Parallel Collaborative Filtering for Streaming Data
نویسندگان
چکیده
We present a distributed stochastic gradient descent algorithm for performing low-rank matrix factorization on streaming data. Low-rank matrix factorization is often used as a technique for collaborative filtering. As opposed to recent algorithms that perform matrix factorization in parallel on a batch of training examples [4], our algorithm operates on a stream of incoming examples. We experimentally compare our algorithm with a state-of-art method for performing low-rank matrix factorization on batch data.
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