CAViaR: Context Aware Video Recommendations
- URL: http://arxiv.org/abs/2304.08435v1
- Date: Mon, 17 Apr 2023 16:56:23 GMT
- Title: CAViaR: Context Aware Video Recommendations
- Authors: Khushhall Chandra Mahajan, Aditya Palnitkar, Ameya Raul, Brad
Schumitsch
- Abstract summary: We propose a novel method which introduces diversity by modeling the impact of low diversity on user's engagement on individual items.
The proposed method is designed to be easily pluggable into existing large-scale recommender systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many recommendation systems rely on point-wise models, which score items
individually. However, point-wise models generating scores for a video are
unable to account for other videos being recommended in a query. Due to this,
diversity has to be introduced through the application of heuristic-based
rules, which are not able to capture user preferences, or make balanced
trade-offs in terms of diversity and item relevance. In this paper, we propose
a novel method which introduces diversity by modeling the impact of low
diversity on user's engagement on individual items, thus being able to account
for both diversity and relevance to adjust item scores. The proposed method is
designed to be easily pluggable into existing large-scale recommender systems,
while introducing minimal changes in the recommendations stack. Our models show
significant improvements in offline metrics based on the normalized cross
entropy loss compared to production point-wise models. Our approach also shows
a substantial increase of 1.7% in topline engagements coupled with a 1.5%
increase in daily active users in an A/B test with live traffic on Facebook
Watch, which translates into an increase of millions in the number of daily
active users for the product.
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