# Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up

## TL;DR

This paper introduces AVAEW, a novel adversarial variational auto-encoder model that generates warm-up item embeddings for cold items, improving recommendation accuracy by aligning embedding distributions.

## Contribution

The paper proposes a new adversarial variational auto-encoder approach leveraging item side information to generate effective warm-up embeddings for cold items.

## Key findings

- Significant improvement in cold item recommendation accuracy.
- Effective alignment of embedding distributions demonstrated.
- Validated through offline and online experiments.

## Abstract

The gap between the randomly initialized item ID embedding and the well-trained warm item ID embedding makes the cold items hard to suit the recommendation system, which is trained on the data of historical warm items. To alleviate the performance decline of new items recommendation, the distribution of the new item ID embedding should be close to that of the historical warm items. To achieve this goal, we propose an Adversarial Variational Auto-encoder Warm-up model (AVAEW) to generate warm-up item ID embedding for cold items. Specifically, we develop a conditional variational auto-encoder model to leverage the side information of items for generating the warm-up item ID embedding. Particularly, we introduce an adversarial module to enforce the alignment between warm-up item ID embedding distribution and historical item ID embedding distribution. We demonstrate the effectiveness and compatibility of the proposed method by extensive offline experiments on public datasets and online A/B tests on a real-world large-scale news recommendation platform.