Hannes Werthner, Markus Zanker, Jennifer Golbeck, Giovanni Semeraro
Proceedings of the Ninth Conference on Recommender Systems
RecSys, 2015.

@proceedings{RecSys-2015,
	acmid         = "2792838",
	address       = "Vienna, Austria",
	editor        = "Hannes Werthner and Markus Zanker and Jennifer Golbeck and Giovanni Semeraro",
	isbn          = "978-1-4503-3692-5",
	publisher     = "{ACM}",
	title         = "{Proceedings of the Ninth Conference on Recommender Systems}",
	year          = 2015,
}

Contents (80 items)

59 ×#recommendation
7 ×#personalisation
7 ×#social
6 ×#modelling
5 ×#automation
5 ×#matrix
5 ×#named
5 ×#scalability
4 ×#algorithm
4 ×#case study

RecSys-2015-Stock #automation #persuasion #speech

A (Persuasive?) Speech on Automated Persuasion (OS), pp. 1–2.

RecSys-2015-HarperXKCCT #recommendation

Putting Users in Control of their Recommendations (FMH, FX, HK, KC, SC, LGT), pp. 3–10.

RecSys-2015-EkstrandKHK #algorithm #case study #recommendation

Letting Users Choose Recommender Algorithms: An Experimental Study (MDE, DK, FMH, JAK), pp. 11–18.

RecSys-2015-KapoorKTKS #adaptation #quote

“I like to explore sometimes”: Adapting to Dynamic User Novelty Preferences (KK, VK, LGT, JAK, PRS), pp. 19–26.

RecSys-2015-LiWTM #community #predict #rating #recommendation #social

Overlapping Community Regularization for Rating Prediction in Social Recommender Systems (HL, DW, WT, NM), pp. 27–34.

RecSys-2015-Salehi-AbariB #network #recommendation #social

Preference-oriented Social Networks: Group Recommendation and Inference (ASA, CB), pp. 35–42.

RecSys-2015-ChaneyBE #network #personalisation #probability #recommendation #social #using

A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (AJBC, DMB, TER), pp. 43–50.

RecSys-2015-ForsatiBMER #algorithm #named #performance #recommendation #trust

PushTrust: An Efficient Recommendation Algorithm by Leveraging Trust and Distrust Relations (RF, IB, FM, AHE, HR), pp. 51–58.

RecSys-2015-VerstrepenG #recommendation

Top-N Recommendation for Shared Accounts (KV, BG), pp. 59–66.

RecSys-2015-LuC #personalisation #recommendation

Exploiting Geo-Spatial Preference for Personalized Expert Recommendation (HL, JC), pp. 67–74.

RecSys-2015-ZhaoZ0 #recommendation

Risk-Hedged Venture Capital Investment Recommendation (XZ, WZ, JW), pp. 75–82.

RecSys-2015-AharonAADGS #named #recommendation

ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations (MA, OA, NAE, DDC, SG, OS), pp. 83–90.

RecSys-2015-BarjastehFMER #recommendation

Cold-Start Item and User Recommendation with Decoupled Completion and Transduction (IB, RF, FM, AHE, HR), pp. 91–98.

RecSys-2015-KoukiFFEG #flexibility #framework #hybrid #named #probability #recommendation

HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems (PK, SF, JRF, ME, LG), pp. 99–106.

RecSys-2015-BerliozFKBB #difference #matrix #privacy

Applying Differential Privacy to Matrix Factorization (AB, AF, MAK, RB, SB), pp. 107–114.

RecSys-2015-Steck #matrix #ranking

Gaussian Ranking by Matrix Factorization (HS), pp. 115–122.

RecSys-2015-MacedoMS #network #recommendation #social

Context-Aware Event Recommendation in Event-based Social Networks (AQdM, LBM, RLTS), pp. 123–130.

RecSys-2015-SahebiB #collaboration

It Takes Two to Tango: An Exploration of Domain Pairs for Cross-Domain Collaborative Filtering (SS, PB), pp. 131–138.

RecSys-2015-BistaffaFCR #recommendation #scalability #social

Recommending Fair Payments for Large-Scale Social Ridesharing (FB, AF, GC, SDR), pp. 139–146.

RecSys-2015-AlmahairiKCC #collaboration #distributed #learning

Learning Distributed Representations from Reviews for Collaborative Filtering (AA, KK, KC, ACC), pp. 147–154.

RecSys-2015-CharlinRMB

Dynamic Poisson Factorization (LC, RR, JM, DMB), pp. 155–162.

RecSys-2015-ChristoffelPNB #random #recommendation #scalability

Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks (FC, BP, CN, AB), pp. 163–170.

