Lawrence D. Bergman, Alexander Tuzhilin, Robin D. Burke, Alexander Felfernig, Lars Schmidt-Thieme
Proceedings of the Third Conference on Recommender Systems
RecSys, 2009.

@proceedings{RecSys-2009,
	address       = "New York, New York, USA",
	editor        = "Lawrence D. Bergman and Alexander Tuzhilin and Robin D. Burke and Alexander Felfernig and Lars Schmidt-Thieme",
	isbn          = "978-1-60558-435-5",
	publisher     = "{ACM}",
	title         = "{Proceedings of the Third Conference on Recommender Systems}",
	year          = 2009,
}

Contents (81 items)

60 ×#recommendation
13 ×#collaboration
7 ×#personalisation
6 ×#named
6 ×#trust
6 ×#using
5 ×#social
3 ×#approach
3 ×#network
3 ×#predict

RecSys-2009-WedelRC #personalisation #recommendation

Up close and personalized: a marketing view of recommendation systems (MW, RTR, TSC), pp. 3–4.

RecSys-2009-MarlinZ #collaboration #predict #ranking

Collaborative prediction and ranking with non-random missing data (BMM, RSZ), pp. 5–12.

RecSys-2009-LuAD #approach #collaboration

A spatio-temporal approach to collaborative filtering (ZL, DA, ISD), pp. 13–20.

RecSys-2009-ParkC #recommendation

Pairwise preference regression for cold-start recommendation (STP, WC), pp. 21–28.

RecSys-2009-TylerZCZ #categorisation #recommendation

Ordering innovators and laggards for product categorization and recommendation (SKT, SZ, YC, YZ), pp. 29–36.

RecSys-2009-UmyarovT #estimation #modelling #rating #recommendation #using

Improving rating estimation in recommender systems using aggregation- and variance-based hierarchical models (AU, AT), pp. 37–44.

RecSys-2009-GemmellRSCM #ambiguity #folksonomy #recommendation

The impact of ambiguity and redundancy on tag recommendation in folksonomies (JG, MR, TS, LC, BM), pp. 45–52.

RecSys-2009-GuyZCRUYO #personalisation #recommendation #social

Personalized recommendation of social software items based on social relations (IG, NZ, DC, IR, EU, SY, SOK), pp. 53–60.

RecSys-2009-KrestelFN #recommendation

Latent dirichlet allocation for tag recommendation (RK, PF, WN), pp. 61–68.

RecSys-2009-ZhenLY #collaboration #named

TagiCoFi: tag informed collaborative filtering (YZ, WJL, DYY), pp. 69–76.

RecSys-2009-BaragliaCCFFPS #approach #query #recommendation

Search shortcuts: a new approach to the recommendation of queries (RB, FC, VC, DF, VF, RP, FS), pp. 77–84.

RecSys-2009-FreyneJGG #recommendation

Increasing engagement through early recommender intervention (JF, MJ, IG, WG), pp. 85–92.

RecSys-2009-PilaszyT #metadata #recommendation

Recommending new movies: even a few ratings are more valuable than metadata (IP, DT), pp. 93–100.

RecSys-2009-ViappianiB #recommendation #set

Regret-based optimal recommendation sets in conversational recommender systems (PV, CB), pp. 101–108.

RecSys-2009-BaoBT #recommendation

Stacking recommendation engines with additional meta-features (XB, LB, RT), pp. 109–116.

RecSys-2009-GunawardanaM #approach #hybrid #recommendation

A unified approach to building hybrid recommender systems (AG, CM), pp. 117–124.

RecSys-2009-ShiLH #collaboration #similarity

Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering (YS, ML, AH), pp. 125–132.

RecSys-2009-ZhouR #assessment #recommendation

Assessment of conversation co-mentions as a resource for software module recommendation (DXZ, PR), pp. 133–140.

RecSys-2009-ChengH #effectiveness #modelling #obfuscation #recommendation

Effective diverse and obfuscated attacks on model-based recommender systems (ZC, NH), pp. 141–148.

RecSys-2009-HurleyCZ #detection #statistics

Statistical attack detection (NH, ZC, MZ), pp. 149–156.

RecSys-2009-ShokriPTH #collaboration #distributed #privacy

Preserving privacy in collaborative filtering through distributed aggregation of offline profiles (RS, PP, GT, JPH), pp. 157–164.

RecSys-2009-RoyY #collaboration

Manipulation-resistant collaborative filtering systems (BVR, XY), pp. 165–172.

RecSys-2009-AmatriainPTO #recommendation

Rate it again: increasing recommendation accuracy by user re-rating (XA, JMP, NT, NO), pp. 173–180.

