Alfred Kobsa, Michelle X. Zhou, Martin Ester, Yehuda Koren
Proceedings of the Eighth Conference on Recommender Systems
RecSys, 2014.

@proceedings{RecSys-2014,
	acmid         = "2645710",
	address       = "Silicon Valley, California, USA",
	editor        = "Alfred Kobsa and Michelle X. Zhou and Martin Ester and Yehuda Koren",
	isbn          = "978-1-4503-2668-1",
	publisher     = "{ACM}",
	title         = "{Proceedings of the Eighth Conference on Recommender Systems}",
	year          = 2014,
}

Contents (79 items)

55 ×#recommendation
9 ×#social
8 ×#named
7 ×#collaboration
6 ×#personalisation
4 ×#algorithm
4 ×#framework
4 ×#matrix
4 ×#modelling
4 ×#network

RecSys-2014-BastianHVSSKUL #scalability #topic

LinkedIn skills: large-scale topic extraction and inference (MB, MH, WV, SS, PS, HJK, SU, CL), pp. 1–8.

RecSys-2014-PeraN14a #automation #recommendation

Automating readers’ advisory to make book recommendations for K-12 readers (MSP, YKN), pp. 9–16.

RecSys-2014-YuanMZS #predict #sentiment

Exploiting sentiment homophily for link prediction (GY, PKM, ZZ, MPS), pp. 17–24.

RecSys-2014-LiuSM #robust

A robust model for paper reviewer assignment (XL, TS, NDM), pp. 25–32.

RecSys-2014-TavakolB #detection #topic

Factored MDPs for detecting topics of user sessions (MT, UB), pp. 33–40.

RecSys-2014-HaririMB #adaptation #interactive #recommendation

Context adaptation in interactive recommender systems (NH, BM, RDB), pp. 41–48.

RecSys-2014-YangAR #constraints #online #recommendation

Question recommendation with constraints for massive open online courses (DY, DA, CPR), pp. 49–56.

RecSys-2014-SeminarioW #recommendation

Attacking item-based recommender systems with power items (CES, DCW), pp. 57–64.

RecSys-2014-BhagatWIT #learning #matrix #recommendation #using

Recommending with an agenda: active learning of private attributes using matrix factorization (SB, UW, SI, NT), pp. 65–72.

RecSys-2014-TangJLL #personalisation #recommendation

Ensemble contextual bandits for personalized recommendation (LT, YJ, LL, TL), pp. 73–80.

RecSys-2014-TrevisiolASJ #graph #recommendation

Cold-start news recommendation with domain-dependent browse graph (MT, LMA, RS, AJ), pp. 81–88.

RecSys-2014-SaveskiM #learning #recommendation

Item cold-start recommendations: learning local collective embeddings (MS, AM), pp. 89–96.

RecSys-2014-LiuGWB #power of #using

Improving the discriminative power of inferred content information using segmented virtual profile (HL, AG, TW, AB), pp. 97–104.

RecSys-2014-LingLK #approach #recommendation

Ratings meet reviews, a combined approach to recommend (GL, MRL, IK), pp. 105–112.

RecSys-2014-YiHZLR #personalisation

Beyond clicks: dwell time for personalization (XY, LH, EZ, NNL, SR), pp. 113–120.

RecSys-2014-KluverK #behaviour #recommendation

Evaluating recommender behavior for new users (DK, JAK), pp. 121–128.

RecSys-2014-SaidB #benchmark #comparative #evaluation #framework #metric #recommendation

Comparative recommender system evaluation: benchmarking recommendation frameworks (AS, AB), pp. 129–136.

RecSys-2014-KrishnanPFG #bias #learning #recommendation #social

A methodology for learning, analyzing, and mitigating social influence bias in recommender systems (SK, JP, MJF, KG), pp. 137–144.

RecSys-2014-VargasC #recommendation

Improving sales diversity by recommending users to items (SV, PC), pp. 145–152.

RecSys-2014-AdamopoulosT14a #bias #collaboration #on the #probability #recommendation

On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systems (PA, AT), pp. 153–160.

RecSys-2014-EkstrandHWK #algorithm #difference #recommendation

User perception of differences in recommender algorithms (MDE, FMH, MCW, JAK), pp. 161–168.

RecSys-2014-GarcinFDABH #evaluation #online #recommendation

Offline and online evaluation of news recommender systems at swissinfo.ch (FG, BF, OD, AA, CB, AH), pp. 169–176.

RecSys-2014-VerstrepenG #collaboration #nearest neighbour

Unifying nearest neighbors collaborative filtering (KV, BG), pp. 177–184.

