Abstract is missing.
- Context-aware review helpfulness rating predictionJiliang Tang, Huiji Gao, Xia Hu, Huan Liu. 1-8 [doi]
- Query-driven context aware recommendationNegar Hariri, Bamshad Mobasher, Robin D. Burke. 9-16 [doi]
- Location-aware music recommendation using auto-tagging and hybrid matchingMarius Kaminskas, Francesco Ricci, Markus Schedl. 17-24 [doi]
- Spatial topic modeling in online social media for location recommendationBo Hu, Martin Ester. 25-32 [doi]
- Orthogonal query recommendationHossein Vahabi, Margareta Ackerman, David Loker, Ricardo A. Baeza-Yates, Alejandro López-Ortiz. 33-40 [doi]
- Understanding and improving relational matrix factorization in recommender systemsLi Pu, Boi Faltings. 41-48 [doi]
- Retargeted matrix factorization for collaborative filteringOluwasanmi Koyejo, Sreangsu Acharyya, Joydeep Ghosh. 49-56 [doi]
- Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approachLei Shi. 57-64 [doi]
- Nonlinear latent factorization by embedding multiple user interestsJason Weston, Ron J. Weiss, Hector Yee. 65-68 [doi]
- Diffusion-aware personalized social update recommendationYe Pan, Feng Cong, Kailong Chen, Yong Yu. 69-76 [doi]
- Recommending branded products from social mediaYongzheng Zhang, Marco Pennacchiotti. 77-84 [doi]
- Top-N recommendations from implicit feedback leveraging linked open dataVito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio, Roberto Mirizzi. 85-92 [doi]
- Exploring temporal effects for location recommendation on location-based social networksHuiji Gao, Jiliang Tang, Xia Hu, Huan Liu. 93-100 [doi]
- The curated web: a recommendation challengeZurina Saaya, Rachael Rafter, Markus Schaal, Barry Smyth. 101-104 [doi]
- Personalized news recommendation with context treesFlorent Garcin, Christos Dimitrakakis, Boi Faltings. 105-112 [doi]
- What to read next?: making personalized book recommendations for K-12 usersMaria Soledad Pera, Yiu-Kai Ng. 113-120 [doi]
- Movie recommender system for profit maximizationAmos Azaria, Avinatan Hassidim, Sarit Kraus, Adi Eshkol, Ofer Weintraub, Irit Netanely. 121-128 [doi]
- Xbox movies recommendations: variational bayes matrix factorization with embedded feature selectionNoam Koenigstein, Ulrich Paquet. 129-136 [doi]
- Personalized next-song recommendation in online karaokesXiang Wu, Qi Liu, Enhong Chen, Liang He, Jingsong Lv, Can Cao, Guoping Hu. 137-140 [doi]
- Topic diversity in tag recommendationFabiano Belém, Rodrygo L. T. Santos, Jussara M. Almeida, Marcos André Gonçalves. 141-148 [doi]
- Rating support interfaces to improve user experience and recommender accuracyTien T. Nguyen, Daniel Kluver, Ting-Yu Wang, Pik-Mai Hui, Michael D. Ekstrand, Martijn C. Willemsen, John Riedl. 149-156 [doi]
- ReComment: towards critiquing-based recommendation with speech interactionPeter Grasch, Alexander Felfernig, Florian Reinfrank. 157-164 [doi]
- Hidden factors and hidden topics: understanding rating dimensions with review textJulian J. McAuley, Jure Leskovec. 165-172 [doi]
- Improving augmented reality using recommender systemsZhuo Zhang, Shang Shang, Sanjeev R. Kulkarni, Pan Hui. 173-176 [doi]
- Exploiting non-content preference attributes through hybrid recommendation methodFernando Mourão, Leonardo C. da Rocha, Joseph A. Konstan, Wagner Meira Jr.. 177-184 [doi]
- Hybrid event recommendation using linked data and user diversityHouda Khrouf, Raphaël Troncy. 185-192 [doi]
- Pairwise learning in recommendation: experiments with community recommendation on linkedinAmit Sharma, Baoshi Yan. 193-200 [doi]
- Which app will you use next?: collaborative filtering with interactional contextNagarajan Natarajan, Donghyuk Shin, Inderjit S. Dhillon. 201-208 [doi]
- A food recommender for patients in a care facilityToon De Pessemier, Simon Dooms, Luc Martens. 209-212 [doi]
- Evaluation of recommendations: rating-prediction and rankingHarald Steck. 213-220 [doi]
- You are what you consume: a bayesian method for personalized recommendationsKonstantinos Babas, Georgios Chalkiadakis, Evangelos Tripolitakis. 221-228 [doi]
- To personalize or not: a risk management perspectiveWeinan Zhang, Jun Wang, Bowei Chen, Xiaoxue Zhao. 229-236 [doi]
- Online multi-task collaborative filtering for on-the-fly recommender systemsJialei Wang, Steven C. H. Hoi, Peilin Zhao, Zhi-Yong Liu. 237-244 [doi]
