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

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@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)

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.

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