Accepted papers of ACM Recommender Systems 2011

[丕子]

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: Activity Stream Personalization: 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: Personalized Recommendations based on 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
• Xin Su and Xin Li: Social Network-based Recommendation: A Graph Random Walk Kernel Approach
• 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