Multi-expert neural networks for recommendation

Kawato, Takeshi

Student Work

Multi-expert neural networks for recommendation

Public

This project investigated a mixture of experts neural architecture for a combined collaborative and content-based recommender system. The effect of first reducing the dimensionality of the input data using the singular value decomposition was also studied. We showed that the mixture of experts architecture achieves the same recommendation quality as a fully-connected architecture while requiring less computation time, or, if desired, higher quality can be achieved with only slight increase in running time.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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