Learning Representations of Hierarchical Slates in Collaborative
Filtering
- URL: http://arxiv.org/abs/2010.06987v1
- Date: Fri, 25 Sep 2020 18:34:02 GMT
- Title: Learning Representations of Hierarchical Slates in Collaborative
Filtering
- Authors: Ehtsham Elahi and Ashok Chandrashekar
- Abstract summary: We are interested in building collaborative filtering models for recommendation systems where users interact with slates instead of individual items.
The central idea of our approach is to learn low dimensional embeddings of these slates.
We present a novel way to learn these embeddings by making use of the (unknown) statistics of the underlying distribution generating the hierarchical data.
- Score: 2.8145809047875066
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We are interested in building collaborative filtering models for
recommendation systems where users interact with slates instead of individual
items. These slates can be hierarchical in nature. The central idea of our
approach is to learn low dimensional embeddings of these slates. We present a
novel way to learn these embeddings by making use of the (unknown) statistics
of the underlying distribution generating the hierarchical data. Our
representation learning algorithm can be viewed as a simple composition rule
that can be applied recursively in a bottom-up fashion to represent arbitrarily
complex hierarchical structures in terms of the representations of its
constituent components. We demonstrate our ideas on two real world
recommendation systems datasets including the one used for the RecSys 2019
challenge. For that dataset, we improve upon the performance achieved by the
winning team's model by incorporating embeddings as features generated by our
approach in their solution.
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