RecSys-2014-Liu0L #recommendation

Recommending user generated item lists (YL, MX, LVSL), pp. 185–192.

RecSys-2014-PedroK #collaboration #recommendation

Question recommendation for collaborative question answering systems with RankSLDA (JSP, AK), pp. 193–200.

RecSys-2014-KimC #collaboration #predict

Bayesian binomial mixture model for collaborative prediction with non-random missing data (YDK, SC), pp. 201–208.

RecSys-2014-VargasBKC #recommendation

Coverage, redundancy and size-awareness in genre diversity for recommender systems (SV, LB, AK, PC), pp. 209–216.

RecSys-2014-LiuA #framework #recommendation #towards

Towards a dynamic top-N recommendation framework (XL, KA), pp. 217–224.

RecSys-2014-VanchinathanNBK #process #recommendation

Explore-exploit in top-N recommender systems via Gaussian processes (HPV, IN, FDB, AK), pp. 225–232.

RecSys-2014-GueyeAN #algorithm #recommendation

A parameter-free algorithm for an optimized tag recommendation list size (MG, TA, HN), pp. 233–240.

RecSys-2014-PetroniQ #clustering #distributed #graph #matrix #named #probability

GASGD: stochastic gradient descent for distributed asynchronous matrix completion via graph partitioning (FP, LQ), pp. 241–248.

RecSys-2014-BauerN #framework #matrix

A framework for matrix factorization based on general distributions (JB, AN), pp. 249–256.

RecSys-2014-BachrachFGKKNP #recommendation #using

Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces (YB, YF, RGB, LK, NK, NN, UP), pp. 257–264.

RecSys-2014-ChengXZKL

Gradient boosting factorization machines (CC, FX, TZ, IK, MRL), pp. 265–272.

RecSys-2014-PalovicsBKKF #online #recommendation

Exploiting temporal influence in online recommendation (RP, AAB, LK, TK, EF), pp. 273–280.

RecSys-2014-LoniSLH #collaboration

“Free lunch” enhancement for collaborative filtering with factorization machines (BL, AS, ML, AH), pp. 281–284.

RecSys-2014-NoiaORTS #analysis #recommendation #towards

An analysis of users’ propensity toward diversity in recommendations (TDN, VCO, JR, PT, EDS), pp. 285–288.

RecSys-2014-SuiB #feedback #online #rank #recommendation

Clinical online recommendation with subgroup rank feedback (YS, JWB), pp. 289–292.

RecSys-2014-Aiolli #feedback #optimisation #recommendation

Convex AUC optimization for top-N recommendation with implicit feedback (FA), pp. 293–296.

RecSys-2014-CremonesiQ #question #recommendation

Cross-domain recommendations without overlapping data: myth or reality? (PC, MQ), pp. 297–300.

RecSys-2014-ZhengMB #algorithm #named #recommendation

CSLIM: contextual SLIM recommendation algorithms (YZ, BM, RDB), pp. 301–304.

RecSys-2014-HarmanOAG #recommendation #trust

Dynamics of human trust in recommender systems (JLH, JO, TFA, CG), pp. 305–308.

RecSys-2014-NeidhardtSSW #elicitation

Eliciting the users’ unknown preferences (JN, RS, LS, HW), pp. 309–312.

RecSys-2014-WaldnerV #exclamation #game studies #recommendation #timeline #twitter

Emphasize, don’t filter!: displaying recommendations in Twitter timelines (WW, JV), pp. 313–316.

RecSys-2014-FazeliLBDS #matrix #social #trust

Implicit vs. explicit trust in social matrix factorization (SF, BL, AB, HD, PBS), pp. 317–320.

RecSys-2014-RafailidisN #modelling

Modeling the dynamics of user preferences in coupled tensor factorization (DR, AN), pp. 321–324.

RecSys-2014-DalyBKM #multi #recommendation

Multi-criteria journey aware housing recommender system (EMD, AB, AK, RM), pp. 325–328.

RecSys-2014-AdamsSBKHM #collaboration #health #named

PERSPeCT: collaborative filtering for tailored health communications (RJA, RSS, KB, RLK, TKH, BMM), pp. 329–332.

RecSys-2014-DeryKRS #elicitation #recommendation

Preference elicitation for narrowing the recommended list for groups (LND, MK, LR, BS), pp. 333–336.

RecSys-2014-JannachF #data mining #mining #modelling #process #recommendation

Recommendation-based modeling support for data mining processes (DJ, SF), pp. 337–340.

RecSys-2014-ZhangOFL #modelling #network #scalability #social

Scalable audience targeted models for brand advertising on social networks (KZ, AMO, SF, HL), pp. 341–344.

