Cross domain recommender system constitutes a powerful method to tackle the
cold-start and sparsity problem by aggregating and transferring user
preferences across multiple category domains. Therefore, it has great potential
to improve click-through-rate prediction performance in online commerce
platforms having many domains of products. While several cross domain
sequential recommendation models have been proposed to leverage information
from a source domain to improve CTR predictions in a target domain, they did
not take into account bidirectional latent relations of user preferences across
source-target domain pairs. As such, they cannot provide enhanced cross-domain
CTR predictions for both domains simultaneously. In this paper, we propose a
novel approach to cross-domain sequential recommendations based on the dual
learning mechanism that simultaneously transfers information between two
related domains in an iterative manner until the learning process stabilizes.
In particular, the proposed Dual Attentive Sequential Learning (DASL) model
consists of two novel components Dual Embedding and Dual Attention, which
jointly establish the two-stage learning process: we first construct dual
latent embeddings that extract user preferences in both domains simultaneously,
and subsequently provide cross-domain recommendations by matching the extracted
latent embeddings with candidate items through dual-attention learning
mechanism. We conduct extensive offline experiments on three real-world
datasets to demonstrate the superiority of our proposed model, which
significantly and consistently outperforms several state-of-the-art baselines
across all experimental settings. We also conduct an online A/B test at a major
video streaming platform Alibaba-Youku, where our proposed model significantly
improves business performance over the latest production system in the company.

提出了一种基于双学习机制的跨领域用户行为建模方法，为两个相关领域建立双重的潜在嵌入，实现了在两个目标域上同时提高 CTR 预测性能的目标，实验证明了比其他最先进的模型表现更好的效率和性能。