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Personalized System Recommendation Plan

Challenges

(1) The scale of e-commerce is constantly expanding and the competition pattern is becoming increasingly complex;

(2) The number and variety of commodities are growing rapidly;

(3) Information overload leads to continuous loss of consumers;

(4) Quickly capture user needs and accurately launch related strategies;

Solving Problems

Accurately match users' interests and concerns in the information explosion, and continuously improve user stickiness.

  • Information Overload

    Avoid browsing meaningless information


  • User Loss

    Prevent continued user loss and recover
    lost users


  • Precise Personalization

    Massive data mining
    (for recommendations)


Architecture Design

Program Value

(1) Analyze and process massive data, quickly process new data and real-time interaction.

(2) Flexible addition and iteration of various recommendation algorithms.

(3) Standard SQL, no learning curve, supporting standard ANSI SQL.

(4) Low latency response to high QPS recommendation requests.

(5) Compatible with multiple industries and fields.

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