default search action
RecSys 2013: Hong Kong, China
- Qiang Yang, Irwin King, Qing Li, Pearl Pu, George Karypis:
Seventh ACM Conference on Recommender Systems, RecSys '13, Hong Kong, China, October 12-16, 2013. ACM 2013, ISBN 978-1-4503-2409-0
Technical session: context-aware
- Jiliang Tang, Huiji Gao, Xia Hu, Huan Liu:
Context-aware review helpfulness rating prediction. 1-8 - Negar Hariri, Bamshad Mobasher
, Robin D. Burke
Query-driven context aware recommendation. 9-16 - Marius Kaminskas, Francesco Ricci
Location-aware music recommendation using auto-tagging and hybrid matching. 17-24 - Bo Hu, Martin Ester:
Spatial topic modeling in online social media for location recommendation. 25-32
Technical session: methods, algorithms, and theory I
- Hossein Vahabi
Orthogonal query recommendation. 33-40 - Li Pu, Boi Faltings:
Understanding and improving relational matrix factorization in recommender systems. 41-48 - Oluwasanmi Koyejo, Sreangsu Acharyya, Joydeep Ghosh:
Retargeted matrix factorization for collaborative filtering. 49-56 - Lei Shi:
Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach. 57-64 - Jason Weston, Ron J. Weiss, Hector Yee:
Nonlinear latent factorization by embedding multiple user interests. 65-68
Technical session: social media and recommender systems
- Ye Pan, Feng Cong, Kailong Chen, Yong Yu:
Diffusion-aware personalized social update recommendation. 69-76 - Yongzheng Zhang, Marco Pennacchiotti:
Recommending branded products from social media. 77-84 - Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio
Top-N recommendations from implicit feedback leveraging linked open data. 85-92 - Huiji Gao, Jiliang Tang, Xia Hu, Huan Liu:
Exploring temporal effects for location recommendation on location-based social networks. 93-100 - Zurina Saaya
The curated web: a recommendation challenge. 101-104
Technical session: media recommendation
- Florent Garcin, Christos Dimitrakakis
Personalized news recommendation with context trees. 105-112 - Maria Soledad Pera
What to read next?: making personalized book recommendations for K-12 users. 113-120 - Amos Azaria
Movie recommender system for profit maximization. 121-128 - Noam Koenigstein
Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection. 129-136 - Xiang Wu, Qi Liu, Enhong Chen, Liang He
Personalized next-song recommendation in online karaokes. 137-140
Technical session: user experience
- Fabiano Belém, Rodrygo L. T. Santos
Topic diversity in tag recommendation. 141-148 - Tien T. Nguyen, Daniel Kluver, Ting-Yu Wang, Pik-Mai Hui, Michael D. Ekstrand
Rating support interfaces to improve user experience and recommender accuracy. 149-156 - Peter Grasch, Alexander Felfernig, Florian Reinfrank:
ReComment: towards critiquing-based recommendation with speech interaction. 157-164 - Julian J. McAuley, Jure Leskovec
Hidden factors and hidden topics: understanding rating dimensions with review text. 165-172 - Zhuo Zhang, Shang Shang, Sanjeev R. Kulkarni, Pan Hui:
Improving augmented reality using recommender systems. 173-176
Technical session: beyond ratings
- Fernando Mourão, Leonardo Rocha
Exploiting non-content preference attributes through hybrid recommendation method. 177-184 - Houda Khrouf, Raphaël Troncy
Hybrid event recommendation using linked data and user diversity. 185-192 - Amit Sharma, Baoshi Yan:
Pairwise learning in recommendation: experiments with community recommendation on linkedin. 193-200 - Nagarajan Natarajan, Donghyuk Shin
Which app will you use next?: collaborative filtering with interactional context. 201-208 - Toon De Pessemier, Simon Dooms, Luc Martens:
A food recommender for patients in a care facility. 209-212
Technical session: methods, algorithms, and theory II
- Harald Steck:
Evaluation of recommendations: rating-prediction and ranking. 213-220 - Konstantinos Babas, Georgios Chalkiadakis
You are what you consume: a bayesian method for personalized recommendations. 221-228 - Weinan Zhang, Jun Wang, Bowei Chen