- Learning to rank recommendations with the k-order statistic lossJason Weston, Hector Yee, Ron J. Weiss. 245-248 [doi]
- A fast parallel SGD for matrix factorization in shared memory systemsYong Zhuang, Wei-Sheng Chin, Yu-Chin Juan, Chih-Jen Lin. 249-256 [doi]
- DrunkardMob: billions of random walks on just a PCAapo Kyrola. 257-264 [doi]
- Using maximum coverage to optimize recommendation systems in e-commerceMikael Hammar, Robin Karlsson, Bengt J. Nilsson. 265-272 [doi]
- Efficient top-n recommendation for very large scale binary rated datasetsFabio Aiolli. 273-280 [doi]
- Distributed matrix factorization with mapreduce using a series of broadcast-joinsSebastian Schelter, Christoph Boden, Martin Schenck, Alexander Alexandrov, Volker Markl. 281-284 [doi]
- Catch-up TV recommendations: show old favourites and find new onesMengxi Xu, Shlomo Berkovsky, Sebastien Ardon, Sipat Triukose, Anirban Mahanti, Irena Koprinska. 285-294 [doi]
- Generating supplemental content information using virtual profilesHaishan Liu, Mohammad Shafkat Amin, Baoshi Yan, Anmol Bhasin. 295-302 [doi]
- A people-to-people content-based reciprocal recommender using hidden markov modelsAmmar Alanazi, Michael Bain. 303-306 [doi]
- Acquiring user profiles from implicit feedback in a conversational recommender systemHenry Blanco, Francesco Ricci. 307-310 [doi]
- A system for advice provision in multiple prospectselection problemsAmos Azaria, Sarit Kraus, Ariella Richardson. 311-314 [doi]
- Clustering-based factorized collaborative filteringNima Mirbakhsh, Charles X. Ling. 315-318 [doi]
- Cross social networks interests predictions based ongraph featuresAmit Tiroshi, Shlomo Berkovsky, Mohamed Ali Kâafar, Terence Chen, Tsvi Kuflik. 319-322 [doi]
- Differential data analysis for recommender systemsRichard Chow, Hongxia Jin, Bart P. Knijnenburg, Gökay Saldamli. 323-326 [doi]
- Effectiveness of the data generated on different time in latent factor modelQianru Zheng, Horace H. S. Ip. 327-330 [doi]
- Interview process learning for top-n recommendationFangwei Hu, Yong Yu. 331-334 [doi]
- Escape the bubble: guided exploration of music preferences for serendipity and noveltyMaria Taramigkou, Efthimios Bothos, Konstantinos Christidis, Dimitris Apostolou, Gregoris Mentzas. 335-338 [doi]
- Evaluating top-n recommendations "when the best are gone"Paolo Cremonesi, Franca Garzotto, Massimo Quadrana. 339-342 [doi]
- An analysis of tag-recommender evaluation proceduresStephan Doerfel, Robert Jäschke. 343-346 [doi]
- Recommendation in heterogeneous information networks with implicit user feedbackXiao Yu, Xiang Ren, Yizhou Sun, Bradley Sturt, Urvashi Khandelwal, Quanquan Gu, Brandon Norick, Jiawei Han. 347-350 [doi]
- Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systemsPanagiotis Adamopoulos, Alexander Tuzhilin. 351-354 [doi]
- Improving user profile with personality traits predicted from social media contentRui Gao, Bibo Hao, Shuotian Bai, Lin Li, Ang Li, Tingshao Zhu. 355-358 [doi]
- Leveraging the citation graph to recommend keywordsIdo Blank, Lior Rokach, Guy Shani. 359-362 [doi]
- Local context modeling with semantic pre-filteringVictor Codina, Francesco Ricci, Luigi Ceccaroni. 363-366 [doi]
- Musical recommendations and personalization in a social networkDmitry Bugaychenko, Alexandr Dzuba. 367-370 [doi]
- Not by search alone: how recommendations complement search resultsDaria Dzyabura, Alex Tuzhilin. 371-374 [doi]
- OFF-set: one-pass factorization of feature sets for online recommendation in persistent cold start settingsMichal Aharon, Natalie Aizenberg, Edward Bortnikov, Ronny Lempel, Roi Adadi, Tomer Benyamini, Liron Levin, Ran Roth, Ohad Serfaty. 375-378 [doi]
- Personalised ranking with diversityNeil J. Hurley. 379-382 [doi]
- Prior ratings: a new information source for recommender systems in e-commerceGuibing Guo, Jie Zhang, Daniel Thalmann, Neil Yorke-Smith. 383-386 [doi]
- Probabilistic collaborative filtering with negative cross entropyAlejandro Bellogín, Javier Parapar, Pablo Castells. 387-390 [doi]
- Recommending improved configurations for complex objects with an application in travel planningAmihai Savir, Ronen I. Brafman, Guy Shani. 391-394 [doi]