RecSys-2014-SedhainSBXC #collaboration #recommendation #social

Social collaborative filtering for cold-start recommendations (SS, SS, DB, LX, JC), pp. 345–348.

RecSys-2014-BraunhoferCR #hybrid #recommendation

Switching hybrid for cold-starting context-aware recommender systems (MB, VC, FR), pp. 349–352.

RecSys-2014-LercheJ #feedback #personalisation #ranking #using

Using graded implicit feedback for bayesian personalized ranking (LL, DJ), pp. 353–356.

RecSys-2014-BhattacharyaZGGG #network #social #twitter

Inferring user interests in the Twitter social network (PB, MBZ, NG, SG, KPG), pp. 357–360.

RecSys-2014-SureshRE #mining #recommendation

Aspect-based opinion mining and recommendationsystem for restaurant reviews (VS, SR, ME), pp. 361–362.

RecSys-2014-Ben-ShimonTFH #as a service #configuration management #monitoring #recommendation

Configuring and monitoring recommender system as a service (DBS, AT, MF, JH), pp. 363–364.

RecSys-2014-GingerichA

Content ordering based on commuting patterns (TG, OA), pp. 365–366.

RecSys-2014-KellerR #e-commerce #framework #named #platform #recommendation

Cosibon: an E-commerce like platform enabling bricks-and-mortar stores to use sophisticated product recommender systems (TK, MR), pp. 367–368.

RecSys-2014-GarcinGF #mobile #named #personalisation

Focal: a personalized mobile news reader (FG, FG, BF), pp. 369–370.

RecSys-2014-SaidB14a #evaluation #named #recommendation #tool support

Rival: a toolkit to foster reproducibility in recommender system evaluation (AS, AB), pp. 371–372.

RecSys-2014-BadenesBCGHMNPSSXYZ #automation #people #recommendation #social #social media

System U: automatically deriving personality traits from social media for people recommendation (HB, MNB, JC, LG, EMH, JM, JWN, AP, JS, BAS, YX, HY, MXZ), pp. 373–374.

RecSys-2014-LiuWW #what

Tell me where to go and what to do next, but do not bother me (HL, GW, GW), pp. 375–376.

RecSys-2014-LoniS #library #named #recommendation

WrapRec: an easy extension of recommender system libraries (BL, AS), pp. 377–378.

RecSys-2014-SaidDLT #challenge #recommendation

Recommender systems challenge 2014 (AS, SD, BL, DT), pp. 387–388.

RecSys-2014-XuPA #predict #ranking #recommendation

Controlled experimentation in recommendations, ranking & response prediction (YX, RP, JA), p. 389.

RecSys-2014-Amatriain #problem #recommendation #revisited

The recommender problem revisited (XA), pp. 397–398.

RecSys-2014-GaoTL #network #personalisation #recommendation #social

Personalized location recommendation on location-based social networks (HG, JT, HL), pp. 399–400.

RecSys-2014-CantadorC #recommendation #tutorial

Tutorial on cross-domain recommender systems (IC, PC), pp. 401–402.

RecSys-2014-GuyG #recommendation #social #tutorial

Social recommender system tutorial (IG, WG), pp. 403–404.

RecSys-2014-Braunhofer #recommendation

Hybridisation techniques for cold-starting context-aware recommender systems (MB), pp. 405–408.

RecSys-2014-Christakopoulou #independence #recommendation

Moving beyond linearity and independence in top-N recommender systems (EC), pp. 409–412.

RecSys-2014-Mayeku #personalisation #recommendation

Enhancing personalization and learner engagement through context-aware recommendation in TEL (BM), pp. 413–415.

RecSys-2014-Nguyen #lifecycle #recommendation

Improving recommender systems: user roles and lifecycles (TTN), pp. 417–420.

RecSys-2014-Sharma #modelling #people #social

Modeling the effect of people’s preferences and social forces on adopting and sharing items (AS), pp. 421–424.

RecSys-2014-Stettinger #independence #named #towards

Choicla: towards domain-independent decision support for groups of users (MS), pp. 425–428.

RecSys-2014-Vahedian #hybrid #network #recommendation

Weighted hybrid recommendation for heterogeneous networks (FV), pp. 429–432.

RecSys-2014-Zhang #recommendation

Browser-oriented universal cross-site recommendation and explanation based on user browsing logs (YZ), pp. 433–436.

RecSys-2014-Zheng #algorithm #recommendation #similarity

Deviation-based and similarity-based contextual SLIM recommendation algorithms (YZ), pp. 437–440.