Webpage Personalization via Pre-computed User Segments
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Solution Overview
Problem
System bandwidth can become slow or bottlenecked when retrieving search results, leading to inefficiencies and poor user experience on eCommerce websites due to increased demand on system resources from numerous user actions and item information retrieval.
Innovation Solution
A system comprising processing modules and storage modules that receive search queries, determine purchase probabilities, rank items, and facilitate their display, while also clustering items based on user interactions to provide personalized recommendations and update user profiles, thereby optimizing webpage updates and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system retrieves search results and updates webpages in response to numerous user actions, then the user experience is improved with more relevant item information, but the system bandwidth becomes slow and bottlenecked due to increased demand on system resources
Solution Approach 1:
The system pre-generates personalized item sets for different user segments based on their profiles and behaviors before users actually search. When a user performs a search, the system can quickly retrieve pre-prepared personalized recommendations rather than generating them in real-time, thus improving user experience without overloading system bandwidth during peak usage.
Solution Approach 2:
The system segments users into different user segments based on their profiles, behaviors, and preferences. By creating personalized item sets for each segment rather than treating all users uniformly, the system reduces the overall computational burden while still providing customized recommendations to each user, thereby maintaining ease of operation without proportionally increasing system resource demands.
2Loss of information
If the system retrieves extensive item information from the database in response to user actions, then more comprehensive search results are provided, but the efficiency of the system decreases due to increased resource consumption
Solution Approach 1:
The system creates personalized item sets tailored to each user segment's specific preferences, behaviors, and profile characteristics. Instead of retrieving and processing all possible item information for every user, the system selectively retrieves and processes only the item information relevant to each user segment, thus maintaining information completeness for personalized recommendations while significantly reducing overall system resource consumption and improving efficiency.
3Adaptability or versatility
If the system provides personalized recommendations based on user behavior analysis, then user experience is enhanced with more relevant items, but the complexity of the system increases due to user profiling and clustering operations
Solution Approach 1:
The system performs user profiling, behavior analysis, and clustering operations in advance to create user segments and their corresponding personalized item sets before users actually interact with the search system. This preliminary processing allows the system to provide highly personalized recommendations without performing complex analysis in real-time during user interactions, thus enhancing adaptability and personalization capability while managing system complexity through batch processing rather than real-time computation.
Data Source
AI summary
In many embodiments, the method can comprise storing one or more user actions of a user of one or more users in a database, sorting the one or more user actions into one or more user action types, and extracting one or more correlated signals related to the one or more user actions of the user of the one or more users based at least in part on the one or more user action types to determine one or more independent signals related to the one or more user actions of the user of the one or more users. In some embodiments, the method can further comprise averaging the one or more independent signals related to the one or more user actions of the user of the one or more users to determine a personalization score related to the user of the one or more users and updating a webpage for the user of the one or more users based at least in part on the personalization score. Other embodiments of related methods and systems are also provided.