Xavier Amatriain, Marc Torrens, Paul Resnick, Markus Zanker
Proceedings of the Fourth Conference on Recommender Systems
RecSys, 2010.

@proceedings{RecSys-2010,
	address       = "Barcelona, Spain",
	editor        = "Xavier Amatriain and Marc Torrens and Paul Resnick and Markus Zanker",
	isbn          = "978-1-60558-906-0",
	publisher     = "{ACM}",
	title         = "{Proceedings of the Fourth Conference on Recommender Systems}",
	year          = 2010,
}

Contents (71 items)

51 ×#recommendation
15 ×#collaboration
12 ×#social
6 ×#algorithm
6 ×#network
6 ×#online
5 ×#named
5 ×#predict
3 ×#approach
3 ×#learning

RecSys-2010-Shani #recommendation #tutorial

Tutorial on evaluating recommender systems (GS), p. 1.

RecSys-2010-Baeza-Yates #predict #query #recommendation

Query intent prediction and recommendation (RABY), pp. 5–6.

RecSys-2010-GuyJAMNNS #industrial #perspective #recommendation

Will recommenders kill search?: recommender systems — an industry perspective (IG, AJ, PA, PM, PN, CN, HS), pp. 7–12.

RecSys-2010-SandholmUAH #recommendation

Global budgets for local recommendations (TS, HU, CA, BAH), pp. 13–20.

RecSys-2010-DesarkarSM #collaboration #graph #predict #rating

Aggregating preference graphs for collaborative rating prediction (MSD, SS, PM), pp. 21–28.

RecSys-2010-CastagnosJP #recommendation

Eye-tracking product recommenders’ usage (SC, NJ, PP), pp. 29–36.

RecSys-2010-ResnickKHP #contest #named #question

Contests: way forward or detour? (PR, JAK, AH, JP), pp. 37–38.

RecSys-2010-CremonesiKT #algorithm #performance #recommendation

Performance of recommender algorithms on top-n recommendation tasks (PC, YK, RT), pp. 39–46.

RecSys-2010-AdomaviciusZ #algorithm #on the #recommendation

On the stability of recommendation algorithms (GA, JZ), pp. 47–54.

RecSys-2010-JamborW #collaboration #multi #optimisation

Optimizing multiple objectives in collaborative filtering (TJ, JW), pp. 55–62.

RecSys-2010-BollenKWG #comprehension #recommendation

Understanding choice overload in recommender systems (DGFMB, BPK, MCW, MPG), pp. 63–70.

RecSys-2010-PilaszyZT #dataset #feedback #matrix #performance

Fast als-based matrix factorization for explicit and implicit feedback datasets (IP, DZ, DT), pp. 71–78.

RecSys-2010-KaratzoglouABO #collaboration #multi #recommendation

Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering (AK, XA, LB, NO), pp. 79–86.

RecSys-2010-KhoshneshinS #collaboration

Collaborative filtering via euclidean embedding (MK, WNS), pp. 87–94.

RecSys-2010-LiuZXY #collaboration #online

Online evolutionary collaborative filtering (NNL, MZ, EWX, QY), pp. 95–102.

RecSys-2010-VasukiNLD #network #recommendation #using

Affiliation recommendation using auxiliary networks (VV, NN, ZL, ISD), pp. 103–110.

RecSys-2010-BerkovskyF #analysis #recommendation

Group-based recipe recommendations: analysis of data aggregation strategies (SB, JF), pp. 111–118.

RecSys-2010-BaltrunasMR #collaboration #rank #recommendation

Group recommendations with rank aggregation and collaborative filtering (LB, TM, FR), pp. 119–126.

RecSys-2010-LappasG #interactive #network #recommendation #social

Interactive recommendations in social endorsement networks (TL, DG), pp. 127–134.

RecSys-2010-JamaliE #matrix #network #recommendation #social #trust

A matrix factorization technique with trust propagation for recommendation in social networks (MJ, ME), pp. 135–142.

RecSys-2010-ZhaoZYZZF #recommendation #social #what

Who is talking about what: social map-based recommendation for content-centric social websites (SZ, MXZ, QY, XZ, WZ, RF), pp. 143–150.

