Categorical-Attributes-Based Multi-Level Classification for Recommender Systems

Many techniques to utilize side information of users and/or items
as inputs to recommenders to improve recommendation, especially
on cold-start items/users, have been developed over the years. In
this work, we test the approach of utilizing item side information,
specifically categorical attributes, in the output of recommendation
models either through multi-task learning or hierarchical classification.
We first demonstrate the efficacy of these approaches for both
matrix factorization and neural networks with a medium-size realword
data set. We then show that they improve a neural-network
based production model in an industrial-scale recommender system.
We demonstrate the robustness of the hierarchical classification
approach by introducing noise in building the hierarchy. Lastly, we
investigate the generalizability of hierarchical classification on a
simulated dataset by building two user models in which we can
fully control the generative process of user-item interactions.

• Data Mining and Modeling

• Machine Intelligence