RecSys-2015-LiuWS #matrix #performance

Fast Differentially Private Matrix Factorization (ZL, YXW, AJS), pp. 171–178.

RecSys-2015-MaksaiGF #evaluation #metric #online #performance #predict #recommendation

Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics (AM, FG, BF), pp. 179–186.

RecSys-2015-JannachLK #continuation #generative #music

Beyond “Hitting the Hits”: Generating Coherent Music Playlist Continuations with the Right Tracks (DJ, LL, IK), pp. 187–194.

RecSys-2015-BansalDB #profiling #recommendation

Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles (TB, MKD, CB), pp. 195–202.

RecSys-2015-KarSA #linear #online #video

Selection and Ordering of Linear Online Video Ads (WK, VS, PA), pp. 203–210.

RecSys-2015-JannachLJ #adaptation #evaluation #recommendation

Adaptation and Evaluation of Recommendations for Short-term Shopping Goals (DJ, LL, MJ), pp. 211–218.

RecSys-2015-ZhaoZFT #e-commerce #personalisation #recommendation

E-commerce Recommendation with Personalized Promotion (QZ, YZ, DF, FT), pp. 219–226.

RecSys-2015-AuteriT #linear #personalisation

Personalized Catch-up & DVR: VOD or Linear, That is the Question (PA, RT), p. 227.

RecSys-2015-NeumannS #recommendation

Recommendations for Live TV (JN, HS), p. 228.

RecSys-2015-Bourke #multi #recommendation

The Application of Recommender Systems in a Multi Site, Multi Domain Environment (SB), p. 229.

RecSys-2015-Abel #recommendation

We Know Where You Should Work Next Summer: Job Recommendations (FA), p. 230.

RecSys-2015-MojsilovicV #enterprise #perspective #recommendation

Assessing Expertise in the Enterprise: The Recommender Point of View (AM, KRV), p. 231.

RecSys-2015-LerallutGR #realtime #recommendation #scalability

Large-Scale Real-Time Product Recommendation at Criteo (RL, DG, NLR), p. 232.

RecSys-2015-Nemeth #recommendation #scalability

Scaling Up Recommendation Services in Many Dimensions (BN), p. 233.

RecSys-2015-Zoeter #recommendation

Recommendations in Travel (OZ), p. 234.

RecSys-2015-Das #recommendation

Making Meaningful Restaurant Recommendations At OpenTable (SD), p. 235.

RecSys-2015-Guy #personalisation #recommendation

The Role of User Location in Personalized Search and Recommendation (IG), p. 236.

RecSys-2015-ValcarcePB #case study #modelling #recommendation

A Study of Priors for Relevance-Based Language Modelling of Recommender Systems (DV, JP, AB), pp. 237–240.

RecSys-2015-AghdamHMB #adaptation #markov #modelling #recommendation #using

Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models (MHA, NH, BM, RDB), pp. 241–244.

RecSys-2015-MarinhoTP #algorithm #question #recommendation

Are Real-World Place Recommender Algorithms Useful in Virtual World Environments? (LBM, CT, DP), pp. 245–248.

RecSys-2015-NovA #recommendation #social #symmetry

Asymmetric Recommendations: The Interacting Effects of Social Ratings? Direction and Strength on Users’ Ratings (ON, OA), pp. 249–252.

RecSys-2015-DalyBS #recommendation

Crowd Sourcing, with a Few Answers: Recommending Commuters for Traffic Updates (ED, MB, FS), pp. 253–256.

RecSys-2015-ShalomBRZA #matter #quality #recommendation

Data Quality Matters in Recommender Systems (OSS, SB, RR, EZ, AA), pp. 257–260.

RecSys-2015-KangDS #recommendation

Elsevier Journal Finder: Recommending Journals for your Paper (NK, MAD, RJAS), pp. 261–264.

RecSys-2015-KowaldL #algorithm #case study #comparative #folksonomy #recommendation

Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study (DK, EL), pp. 265–268.

RecSys-2015-LarrainTPGN #case study #collaboration #social

Good Times Bad Times: A Study on Recency Effects in Collaborative Filtering for Social Tagging (SL, CT, DP, EGG, KN), pp. 269–272.

RecSys-2015-GrausW #elicitation #experience #user interface

Improving the User Experience during Cold Start through Choice-Based Preference Elicitation (MPG, MCW), pp. 273–276.

RecSys-2015-SongCL #incremental #matrix #recommendation

Incremental Matrix Factorization via Feature Space Re-learning for Recommender System (QS, JC, HL), pp. 277–280.

RecSys-2015-Guardia-Sebaoun #modelling #performance #recommendation

Latent Trajectory Modeling: A Light and Efficient Way to Introduce Time in Recommender Systems (ÉGS, VG, PG), pp. 281–284.