RecSys-2010-XieLW #recommendation

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

RecSys-2010-EkstrandKSBKR #automation #research

Automatically building research reading lists (MDE, PK, JAS, JTB, JAK, JR), pp. 159–166.

RecSys-2010-LipczakM #learning #performance #recommendation

Learning in efficient tag recommendation (ML, EEM), pp. 167–174.

RecSys-2010-BerkovskyFCB #algorithm #game studies #process #recommendation

Recommender algorithms in activity motivating games (SB, JF, MC, DB), pp. 175–182.

RecSys-2010-SymeonidisTM #network #predict #similarity #social #transitive

Transitive node similarity for link prediction in social networks with positive and negative links (PS, ET, YM), pp. 183–190.

RecSys-2010-BaglioniBBCFVP #lightweight #mobile #privacy #recommendation

A lightweight privacy preserving SMS-based recommendation system for mobile users (EB, LB, LB, UMC, LF, AV, GP), pp. 191–198.

RecSys-2010-HannonBS #collaboration #recommendation #twitter #using

Recommending twitter users to follow using content and collaborative filtering approaches (JH, MB, BS), pp. 199–206.

RecSys-2010-PizzatoRCKK #named #online #recommendation

RECON: a reciprocal recommender for online dating (LASP, TR, TC, IK, JK), pp. 207–214.

RecSys-2010-Servan-Schreiber #recommendation

Recommendation analytics: the business view, and the business case (ESS), pp. 215–216.

RecSys-2010-AydayF #online #recommendation

A belief propagation based recommender system for online services (EA, FF), pp. 217–220.

RecSys-2010-LeeB #collaboration #process #recommendation #self #using

Using self-defined group activities for improvingrecommendations in collaborative tagging systems (DHL, PB), pp. 221–224.

RecSys-2010-Burke #algorithm #recommendation

Evaluating the dynamic properties of recommendation algorithms (RDB), pp. 225–228.

RecSys-2010-KaragiannidisAZV #framework #named #recommendation

Hydra: an open framework for virtual-fusion of recommendation filters (SK, SA, CZ, AV), pp. 229–232.

RecSys-2010-GedikliJ #rating #recommendation

Recommending based on rating frequencies (FG, DJ), pp. 233–236.

RecSys-2010-CantadorBV #recommendation #social

Content-based recommendation in social tagging systems (IC, AB, DV), pp. 237–240.

RecSys-2010-WuGC #identification #multi

Merging multiple criteria to identify suspicious reviews (GW, DG, PC), pp. 241–244.

RecSys-2010-ImK #personalisation #recommendation

Personalizing the settings for Cf-based recommender systems (II, BHK), pp. 245–248.

RecSys-2010-ZhengWZLY #case study #empirical #recommendation #user study

Do clicks measure recommendation relevancy?: an empirical user study (HZ, DW, QZ, HL, TY), pp. 249–252.

RecSys-2010-BenchettaraKR #approach #collaboration #machine learning #predict #recommendation

A supervised machine learning link prediction approach for academic collaboration recommendation (NB, RK, CR), pp. 253–256.

RecSys-2010-GeDJ #recommendation

Beyond accuracy: evaluating recommender systems by coverage and serendipity (MG, CDB, DJ), pp. 257–260.

RecSys-2010-ZangerleGS #collaboration #information management #recommendation

Recommending structure in collaborative semistructured information systems (EZ, WG, GS), pp. 261–264.

RecSys-2010-DeryKRS #nondeterminism #recommendation

Iterative voting under uncertainty for group recommender systems (LND, MK, LR, BS), pp. 265–268.

RecSys-2010-ShiLH #collaboration #learning #matrix #rank

List-wise learning to rank with matrix factorization for collaborative filtering (YS, ML, AH), pp. 269–272.

RecSys-2010-KamishimaA #approach #collaboration #modelling

Nantonac collaborative filtering: a model-based approach (TK, SA), pp. 273–276.

RecSys-2010-FreyneBDG #network #recommendation #social

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

RecSys-2010-PessemierDDM #algorithm #collaboration #dependence #quality

Time dependency of data quality for collaborative filtering algorithms (TDP, SD, TD, LM), pp. 281–284.

