| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 103 === Matrix factorization (MF) methods provide one of the most effective approaches to collaborative filtering. Its main idea is that according to the behavior patterns of people, we can learn the latent factors behind these patterns. Those factors can help up to predict behavior of people in the future. The study also extends this concept to a network analysis to explore the relationship between objects and objects, and we call this task latent collaborative relations. In addition, we can also take account of the object’s profile. Beyond accuracy, we also discuss another crucial metric: coverage and ponder the long tail phenomenon. Finally, we reduce the retrieval of recommendation in this model to a simple task of k-nearest-neighbor search via multi-probe locality sensitive hashing. We evaluate our algorithms on real-world datasets, demonstrating 5-300x speedup with respect to the naive linear search.
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