Social links recommendation by learning hidden topics
by Masoud Makrehchi

Enhancing collaborative filtering systems with personality information
by Rong Hu and Pearl Pu

A market-based approach to address the new item problem
by Sarabjot Singh Anand and Nathan Griffiths

My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation
by Kibeom Lee and Kyogu Lee

Multi-value probabilistic matrix factorization for IP-TV recommendations
by Yu Xin and Harald Steck

A probabilistic definition of item similarity
by Oliver Jojic, Manu Shukla and Niranjan Bhosarekar

The top n news recommender: count distortion and manipulation resistance
by Shankar Prawesh and Balaji Padmanabhan

Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation
by Quan Yuan, Li Chen and Shiwan Zhao

• 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