in conjunction with the DePaul Center for


Event Sponsors:

in cooperation with SIGECOM, SIGKDD, and SIGCHI

Accepted Papers

Long Papers, Oral Presentation

  • Liang Zhang, Deepak Agarwal and Bee-Chung Chen: Generalizing Matrix Factorization Through Flexible Regression Priors
  • Sibren Isaacman, Stratis Ioannidis, Augustin Chaintreau and Margaret Martonosi: Distributed Rating Prediction in User Generated Content Streams
  • Heung-Nam Kim and Abdulmotaleb El Saddik: Personalized PageRank Vectors for Tag Recommendations: Inside FolkRank
  • Yehuda Koren and Joe Sill: OrdRec: An ordinal model for predicting personalized item rating distributions
  • Sangkeun Lee, Sang-Il Song, Minsuk Kahng, Dongjoo Lee and Sang-Goo Lee: Random Walk based Entity Ranking on Graph for Multidimensional Recommendation
  • Panagiotis Symeonidis, Eleftherios Tiakas and Yannis Manolopoulos: Product Recommendation and Rating Prediction based on Multi-modal Social Networks
  • Yu Zhao, Xinping Feng, Jianqiang Li and Bo Liu: Shared Collaborative Filtering
  • Gideon Dror, Noam Koenigstein and Yehuda Koren: Yahoo! Music Recommendations: Modeling Music Ratings with Temporal Dynamics and Item Taxonomy
  • Mohsen Jamali, Tianle Huang and Martin Ester: A Generalized Stochastic Block Model for Recommendation in Social Rating Networks
  • E. Isaac Sparling and Shilad Sen: Rating: How difficult is it?
  • Pearl Pu, Li Chen and Rong Hu: A User-Centric Evaluation Framework for Recommender Systems
  • Shunichi Seko, Takashi Yagi, Manabu Motegi and Shinyo Muto: Group Recommendation using Feature Space representing Behavioral Tendency and Power Balance among Members
  • Michele Gorgoglione, Umberto Panniello and Alexander Tuzhilin: The Effect of Context-Aware Recommendations on Customer Purchasing Behavior and Trust
  • Nicola Barbieri, Gianni Costa, Giuseppe Manco and Riccardo Ortale: Modeling Item Selection and Relevance for Accurate Recommendations: a Bayesian Approach
  • Michael D. Ekstrand, Michael Ludwig, Joseph A. Konstan and John T. Riedl: Rethinking the Recommender Research Ecosystem: Reproducibility, Openness, and LensKit
  • Liwei Liu, Nikolay Mehandjiev and Ling Xu: Multi-Criteria Service Recommendation Based on User Criteria Preferences
  • Mohammad A. Tayebi, Mohsen Jamali, Martin Ester, Uwe Glasser and Richard Frank: CrimeWalker: A Recommendation Model for Suspect Investigation
  • Harald Steck: Item Popularity and Recommendation Accuracy
  • Saúl Vargas and Pablo Castells: Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems
  • Nathan Liu, Xiangrui Meng, Chao Liu: Wisdom of the Better Few: Cold Start Recommendation via Representative based Rating Elicitation
  • Ido Guy, Inbal Ronen and Ariel Raviv: Personalized Activity Streams: Sifting through the “River of News"
  • Bart Knijnenburg, Niels Reijmer and Martijn Willemsen: Each to His Own: How Different Users Call for Different Interaction Methods in Recommender Systems

