Cloud solutions have gained significant popularity in the technology industry
as they offer a combination of services and tools to tackle specific problems.
However, despite their widespread use, the task of identifying appropriate
company customers for a specific target solution to the sales team of a
solution provider remains a complex business problem that existing matching
systems have yet to adequately address. In this work, we study the B2B solution
matching problem and identify two main challenges of this scenario: (1) the
modeling of complex multi-field features and (2) the limited, incomplete, and
sparse transaction data. To tackle these challenges, we propose a framework
CAMA, which is built with a hierarchical multi-field matching structure as its
backbone and supplemented by three data augmentation strategies and a
contrastive pre-training objective to compensate for the imperfections in the
available data. Through extensive experiments on a real-world dataset, we
demonstrate that CAMA outperforms several strong baseline matching models
significantly. Furthermore, we have deployed our matching framework on a system
of Huawei Cloud. Our observations indicate an improvement of about 30% compared
to the previous online model in terms of Conversion Rate (CVR), which
demonstrates its great business value.

基于多领域特征建模和数据增强策略，我们提出了一种名为 CAMA 的框架来解决 B2B 解决方案匹配问题，并通过实验证明 CAMA 相比现有基线模型在实际情况下显著提升商品转化率，展示了其重要的商业价值。

通过对比预训练提升多领域 B2B 云解决方案匹配

Enhancing Multi-field B2B Cloud Solution Matching via Contrastive  Pre-training

Timeliness and contextual accuracy of recommendations are increasingly
important when delivering contemporary digital marketing experiences.
Conventional recommender systems (RS) suggest relevant but time-invariant items
to users by accounting for their past purchases. These recommendations only map
to customers' general preferences rather than a customer's specific needs
immediately preceding a purchase. In contrast, RSs that consider the order of
transactions, purchases, or experiences to measure evolving preferences can
offer more salient and effective recommendations to customers: Sequential RSs
not only benefit from a better behavioral understanding of a user's current
needs but also better predictive power. In this paper, we demonstrate and rank
the effectiveness of a sequential recommendation system by utilizing a
production dataset of over 2.7 million credit card transactions for 46K
cardholders. The method first employs an autoencoder on raw transaction data
and submits observed transaction encodings to a GRU-based sequential model. The
sequential model produces a MAP@1 metric of 47% on the out-of-sample test set,
in line with existing research. We also discuss implications for embedding
real-time predictions using the sequential RS into Nexus, a scalable,
low-latency, event-based digital experience architecture.

本文介绍了一种基于顺序模型的推荐系统，它利用原始交易数据进行预测，并在生产数据集上测试，取得了 47% 的 MAP@1 度量，证明了其效果的可靠性和实用性。同时，本文还探讨了将该系统嵌入 Nexus 数字体验架构的可能性和潜在的市场应用价值。

基于序列的下一次购买预测推荐模型

Sequential Recommendation Model for Next Purchase Prediction

Inferring user characteristics such as demographic attributes is of the
utmost importance in many user-centric applications. Demographic data is an
enabler of personalization, identity security, and other applications. Despite
that, this data is sensitive and often hard to obtain. Previous work has shown
that purchase history can be used for multi-task prediction of many demographic
fields such as gender and marital status. Here we present an embedding based
method to integrate multifaceted sequences of transaction data, together with
auxiliary relational tables, for better user modeling and demographic
prediction.

该研究提出了一种基于嵌入的方法来综合多方面的交易数据序列和辅助关系表，以实现更好的用户建模和人口统计预测。