To personalize or not: a risk management perspective. 229-236 - Jialei Wang, Steven C. H. Hoi
Online multi-task collaborative filtering for on-the-fly recommender systems. 237-244 - Jason Weston, Hector Yee, Ron J. Weiss:
Learning to rank recommendations with the k-order statistic loss. 245-248
Technical session: scalability
- Yong Zhuang, Wei-Sheng Chin, Yu-Chin Juan, Chih-Jen Lin
A fast parallel SGD for matrix factorization in shared memory systems. 249-256 - Aapo Kyrola:
DrunkardMob: billions of random walks on just a PC. 257-264 - Mikael Hammar, Robin Karlsson, Bengt J. Nilsson:
Using maximum coverage to optimize recommendation systems in e-commerce. 265-272 - Fabio Aiolli:
Efficient top-n recommendation for very large scale binary rated datasets. 273-280 - Sebastian Schelter, Christoph Boden, Martin Schenck, Alexander Alexandrov, Volker Markl:
Distributed matrix factorization with mapreduce using a series of broadcast-joins. 281-284
Industry session
- Mengxi Xu, Shlomo Berkovsky
Catch-up TV recommendations: show old favourites and find new ones. 285-294 - Haishan Liu, Mohammad Shafkat Amin, Baoshi Yan, Anmol Bhasin:
Generating supplemental content information using virtual profiles. 295-302
Poster session
- Ammar S. Alanazi
A people-to-people content-based reciprocal recommender using hidden markov models. 303-306 - Henry Blanco
Acquiring user profiles from implicit feedback in a conversational recommender system. 307-310 - Amos Azaria, Sarit Kraus, Ariella Richardson:
A system for advice provision in multiple prospectselection problems. 311-314 - Nima Mirbakhsh, Charles X. Ling:
Clustering-based factorized collaborative filtering. 315-318 - Amit Tiroshi, Shlomo Berkovsky
Cross social networks interests predictions based ongraph features. 319-322 - Richard Chow, Hongxia Jin, Bart P. Knijnenburg, Gökay Saldamli:
Differential data analysis for recommender systems. 323-326 - Qianru Zheng, Horace H. S. Ip
Effectiveness of the data generated on different time in latent factor model. 327-330 - Fangwei Hu, Yong Yu:
Interview process learning for top-n recommendation. 331-334 - Maria Taramigkou, Efthimios Bothos
Escape the bubble: guided exploration of music preferences for serendipity and novelty. 335-338 - Paolo Cremonesi
Evaluating top-n recommendations "when the best are gone". 339-342 - Stephan Doerfel, Robert Jäschke
An analysis of tag-recommender evaluation procedures. 343-346 - Xiao Yu, Xiang Ren, Yizhou Sun, Bradley Sturt, Urvashi Khandelwal, Quanquan Gu, Brandon Norick, Jiawei Han:
Recommendation in heterogeneous information networks with implicit user feedback. 347-350 - Panagiotis Adamopoulos
Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems. 351-354 - Rui Gao, Bibo Hao
Improving user profile with personality traits predicted from social media content. 355-358 - Ido Blank, Lior Rokach, Guy Shani
Leveraging the citation graph to recommend keywords. 359-362 - Victor Codina, Francesco Ricci
Local context modeling with semantic pre-filtering. 363-366 - Dmitry Bugaychenko, Alexandr Dzuba:
Musical recommendations and personalization in a social network. 367-370 - Daria Dzyabura
Not by search alone: how recommendations complement search results. 371-374 - Michal Aharon, Natalie Aizenberg, Edward Bortnikov, Ronny Lempel, Roi Adadi, Tomer Benyamini, Liron Levin, Ran Roth, Ohad Serfaty:
OFF-set: one-pass factorization of feature sets for online recommendation in persistent cold start settings. 375-378 - Neil J. Hurley
Personalised ranking with diversity. 379-382 - Guibing Guo, Jie Zhang, Daniel Thalmann, Neil Yorke-Smith:
Prior ratings: a new information source for recommender systems in e-commerce. 383-386 - Alejandro Bellogín
Probabilistic collaborative filtering with negative cross entropy. 387-390 - Amihai Savir, Ronen I. Brafman