RecSys-2010-HammerKA #named #recommendation

MED-StyleR: METABO diabetes-lifestyle recommender (SH, JK, EA), pp. 285–288.

RecSys-2010-JancsaryNT #personalisation #semantics #towards

Towards context-aware personalization and a broad perspective on the semantics of news articles (JJ, FN, HT), pp. 289–292.

RecSys-2010-DavidsonLLNVGGHLLS #recommendation #video

The YouTube video recommendation system (JD, BL, JL, PN, TVV, UG, SG, YH, ML, BL, DS), pp. 293–296.

RecSys-2010-MarxHM #algorithm #comprehension #hybrid #recommendation

Increasing consumers’ understanding of recommender results: a preference-based hybrid algorithm with strong explanatory power (PM, THT, AM), pp. 297–300.

RecSys-2010-DalyGM #network #recommendation #social

The network effects of recommending social connections (EMD, WG, DRM), pp. 301–304.

RecSys-2010-EsparzaOS #on the #realtime #recommendation #web

On the real-time web as a source of recommendation knowledge (SGE, MPO, BS), pp. 305–308.

RecSys-2010-Diaz-AvilesGSN #on the fly

LDA for on-the-fly auto tagging (EDA, MG, AS, WN), pp. 309–312.

RecSys-2010-Balakrishnan #on-demand #recommendation

On-demand set-based recommendations (SB), pp. 313–316.

RecSys-2010-JawaheerSK #feedback #music #online #recommendation

Characterisation of explicit feedback in an online music recommendation service (GJ, MS, PK), pp. 317–320.

RecSys-2010-Gutschmidt #approach #online #segmentation

An approach to situational market segmentation on on-line newspapers based on current tasks (AG), pp. 321–324.

RecSys-2010-KhoshneshinS10a #clustering #collaboration #incremental

Incremental collaborative filtering via evolutionary co-clustering (MK, WNS), pp. 325–328.

RecSys-2010-AlbanesedMPP #modelling #problem #recommendation #social

Modeling recommendation as a social choice problem (MA, Ad, VM, FP, AP), pp. 329–332.

RecSys-2010-FarrellRDDD #navigation #social #web

Social navigation for the spoken web (RGF, NR, RD, CMD, KAD), pp. 333–336.

RecSys-2010-MayerMSJ #predict #social

Common attributes in an unusual context: predicting the desirability of a social match (JMM, SM, RPS, QJ), pp. 337–340.

RecSys-2010-MelloAZ #impact analysis #learning #rating

Active learning driven by rating impact analysis (CERdM, MAA, GZ), pp. 341–344.

RecSys-2010-MoldvayBFS #clustering #graph #named #recommendation #semantics #social

Tagmantic: a social recommender service based on semantic tag graphs and tag clusters (JM, IB, AF, MS), pp. 345–346.

RecSys-2010-BarrioR #collaboration #recommendation

Geolocated movie recommendations based on expert collaborative filtering (JBB, XAR), pp. 347–348.

RecSys-2010-CebrianPVA #music #recommendation

Music recommendations with temporal context awareness (TC, MP, PV, XA), pp. 349–352.

RecSys-2010-PizzatoRCKYK #online #recommendation

Reciprocal recommender system for online dating (LASP, TR, TC, IK, KY, JK), pp. 353–354.

RecSys-2010-PronkBKP #video #web

Integrating broadcast and web video content into personal tv channels (VP, MB, JHMK, AP), pp. 355–356.

RecSys-2010-Hu #design #recommendation

Design and user issues in personality-based recommender systems (RH), pp. 357–360.

RecSys-2010-Musto #modelling #recommendation

Enhanced vector space models for content-based recommender systems (CM), pp. 361–364.

RecSys-2010-Said #hybrid #identification #recommendation

Identifying and utilizing contextual data in hybrid recommender systems (AS), pp. 365–368.

RecSys-2010-Schirru #enterprise #platform #recommendation #social #social media #topic

Topic-based recommendations in enterprise social media sharing platforms (RS), pp. 369–372.