Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation
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Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, Jiawei Han

For POI recommendation, we aim to alleviate the scarcity of check-in data via smoothing among similar users and places on the context graphs, which are constructed to take various context information around users (e.g., friendships) and places (e.g., geographical distances). A deep neural architecture called PACE that generalizes matrix factorization and graph laplacian regularizer is developed to bridge collaborative filtering and semi-supervised learning.

The 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Halifax, Canada, 2017.


Bi-directional Joint Inference for User Links and Attributes on Large Social Graphs
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Carl Yang, Zhong Lin, Li-Jia Li, Jie Luo

We propose to jointly infer user links and attributes by exploiting homopily and iteratively addressing smoothness on the social graphs through two directions, i.e., from closeness to similarity (stronger links lead to mrore similar attributes), and vice versa. The two processes are done in a unified probabilistic framework through label propagation and graph construction.

The 26th International World Wide Web Conference (WWW), Perth, Australia, 2017.


Geodesic Distance Function Learning via Heat Flows on Vector Fields
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Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye

We propose to learn the geodesic distance funtion d(p, ·) on data manifolds based on severe theoretical analysis. Specifically, we first learn the gradient field of the distance function by transporting an initial vector field around p to the whole manifold via heat flows on vector fields. Then we obtain d(p, ·) by requiring its gradient field to be close to the normalized vector field.

The 31th International Conference on Machine Learning (ICML), Beijing, China, 2014.


Multi-Query Parallel Field Ranking for Image Retrieval
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Ji Yang, Bin Xu, Binbin Lin, Xiaofei He

For multi-query retrieval tasks, we propose a novel approach which finds an optimal ranking function whose gradient field is as parallel as possible. In this way, the obtained ranking function varies linearly along the geodesics of the data manifold, and achieves the highest value at multiple queries simultaneously, making efficient use of query information and the intrinsic distribution of data.

Neurocomputing, 2014.


Local Coordinate Concept Factorization for Image Representation
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Haifeng Liu, Zheng Yang, Ji Yang, Zhaohui Wu, Xuelong Li

We introduce a locality constraint into the traditional concept factorization. By requiring the concepts (basis vectors) to be as close to the original data points as possible, we represent each data point by a linear combination of only a few basis concepts, thus addressing sparsity and locality simultaneously.

IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2014.