RecSys-2009-JamaliE #network #recommendation #trust #using

Using a trust network to improve top-N recommendation (MJ, ME), pp. 181–188.

RecSys-2009-MaLK #learning #recommendation #trust

Learning to recommend with trust and distrust relationships (HM, MRL, IK), pp. 189–196.

RecSys-2009-WalterBS #network #personalisation #social #trust

Personalised and dynamic trust in social networks (FEW, SB, FS), pp. 197–204.

RecSys-2009-JannachH #case study #effectiveness #internet #mobile #recommendation

A case study on the effectiveness of recommendations in the mobile internet (DJ, KH), pp. 205–208.

RecSys-2009-QasimOWHO #partial order #recommendation

A partial-order based active cache for recommender systems (UQ, VO, YfBW, MEH, MTÖ), pp. 209–212.

RecSys-2009-Castro-HerreraCM #evolution #online #recommendation

A recommender system for dynamically evolving online forums (CCH, JCH, BM), pp. 213–216.

RecSys-2009-TsatsouMKD #analysis #framework #personalisation #recommendation #semantics

A semantic framework for personalized ad recommendation based on advanced textual analysis (DT, FM, IK, PCD), pp. 217–220.

RecSys-2009-HuP #recommendation

Acceptance issues of personality-based recommender systems (RH, PP), pp. 221–224.

RecSys-2009-Chen #adaptation #recommendation #trade-off

Adaptive tradeoff explanations in conversational recommenders (LC), pp. 225–228.

RecSys-2009-BhattacharjeeGK #architecture #recommendation #social

An incentive-based architecture for social recommendations (RB, AG, KK), pp. 229–232.

RecSys-2009-CremonesiT #analysis #recommendation

Analysis of cold-start recommendations in IPTV systems (PC, RT), pp. 233–236.

RecSys-2009-ParraB #collaboration #empirical #social

Collaborative filtering for social tagging systems: an experiment with CiteULike (DP, PB), pp. 237–240.

RecSys-2009-PudhiyaveetilGLE #concept #recommendation

Conceptual recommender system for CiteSeerX (AKP, SG, HPL, JE), pp. 241–244.

RecSys-2009-BaltrunasR #collaboration

Context-based splitting of item ratings in collaborative filtering (LB, FR), pp. 245–248.

RecSys-2009-PuZC #recommendation

Critiquing recommenders for public taste products (PP, MZ, SC), pp. 249–252.

RecSys-2009-NathansonBG #recommendation

Donation dashboard: a recommender system for donation portfolios (TN, EB, KYG), pp. 253–256.

RecSys-2009-AntonelliFGL #named #recommendation

DynamicTV: a culture-aware recommender (FA, GF, MG, SL), pp. 257–260.

RecSys-2009-SchclarTRMA #collaboration #performance

Ensemble methods for improving the performance of neighborhood-based collaborative filtering (AS, AT, LR, AM, LA), pp. 261–264.

RecSys-2009-PannielloTGPP #comparison #recommendation

Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems (UP, AT, MG, CP, AP), pp. 265–268.

RecSys-2009-MoghaddamJEH #feedback #named #recommendation #trust #using

FeedbackTrust: using feedback effects in trust-based recommendation systems (SM, MJ, ME, JH), pp. 269–272.

RecSys-2009-QuerciaC #mobile #named #recommendation #using

FriendSensing: recommending friends using mobile phones (DQ, LC), pp. 273–276.

RecSys-2009-CarolisNPG #comparative #generative

Generating comparative descriptions of places of interest in the tourism domain (BDC, NN, VLP, EG), pp. 277–280.

RecSys-2009-GreenLAMKHBM #generative #recommendation

Generating transparent, steerable recommendations from textual descriptions of items (SJG, PL, JA, FM, SK, JH, JB, XWM), pp. 281–284.

RecSys-2009-AbbassiALVY #recommendation

Getting recommender systems to think outside the box (ZA, SAY, LVSL, SV, CY), pp. 285–288.

RecSys-2009-KhezrzadehTW #power of #recommendation

Harnessing the power of “favorites” lists for recommendation systems (MK, AT, WWW), pp. 289–292.

RecSys-2009-NanopoulosRI #collaboration #how #question

How does high dimensionality affect collaborative filtering? (AN, MR, MI), pp. 293–296.

RecSys-2009-HartJS #named #personalisation

iTag: a personalized blog tagger (MH, RJ, AS), pp. 297–300.

