Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic
Gates
- URL: http://arxiv.org/abs/2112.07615v1
- Date: Sun, 12 Dec 2021 11:37:24 GMT
- Title: Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic
Gates
- Authors: Oren Barkan, Roy Hirsch, Ori Katz, Avi Caciularu, Jonathan Weill, Noam
Koenigstein
- Abstract summary: A major challenge in collaborative filtering methods is how to produce recommendations for cold items.
We propose a novel hybrid recommendation algorithm that bridges these two conflicting objectives.
We demonstrate the effectiveness of the proposed algorithm on movies, apps, and articles recommendations.
- Score: 19.69804455785047
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: A major challenge in collaborative filtering methods is how to produce
recommendations for cold items (items with no ratings), or integrate cold item
into an existing catalog. Over the years, a variety of hybrid recommendation
models have been proposed to address this problem by utilizing items' metadata
and content along with their ratings or usage patterns. In this work, we wish
to revisit the cold start problem in order to draw attention to an overlooked
challenge: the ability to integrate and balance between (regular) warm items
and completely cold items. In this case, two different challenges arise: (1)
preserving high quality performance on warm items, while (2) learning to
promote cold items to relevant users. First, we show that these two objectives
are in fact conflicting, and the balance between them depends on the business
needs and the application at hand. Next, we propose a novel hybrid
recommendation algorithm that bridges these two conflicting objectives and
enables a harmonized balance between preserving high accuracy for warm items
while effectively promoting completely cold items. We demonstrate the
effectiveness of the proposed algorithm on movies, apps, and articles
recommendations, and provide an empirical analysis of the cold-warm trade-off.
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