Collaborative Filtering for Product Recommendation

  1. Marketing scenarios, such as product recommendations, user profiling, and precise marketing.
  2. Finance scenarios, such as bank loan prediction, financial risk control, stock trend prediction, and gold price prediction.
  3. Data mining in social networking sites (SNS), such as Twitter opinion leader (influential) analysis and social relationship chain analysis.
  4. Text scenarios, such as new categorization, keyword extraction, document summarization, and text analysis.
  5. Unstructured data processing scenarios, such as image categorization and optical character recognition (OCR).
  6. Other prediction scenarios, such as rain prediction and soccer game prediction.
  1. Supervised learning. Each sample has an expected value in supervised learning. Supervised learning is a machine learning task that maps the input (feature vectors) to expected values using modeling. Supervised learning is used in regression and classification.
  2. Unsupervised learning. Unsupervised learning is a machine learning task that draws potential inferences from samples without expected values, such as some simple aggregations.
  3. Reinforcement learning. Reinforcement learning is about how agents take actions to interact with an environment to maximize the cumulative reward. Examples of reinforcement learning include AlphaGo Zero and autonomous driving.

Using Alibaba Cloud Machine Learning Platform for AI


Data Exploring Procedure

  1. Generate a product recommendation list based on correlation rules.
  2. Actual shopping behavior after July.
  3. Number of recommended products and hit rate.

1. Generate a Recommendation List

2. Make Recommendations

3. Display Results Statistics


  1. This experiment only introduces how to use collaborative filtering to make recommendations. Key components for shopping behavior-based recommendations, such as time series, are not processed in this experiment. The validation of shopping behavior is essential. Using data from shopping behavior collected across several months may not deliver the expected results.
  2. This experiment only focuses on the correlation between products. The attributes of recommended products, such as the purchase frequency of products, are not concerned. For example, mobile phones are products with a low purchase frequency. if customer A has purchased a mobile phone last month, customer A may not purchase another mobile phone this month.
  3. To increase the accuracy of the prediction, machine learning algorithms must be used to train models. The method of product correlation-based recommendations should only be used to supplement other methods.




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