Recommending improved configurations for complex objects with an application in travel planning. 391-394 - Ralf Krestel, Padhraic Smyth
Recommending patents based on latent topics. 395-398 - Geng Tian, Liping Jing:
Recommending scientific articles using bi-relational graph-based iterative RWR. 399-402 - Thierry Silbermann, Immanuel Bayer, Steffen Rendle:
Sample selection for MCMC-based recommender systems. 403-406 - Royi Ronen, Noam Koenigstein
Selecting content-based features for collaborative filtering recommenders. 407-410 - Ruihai Dong, Michael P. O'Mahony
Sentimental product recommendation. 411-414 - Ruilong Su, Li'ang Yin, Kailong Chen, Yong Yu:
Set-oriented personalized ranking for diversified top-n recommendation. 415-418 - Noam Koenigstein
Towards scalable and accurate item-oriented recommendations. 419-422 - Alexander Ostrikov, Lior Rokach, Bracha Shapira
Using geospatial metadata to boost collaborative filtering. 423-426 - David C. Wilson, Carlos E. Seminario:
When power users attack: assessing impacts in collaborative recommender systems. 427-430 - Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha A. Larson, Alan Hanjalic:
xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance. 431-434 - Jacob W. Bartel, Prasun Dewan:
Evolving friend lists in social networks. 435-438 - Anísio Lacerda, Adriano Veloso, Nivio Ziviani:
Exploratory and interactive daily deals recommendation. 439-442
Doctoral symposium
- Simon Dooms:
Dynamic generation of personalized hybrid recommender systems. 443-446 - Carlos E. Seminario:
Accuracy and robustness impacts of power user attacks on collaborative recommender systems. 447-450 - Guibing Guo:
Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems. 451-454 - Mona Taghavi, Kaveh Bakhtiyari
Agent-based computational investing recommender system. 455-458 - Panagiotis Adamopoulos
Beyond rating prediction accuracy: on new perspectives in recommender systems. 459-462 - David Ben-Shimon:
Anytime algorithms for top-N recommenders. 463-466
Demonstrations
- Jaroslav Kuchar, Tomás Kliegr:
GAIN: web service for user tracking and preference learning - a smart TV use case. 467-468 - Florent Garcin, Boi Faltings:
PEN RecSys: a personalized news recommender systems framework. 469-470 - Yeonchan Ahn, Sungchan Park, Sangkeun Lee, Sang-goo Lee:
A heterogeneous graph-based recommendation simulator. 471-472 - Chris Newell, Libby Miller:
Design and evaluation of a client-side recommender system. 473-474 - Royi Ronen, Noam Koenigstein
Sage: recommender engine as a cloud service. 475-476
Workshops
- Bamshad Mobasher, Dietmar Jannach, Werner Geyer, Jill Freyne, Andreas Hotho, Sarabjot Singh Anand, Ido Guy:
The fifth ACM RecSys workshop on recommender systems and the social web. 477-478 - Li Chen, Marco de Gemmis
Workshop on human decision making in recommender systems: decisions@RecSys'13. 479-480 - Mozhgan Tavakolifard, Jon Atle Gulla, Kevin C. Almeroth, Frank Hopfgartner
Workshop and challenge on news recommender systems. 481-482 - Marco de Gemmis
Workshop on recommender systems meet big data & semantic technologies: SeRSy 2013. 483-484 - Alejandro Bellogín
Workshop on reproducibility and replication in recommender systems evaluation: RepSys. 485-486 - Tao Ye, Danny Bickson, Quan Yuan:
First workshop on large-scale recommender systems: research and best practice(LSRS 2013). 487-488 - Jim Blomo, Martin Ester, Marty Field:
RecSys challenge 2013. 489-490
Tutorials
- Martin Ester:
Recommendation in social networks. 491-492 - Alexandros Karatzoglou, Linas Baltrunas, Yue Shi:
Learning to rank for recommender systems. 493-494 - Luiz Augusto Pizzato, Anmol Bhasin:
Beyond friendship: the art, science and applications of recommending people to people in social networks. 495-496 - Alexis Tsoukiàs, Paolo Viappiani
Tutorial on preference handling. 497-498
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-11-19 20:33 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: