Ranking model plays an essential role in e-commerce search and recommendation. An effective ranking model should give a personalized ranking list for each user according to the user preference. Existing algorithms usually extract a user representation vector from the user behavior sequence, then feed the vector into a feed-forward network (FFN) together with other features for feature interactions, and finally produce a personalized ranking score. Despite tremendous progress in the past, there is still room for improvement. Firstly, the personalized patterns of feature interactions for different users are not explicitly modeled. Secondly, most of existing algorithms have poor personalized ranking results for long-tail users with few historical behaviors due to the data sparsity. To overcome the two challenges, we propose Attention Weighted Mixture of Experts (AW-MoE) with contrastive learning for personalized ranking. Firstly, AW-MoE leverages the MoE framework to capture personalized feature interactions for different users. To model the user preference, the user behavior sequence is simultaneously fed into expert networks and the gate network. Within the gate network, one gate unit and one activation unit are designed to adaptively learn the fine-grained activation vector for experts using an attention mechanism. Secondly, a random masking strategy is applied to the user behavior sequence to simulate long-tail users, and an auxiliary contrastive loss is imposed to the output of the gate network to improve the model generalization for these users. This is validated by a higher performance gain on the long-tail user test set. Experiment results on a JD real production dataset and a public dataset demonstrate the effectiveness of AW-MoE, which significantly outperforms state-of-art methods. Notably, AW-MoE has been successfully deployed in the JD e-commerce search engine, ...

翻译：排序模型在电商搜索与推荐中扮演着至关重要的角色。有效的排序模型应根据用户偏好为每位用户生成个性化的排序列表。现有算法通常从用户行为序列中提取用户表示向量，随后将该向量与其他特征一同输入前馈网络（FFN）进行特征交互，最终生成个性化排序分数。尽管过去取得了巨大进展，但仍有改进空间。首先，不同用户的特征交互个性化模式未被显式建模。其次，由于数据稀疏性，大多数现有算法对历史行为稀少的尾部用户的个性化排序效果较差。为克服这两个挑战，我们提出融合对比学习的注意力加权混合专家（AW-MoE）模型，用于个性化排序。首先，AW-MoE利用MoE框架为不同用户捕捉个性化特征交互。为建模用户偏好，用户行为序列被同时输入专家网络和门控网络。在门控网络中，我们设计了一个门单元和一个激活单元，通过注意力机制自适应学习针对专家的细粒度激活向量。其次，采用随机掩码策略对用户行为序列进行扰动以模拟尾部用户，并对门控网络的输出施加辅助对比损失，以提升模型对这些用户的泛化能力，这一点在尾部用户测试集上更高的性能增益中得到验证。在京东真实生产数据集和公共数据集上的实验结果表明，AW-MoE的有效性显著优于当前最先进的方法。值得注意的是，AW-MoE已成功部署于京东电商搜索引擎中，……