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Hector Yee

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2 papers

1 author row

ICML Conference 2014 Conference Paper

Affinity Weighted Embedding

Jason Weston
Ron J. Weiss
Hector Yee

Supervised linear embedding models like Wsabie (Weston et al. , 2011) and supervised semantic indexing (Bai et al. , 2010) have proven successful at ranking, recommendation and annotation tasks. However, despite being scalable to large datasets they do not take full advantage of the extra data due to their linear nature, and we believe they typically underfit. We propose a new class of models which aim to provide improved performance while retaining many of the benefits of the existing class of embedding models. Our approach works by reweighting each component of the embedding of features and labels with a potentially nonlinear affinity function. We describe several variants of the family, and show its usefulness on several datasets.

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ICML Conference 2013 Conference Paper

Label Partitioning For Sublinear Ranking

Jason Weston
Ameesh Makadia
Hector Yee

We consider the case of ranking a very large set of labels, items, or documents, which is common to information retrieval, recommendation, and large-scale annotation tasks. We present a general approach for converting an algorithm which has linear time in the size of the set to a sublinear one via label partitioning. Our method consists of learning an input partition and a label assignment to each partition of the space such that precision at k is optimized, which is the loss function of interest in this setting. Experiments on large-scale ranking and recommendation tasks show that our method not only makes the original linear time algorithm computationally tractable, but can also improve its performance.

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