Low Conversion Rates
Traditional marketing strategies focus on categories of consumers rather than being consumer-specific. E-commerce platforms recommend the same products to all consumers, leading to low conversion rates.
Poor User Experience
Consumers accessing online stores are presented with the same set of products, regardless of personal preferences.
Ineffective marketing strategies lead to low conversion rates. As a result, there is a continuous increase in unsold inventory.
E-commerce platforms need to invest heavily in recommendation engines that help implement precision marketing.
This solution helps enterprises build precise user models and provides consumer-specific recommendations based on in-depth analysis of consumer behavior and transaction history.
The Computing Engine uses machine learning and deep learning algorithms to meet complex recommendation needs.
The Recommendation Engine provides a complete framework (from data input and computation, to data output), and allows customization of algorithms to meet specific business needs.
The User Profile component leverages consumer favorites, behavior, transaction history, website and mobile app usage, location, logs, and other data to generate 360-degree user models.
API provides external users with RESTful APIs to invoke services, easily interconnecting recommendation systems with existing business systems.
MapReduce ServiceMapReduce Service (MRS) provides enterprise-level big data clusters on the cloud. Tenants can fully control clusters and easily run big data components such as Hadoop, Spark, HBase, Kafka, and Storm.
Machine Learning ServiceMachine Learning Service (MLS) helps you quickly find data patterns to construct prediction models through machine learning technologies and deploy these models as prediction and analysis solutions.
Deep Learning ServiceDeep Learning Service (DLS) is powered on the high-performance computing capabilities of HUAWEI CLOUD. With built-in optimized network models, DLS allows you to implement model training, evaluation, and inference with the flexibility of on-demand scheduling.