RecSys-2009-SemeraroLBG #recommendation

Knowledge infusion into content-based recommender systems (GS, PL, PB, MdG), pp. 301–304.

RecSys-2009-OMahonyS #learning #recommendation

Learning to recommend helpful hotel reviews (MPO, BS), pp. 305–308.

RecSys-2009-WeimerKB #matrix #recommendation

Maximum margin matrix factorization for code recommendation (MW, AK, MB), pp. 309–312.

RecSys-2009-CamposFHR #collaboration #predict

Measuring predictive capability in collaborative filtering (LMdC, JMFL, JFH, MARM), pp. 313–316.

RecSys-2009-SymeonidisNM #named #recommendation

MoviExplain: a recommender system with explanations (PS, AN, YM), pp. 317–320.

RecSys-2009-SeyerlehnerFW #on the #recommendation

On the limitations of browsing top-N recommender systems (KS, AF, GW), pp. 321–324.

RecSys-2009-Recio-GarciaJSD #recommendation

Personality aware recommendations to groups (JARG, GJD, AASRG, BDA), pp. 325–328.

RecSys-2009-KawamaeSY #personalisation #recommendation

Personalized recommendation based on the personal innovator degree (NK, HS, TY), pp. 329–332.

RecSys-2009-GivonL #predict #recommendation

Predicting social-tags for cold start book recommendations (SG, VL), pp. 333–336.

RecSys-2009-BaskinK #recommendation

Preference aggregation in group recommender systems for committee decision-making (JPB, SK), pp. 337–340.

RecSys-2009-BoutilierRV #elicitation

Preference elicitation with subjective features (CB, KR, PV), pp. 341–344.

RecSys-2009-GansnerHKV #clustering #recommendation #visualisation

Putting recommendations on the map: visualizing clusters and relations (ERG, YH, SGK, CV), pp. 345–348.

RecSys-2009-GarcinFJJ #collaboration #rating

Rating aggregation in collaborative filtering systems (FG, BF, RJ, NJ), pp. 349–352.

RecSys-2009-ParameswaranG #recommendation

Recommendations with prerequisites (AGP, HGM), pp. 353–356.

RecSys-2009-ConryKR #problem #recommendation

Recommender systems for the conference paper assignment problem (DC, YK, NR), pp. 357–360.

RecSys-2009-CastagnosJP #process #recommendation

Recommenders’ influence on buyers’ decision process (SC, NJ, PP), pp. 361–364.

RecSys-2009-BroccoG #network #recommendation

Team recommendation in open innovation networks (MB, GG), pp. 365–368.

RecSys-2009-JaschkeEHS #recommendation #testing

Testing and evaluating tag recommenders in a live system (RJ, FE, AH, GS), pp. 369–372.

RecSys-2009-HelouSSG #process #ranking #recommendation

The 3A contextual ranking system: simultaneously recommending actors, assets, and group activities (SEH, CS, SS, DG), pp. 373–376.

RecSys-2009-HadzicO #dependence #functional

Uncovering functional dependencies in MDD-compiled product catalogues (TH, BO), pp. 377–380.

RecSys-2009-KnijnenburgW #adaptation #comprehension #elicitation #recommendation #user satisfaction

Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system (BPK, MCW), pp. 381–384.

RecSys-2009-PhelanMS #realtime #recommendation #topic #twitter #using

Using twitter to recommend real-time topical news (OP, KM, BS), pp. 385–388.

RecSys-2009-LousameS #recommendation

View-based recommender systems (FPL, ES), pp. 389–392.

RecSys-2009-Bitton #collaboration #online

A spatial model for collaborative filtering of comments in an online discussion forum (EB), pp. 393–396.

RecSys-2009-Zhang #recommendation

Enhancing diversity in Top-N recommendation (MZ), pp. 397–400.

RecSys-2009-Nnadi #clustering #correlation #multi #recommendation #set

Applying relevant set correlation clustering to multi-criteria recommender systems (NN), pp. 401–404.

RecSys-2009-Kaminskas #music

Matching information content with music (MK), pp. 405–408.

RecSys-2009-Schubert #knowledge-based #personalisation #query #recommendation

Personalized query relaxations and repairs in knowledge-based recommendation (MS), pp. 409–412.

RecSys-2009-Tavakolifard #trust

Situation-aware trust management (MT), pp. 413–416.

RecSys-2009-Tolomei #mining #process #recommendation #web

Search the web x.0: mining and recommending web-mediated processes (GT), pp. 417–420.

RecSys-2009-Golbeck #recommendation #social #trust #tutorial #using

Tutorial on using social trust for recommender systems (JG), pp. 425–426.