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GrandhooReclogic Recommended Solution
Overview
In the information technology competitive market, such as retail e-commerce, finance, social and other fields, in order to be able to improve the user experience, to understand the user's real needs and provide a high degree of accuracy of the product recommendation is almost all enterprises need. In order to achieve high accuracy recommendation, it needs to go through product research, component professional technical team, R&D, debugging and maintenance, and many other huge challenges. Hydroglass provides one-stop recommendation solutions in various fields, helping enterprises to deploy quickly and adapt to the access at low cost. Combining traditional machine learning models and original rule discovery theory, it predicts users' product preferences, thus increasing the click rate and conversion rate of users' products.
Challenges
In the recommendation field of user-item matching, 7 points depend on the accuracy of recommendation, and 3 points depend on the experience of operation and industry experts. If the recommended goods are not accurate enough, the operation cost and customer acquisition cost will increase, followed by the user's interest, click rate, conversion rate and retention rate will be reduced.
High customer acquisition costs and difficulty in targeting audiences
Selecting the target audience relies on the subjective experience of the marketing team, and the lack of consistent audience targeting may lead to inaccurate marketing results and the inability to achieve truly personalised recommendations, which in turn leads to an increase in customer acquisition costs. At the same time, due to the lack of analysis of a large amount of real-time data, relying only on historical data and limited market research. It is impossible to comprehensively and accurately understand the real-time needs and behaviours of the target audience, leading to a lag in marketing strategies.
Low conversion rate, difficult to promote
The core reason for the low conversion rate is that people and goods matching is not accurate enough, and thousands of people behind the recommendation of feature engineering and recommendation systems and other related technologies require a certain amount of R & D staff investment, due to the majority of customers in this part of the investment is relatively lack of, resulting in the final results of precision marketing are not ideal. Enhancing the accuracy of digital marketing and realising "intelligence" are the challenges faced by the whole industry.
Retention is difficult to improve
Enterprises in the process of marketing, often found that the users who came before rarely come back afterward, or only in the event of activities, there will be a sizable user traffic, user stickiness is weak, and few repeat customers.
Architecture
Benefits
Experts build features + automatically mine features
Support domain experts to build their deep domain knowledge into specific feature representations that can be organically incorporated into logic rules and machine learning models to form a comprehensive knowledge fusion mechanism. On this basis, new features are mined through graph-association computation, and the system is able to capture the evolution of relationships between entities in real time, enabling the model to understand key features in the domain more comprehensively, thereby reducing customer acquisition costs.
Improve recommendation accuracy
Combine traditional logical reasoning with modern machine learning in order to improve the reasoning and decision-making ability of the recommendation system. The core advantage is to make full use of the accuracy and interpretability of logic rules, while combining the ability of machine learning to learn patterns and laws from a large amount of data. Tests have shown that the logic rule algorithm improves accuracy by 30% on top of machine learning recommendation sorting.
Support the adaptation of data sources in different fields
It supports adapting data from different domains, including e-commerce products, literature and thesis, media content and other domains; there are many ways to dock the data sources to meet the docking requirements of enterprises. Through flexible model design and training methods, it can be optimised for specific domain data characteristics. It has the characteristics of domain adaptability, data preprocessing and feature engineering, and sustainable optimisation.