Long Papers, Poster Presentation

  • Masoud Makrehchi: Social Links Recommendation by Learning Hidden Topics
  • Rong Hu and Pearl Pu: Enhancing Collaborative Filtering Systems with Personality Information
  • Sarabjot Singh Anand and Nathan Griffiths: A Market-based Approach to address the New Item problem
  • Kibeom Lee and Kyogu Lee: My Head is Your Tail: Applying Link Analysis on Long-Tailed Music Listening Behavior for Music Recommendation
  • Yu Xin and Harald Steck: Multi-Value Probabilistic Matrix Factorization for IP-TV Recommendations
  • Oliver Jojic, Manu Shukla and Niranjan Bhosarekar: A Probabilistic Definition of Item Similarity
  • Shankar Prawesh and Balaji Padmanabhan: The "Top N" News Recommender: Count Distortion and Manipulation Resistance
  • Quan Yuan, Li Chen and Shiwan Zhao: Factorization VS. Regularization: Fusing Heterogeneous Social Relationships in Top-N Recommendation

Short Papers, Poster Presentation

  • Matthias Braunhofer, Marius Kaminskas and Francesco Ricci: Recommending Music for Places of Interest in a Mobile Travel Guide
  • Le Yu, Rong Pan and Zhangfeng Li: Adaptive Social Similarities for Recommender Systems
  • Peter Forbes and Mu Zhu: Content-boosted Matrix Factorization for Recommender Systems: Experiments with Recipe Recommendation
  • Francesca Guzzi, Francesco Ricci and Robin Burke: Interactive Multi-Party Critiquing for Group Recommendation
  • Wolfgang Woerndl, Johannes Huebner, Roland Bader and Daniel Gallego Vico: A Model for Proactivity in Mobile, Context-aware Recommender Systems
  • Elizabeth Daly and Werner Geyer: Effective Event Discovery: Using Location and Social Information for Scoping Event Recommendations
  • Yong Ge, Hui Xiong, Alexander Tuzhilin and Qi Liu: Collaborative Filtering with Collective Training
  • Gilad Katz, Nir Ofek, Bracha Shapira, Lior Rokach and Guy Shani: Using Wikipedia to boost collaborative filtering techniques
  • Zhiang Wu, Jie Cao, Bo Mao and Youquan Wang. Semi-SAD: Applying Semi-supervised Learning to Shilling Attack Detection
  • Pasquale Lops, Fedelucio Narducci, Cataldo Musto, Marco De Gemmis and Giovanni Semeraro: Leveraging the LinkedIn Social Network Data for Extracting Content-based User Profiles
  • Gabor Takacs, Istvan Pilaszy and Domonkos Tikk: Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering
  • Linas Baltrunas, Bernd Ludwig and Francesco Ricci: Matrix Factorization Techniques for Context Aware Recommendation
  • Zeno Gantner, Steffen Rendle, Christoph Freudenthaler and Lars Schmidt-Thieme: MyMediaLite: A Free Recommender System Library
  • Pedro G. Campos, Fernando Díez and Manuel Sánchez-Montañés: Towards a More Realistic Evaluation: Testing the Ability to Predict Future Tastes of Matrix Factorization-based Recommenders
  • Alexandros Karatzoglou: Collaborative Temporal Order Modeling
  • Lei Li, Li Zheng and Tao Li: LOGO: A Long-Short User Interest Integration in Personalized News Recommendation
  • Bart Knijnenburg, Martijn Willemsen and Alfred Kobsa: A Pragmatic Procedure to Support the User-Centric Evaluation of Recommender Systems
  • Ioannis Paparrizos, Berkant Barla Cambazoglu and Aristides Gionis: Machine learned job recommendation
  • Jian Wang, Badrul Sarwar and Neel Sundaresan: Utilizing Related Product for Post-Purchase Recommendation in E-commerce
  • Alejandro Bellogin, Pablo Castells and Ivan Cantador: Precision-Based Evaluation of Recommender Systems: An Algorithmic Comparison
  • Steven Bourke, Kevin Mccarthy and Barry Smyth: Power to the people:Exploring neighbourhood formations in social recommender systems
  • Luiz Augusto Pizzato and Cameron Silvestrini: Stochastic Matching and Collaborative Filtering to Recommend People to People
  • Shanchan Wu, William Rand and Louiqa Raschid: Recommendations in Social Media for Brand Monitoring