Machine Learning Item Recommendation Ranking
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Solution Overview
Problem
Existing digital advertisement recommendation systems often provide customers with irrelevant item advertisements, leading to lost sales and decreased customer engagement, as they fail to accurately predict customer interest based on historical interaction and transaction data.
Innovation Solution
A computing device generates feature data from historical website interaction and transaction data, trains machine learning models to predict item purchase likelihood, and ranks recommended items based on this prediction data, filtering them by categorical attributes to ensure relevance and increase sales conversions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional advertisement recommendation systems are used, then advertisements can be displayed to customers, but the advertisements are often irrelevant to customer interests leading to lost sales
Solution Approach 1:
The system transforms advertisement recommendation from static category-based matching to dynamic probability-based ranking by changing the parameters used for evaluation. Machine learning models calculate purchase probability scores for each recommended item based on historical interaction data, transforming the recommendation criterion from simple categorical matching to probabilistic prediction, thereby improving both relevance and conversion rates
Solution Approach 2:
The system implements feedback loops where customer interaction data (views, clicks, purchases) is continuously collected and used to retrain machine learning models. This feedback mechanism allows the system to learn from past performance and continuously improve recommendation accuracy, ensuring advertisements remain relevant while increasing sales conversions over time
2Measurement precision
If machine learning models are trained on historical data to predict purchase likelihood, then advertisement relevance improves, but system complexity increases
Solution Approach 1:
The system segments the recommendation problem into distinct components: feature extraction from historical data, model training phases, prediction generation, and result ranking. Each component is handled by specialized modules, making the complex machine learning system more manageable and maintainable while achieving high prediction accuracy through focused optimization of each segment
Solution Approach 2:
The system introduces intermediary elements such as feature vectors that transform raw historical interaction data into standardized inputs for machine learning models. These intermediaries bridge the gap between complex data sources and model requirements, enabling accurate predictions while maintaining system modularity and reducing overall complexity
3Productivity
If recommended items are ranked based on prediction data, then sales conversions increase, but processing time for generating recommendations increases
Solution Approach 1:
The system performs preliminary actions by pre-processing historical interaction data into feature vectors and pre-training machine learning models offline. This allows the runtime recommendation generation to focus only on executing predictions and ranking items based on pre-computed models, significantly reducing real-time processing time while maintaining high conversion rates through accurate pre-trained predictions
Data Source
AI summary
This application relates to apparatus and methods for automatically determining and providing recommendations of items to advertise customers. In some examples, a computing device generates feature data based on historical website interaction data, historical transaction data, and item categorical data. The computing device trains each of a plurality of machine learning models based on the generated feature data. The computing device may then receive a plurality of recommended items to advertise in association with an anchor item. The computing device may execute the trained machine learning process to generate prediction data associated with a future time period. The prediction data may identify a number of times each recommended item may be purchased during the future time period. The computing device may then rank the plurality of recommended items based on the prediction data. In some examples, the computing device filters the plurality of recommended items based on item categories.