- Recommending patents based on latent topicsRalf Krestel, Padhraic Smyth. 395-398 [doi]
- Recommending scientific articles using bi-relational graph-based iterative RWRGeng Tian, Liping Jing. 399-402 [doi]
- Sample selection for MCMC-based recommender systemsThierry Silbermann, Immanuel Bayer, Steffen Rendle. 403-406 [doi]
- Selecting content-based features for collaborative filtering recommendersRoyi Ronen, Noam Koenigstein, Elad Ziklik, Nir Nice. 407-410 [doi]
- Sentimental product recommendationRuihai Dong, Michael P. O'Mahony, Markus Schaal, Kevin McCarthy, Barry Smyth. 411-414 [doi]
- Set-oriented personalized ranking for diversified top-n recommendationRuilong Su, Li'ang Yin, Kailong Chen, Yong Yu. 415-418 [doi]
- Towards scalable and accurate item-oriented recommendationsNoam Koenigstein, Yehuda Koren. 419-422 [doi]
- Using geospatial metadata to boost collaborative filteringAlexander Ostrikov, Lior Rokach, Bracha Shapira. 423-426 [doi]
- When power users attack: assessing impacts in collaborative recommender systemsDavid C. Wilson, Carlos E. Seminario. 427-430 [doi]
- xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevanceYue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Alan Hanjalic. 431-434 [doi]
- Evolving friend lists in social networksJacob W. Bartel, Prasun Dewan. 435-438 [doi]
- Exploratory and interactive daily deals recommendationAnísio Lacerda, Adriano Veloso, Nivio Ziviani. 439-442 [doi]
- Dynamic generation of personalized hybrid recommender systemsSimon Dooms. 443-446 [doi]
- Accuracy and robustness impacts of power user attacks on collaborative recommender systemsCarlos E. Seminario. 447-450 [doi]
- Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systemsGuibing Guo. 451-454 [doi]
- Agent-based computational investing recommender systemMona Taghavi, Kaveh Bakhtiyari, Edgar Scavino. 455-458 [doi]
- Beyond rating prediction accuracy: on new perspectives in recommender systemsPanagiotis Adamopoulos. 459-462 [doi]
- Anytime algorithms for top-N recommendersDavid Ben-Shimon. 463-466 [doi]
- GAIN: web service for user tracking and preference learning - a smart TV use caseJaroslav Kuchar, Tomás Kliegr. 467-468 [doi]
- PEN RecSys: a personalized news recommender systems frameworkFlorent Garcin, Boi Faltings. 469-470 [doi]
- A heterogeneous graph-based recommendation simulatorYeonchan Ahn Ahn, Sungchan Park, SangKeun Lee, Sang-goo Lee. 471-472 [doi]
- Design and evaluation of a client-side recommender systemChris Newell, Libby Miller. 473-474 [doi]
- Sage: recommender engine as a cloud serviceRoyi Ronen, Noam Koenigstein, Elad Ziklik, Mikael Sitruk, Ronen Yaari, Neta Haiby-Weiss. 475-476 [doi]
- The fifth ACM RecSys workshop on recommender systems and the social webBamshad Mobasher, Dietmar Jannach, Werner Geyer, Jill Freyne, Andreas Hotho, Sarabjot Singh Anand, Ido Guy. 477-478 [doi]
- Workshop on human decision making in recommender systems: decisions@RecSys'13Li Chen, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, Francesco Ricci, Giovanni Semeraro, Martijn C. Willemsen. 479-480 [doi]
- Workshop and challenge on news recommender systemsMozhgan Tavakolifard, Jon Atle Gulla, Kevin C. Almeroth, Frank Hopfgartner, Benjamin Kille, Till Plumbaum, Andreas Lommatzsch, Torben Brodt, Arthur Bucko, Tobias Heintz. 481-482 [doi]
- Workshop on recommender systems meet big data & semantic technologies: SeRSy 2013Marco de Gemmis, Tommaso Di Noia, Ora Lassila, Pasquale Lops, Thomas Lukasiewicz, Giovanni Semeraro. 483-484 [doi]
- Workshop on reproducibility and replication in recommender systems evaluation: RepSysAlejandro Bellogín, Pablo Castells, Alan Said, Domonkos Tikk. 485-486 [doi]
- First workshop on large-scale recommender systems: research and best practice(LSRS 2013)Tao Ye, Danny Bickson, Quan Yuan. 487-488 [doi]
- RecSys challenge 2013Jim Blomo, Martin Ester, Marty Field. 489-490 [doi]
- Recommendation in social networksMartin Ester. 491-492 [doi]
- Learning to rank for recommender systemsAlexandros Karatzoglou, Linas Baltrunas, Yue Shi. 493-494 [doi]
- Beyond friendship: the art, science and applications of recommending people to people in social networksLuiz Augusto Pizzato, Anmol Bhasin. 495-496 [doi]
- Tutorial on preference handlingAlexis Tsoukiàs, Paolo Viappiani. 497-498 [doi]