Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust

Social recommendation could address the data sparsity and cold-start problems for collaborative filtering by leveraging user trust relationships as auxiliary information for recommendation. However, most existing methods tend to consider the trust relationship as preference similarity in a static way and model the representations for user preference and social trust via a common feature space. In this paper, we propose TrustEV and take the view of multi-task learning to unite collaborative filtering for recommendation and network embedding for user trust. We design a special feature evolution unit that enables the embedding vectors for two tasks to exchange their features in a probabilistic manner, and further harness a meta-controller to globally explore proper settings for the feature evolution units. The training process contains two nested loops, where in the outer loop, we optimize the meta-controller by Bayesian optimization, and in the inner loop, we train the feedforward model with given feature evolution units. Experiment results show that TrustEV could make better use of social information and greatly improve recommendation MAE over state-of-the-art approaches.

Authors

• Qitian Wu China Mobile Information Technology Co.,Ltd
• Lei Jiang University of Science and Technology of China
• Xiaofeng Gao Shanghai Jiao Tong University
• Xiaochun Yang School of Computer Science and Engineering, Northeastern University, China
• Guihai Chen Nanjing University

Keywords

• Machine Learning Applications: Networks
• Machine Learning: Learning Preferences or Rankings
• Machine Learning: Recommender Systems
• Machine Learning: Transfer, Adaptation, Multi-task Learning
• Multidisciplinary Topics and Applications: Social Sciences