecSys-2012-Landia #documentation #folksonomy #recommendation: Utilising document content for tag recommendation in folksonomies (NL), pp. 325–328.
RecSys-2012-Ninaus #heuristic #recommendation #requirements #using: Using group recommendation heuristics for the prioritization of requirements (GN), pp. 329–332.
RecSys-2012-Parra #recommendation #visualisation: Beyond lists: studying the effect of different recommendation visualizations (DP), pp. 333–336.
RecSys-2012-Wakeling #design #library #recommendation: The user-centered design of a recommender system for a universal library catalogue (SW), pp. 337–340.
RecSys-2012-ZelenikB #information management #recommendation: Reducing the sparsity of contextual information for recommender systems (DZ, MB), pp. 341–344.

Pádraig Cunningham, Neil J. Hurley, Ido Guy, Sarabjot Singh AnandProceedings of the Sixth Conference on Recommender SystemsRecSys, 2012.

Contents (71 items)

Pádraig Cunningham, Neil J. Hurley, Ido Guy, Sarabjot Singh Anand
Proceedings of the Sixth Conference on Recommender Systems
RecSys, 2012.