# Infer Implicit Contexts in Real-time Online-to-Offline Recommendation

## TL;DR

This paper introduces MACDAE, a novel model that infers implicit user contexts in real-time to enhance online-to-offline recommendations, demonstrating significant offline and online performance improvements.

## Contribution

The paper proposes MACDAE, a new autoencoder-based approach that effectively infers implicit user contexts for dynamic O2O recommendation systems.

## Key findings

- Achieved significant offline improvements over state-of-the-art methods.
- Online A/B testing showed a 2.9% increase in click-through rate.
- Deployed in Koubei's 'Guess You Like' feature with positive results.

## Abstract

Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei. Different from traditional recommendation where individual preference is mostly static, O2O recommendation should be dynamic to capture variation of users' purposes across time and location. However, precisely inferring users' real-time contexts information, especially those implicit ones, is extremely difficult, and it is a central challenge for O2O recommendation. In this paper, we propose a new approach, called Mixture Attentional Constrained Denoise AutoEncoder (MACDAE), to infer implicit contexts and consequently, to improve the quality of real-time O2O recommendation. In MACDAE, we first leverage the interaction among users, items, and explicit contexts to infer users' implicit contexts, then combine the learned implicit-context representation into an end-to-end model to make the recommendation. MACDAE works quite well in the real system. We conducted both offline and online evaluations of the proposed approach. Experiments on several real-world datasets (Yelp, Dianping, and Koubei) show our approach could achieve significant improvements over state-of-the-arts. Furthermore, online A/B test suggests a 2.9% increase for click-through rate and 5.6% improvement for conversion rate in real-world traffic. Our model has been deployed in the product of "Guess You Like" recommendation in Koubei.