RecSys-2015-MouliC #dependence #feedback #modelling

Making the Most of Preference Feedback by Modeling Feature Dependencies (SCM, SC), pp. 285–288.

RecSys-2015-WaymanM

Nudging Grocery Shoppers to Make Healthier Choices (EW, SM), pp. 289–292.

RecSys-2015-SeminarioW #collaboration #recommendation

Nuke ’Em Till They Go: Investigating Power User Attacks to Disparage Items in Collaborative Recommenders (CES, DCW), pp. 293–296.

RecSys-2015-BetzalelSR #exclamation #quote #recommendation

“Please, Not Now!”: A Model for Timing Recommendations (NDB, BS, LR), pp. 297–300.

RecSys-2015-GriesnerAN #matrix #recommendation #towards

POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences (JBG, TA, HN), pp. 301–304.

RecSys-2015-BanksRS #game studies #recommendation #using

The Recommendation Game: Using a Game-with-a-Purpose to Generate Recommendation Data (SB, RR, BS), pp. 305–308.

RecSys-2015-LimML #feedback #recommendation

Top-N Recommendation with Missing Implicit Feedback (DL, JM, GRGL), pp. 309–312.

RecSys-2015-ElsweilerH #automation #recommendation #towards

Towards Automatic Meal Plan Recommendations for Balanced Nutrition (DE, MH), pp. 313–316.

RecSys-2015-GuoD #approach #bias #category theory

Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach (FG, DBD), pp. 317–320.

RecSys-2015-SifaOB #analysis #migration

User Churn Migration Analysis with DEDICOM (RS, CO, CB), pp. 321–324.

RecSys-2015-KazaiCYV #personalisation

A Personalised Reader for Crowd Curated Content (GK, DC, IY, MV), pp. 325–326.

RecSys-2015-HarveyE #automation #personalisation #recommendation

Automated Recommendation of Healthy, Personalised Meal Plans (MH, DE), pp. 327–328.

RecSys-2015-SousaDBM #analysis #named #network #recommendation

CNARe: Co-authorship Networks Analysis and Recommendations (GAdS, MAD, MAB, MMM), pp. 329–330.

RecSys-2015-MagnusonDM #process #recommendation #twitter #using

Event Recommendation using Twitter Activity (AM, VD, DM), pp. 331–332.

RecSys-2015-GeRM #recommendation

Health-aware Food Recommender System (MG, FR, DM), pp. 333–334.

RecSys-2015-LiuK #named #recommendation

Kibitz: End-to-End Recommendation System Builder (QL, DRK), pp. 335–336.

RecSys-2015-KaragiannakisGS #automation #category theory #recommendation

OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap (NK, GG, DS, SA), pp. 337–338.

RecSys-2015-Ben-ShimonTFSRH #challenge #dataset

RecSys Challenge 2015 and the YOOCHOOSE Dataset (DBS, AT, MF, BS, LR, JH), pp. 357–358.

RecSys-2015-SteckZJ #interactive #recommendation #tutorial

Interactive Recommender Systems: Tutorial (HS, RvZ, CJ), pp. 359–360.

RecSys-2015-HopfgartnerKHT #realtime #recommendation

Real-time Recommendation of Streamed Data (FH, BK, TH, RT), pp. 361–362.

RecSys-2015-SaidB #evaluation #recommendation

Replicable Evaluation of Recommender Systems (AS, AB), pp. 363–364.

RecSys-2015-HuD #machine learning #recommendation #scalability

Scalable Recommender Systems: Where Machine Learning Meets Search (SYDH, JD), pp. 365–366.

RecSys-2015-Santos #hybrid #recommendation

A Hybrid Recommendation System Based on Human Curiosity (AMdS), pp. 367–370.

RecSys-2015-Hidasi #modelling

Context-aware Preference Modeling with Factorization (BH), pp. 371–374.

RecSys-2015-Valcarce #modelling #recommendation #statistics

Exploring Statistical Language Models for Recommender Systems (DV), pp. 375–378.

RecSys-2015-Geuens #behaviour #hybrid #recommendation

Factorization Machines for Hybrid Recommendation Systems Based on Behavioral, Product, and Customer Data (SG), pp. 379–382.

RecSys-2015-Unger #recommendation

Latent Context-Aware Recommender Systems (MU), pp. 383–386.

RecSys-2015-Vall #automation #generative #music

Listener-Inspired Automated Music Playlist Generation (AV), pp. 387–390.

RecSys-2015-Ludmann #data type #online #recommendation

Online Recommender Systems based on Data Stream Management Systems (CAL), pp. 391–394.