Real-Time Recommendation System Using Dynamic User Rating Updates
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Solution Overview
Problem
Current off-line recommendation algorithms based on Hadoop Map/Reduce have low accuracy and fail to provide real-time recommendations, which are essential for applications requiring timely and relevant user engagement in search, social networking, and e-commerce.
Innovation Solution
A real-time recommendation method that acquires current user behavior data to update existing rating data and determine similarities between users and network objects, enabling immediate and relevant recommendations by processing data in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If off-line recommendation algorithm based on Hadoop Map/Reduce is used, then system complexity is reduced and ease of operation is improved, but recommendation accuracy deteriorates and response time increases
Solution Approach 1:
The patent segments the recommendation system into multiple independent modules: data acquisition module, data processing module, similarity calculation module, and recommendation generation module. Each module handles specific tasks independently, allowing the system to maintain operational simplicity while improving recommendation accuracy through specialized processing at each stage.
Solution Approach 2:
The patent implements dynamic data processing by continuously acquiring real-time user behavior data and dynamically updating user profiles and item recommendations. The system adapts to changing user preferences and behaviors in real-time, rather than relying on static offline calculations, thereby improving recommendation accuracy while maintaining system operability.
2Ease of operation
If off-line recommendation algorithm based on Hadoop Map/Reduce is used, then system complexity is reduced and ease of operation is improved, but response speed deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-processing user behavior data and pre-calculating user profiles, item features, and similarity metrics in advance. This allows the system to quickly generate recommendations by retrieving and combining pre-computed results, significantly improving response speed while maintaining operational simplicity.
Solution Approach 2:
The patent creates and maintains copies of user profiles, item features, and similarity matrices in memory or fast storage. These copies allow the recommendation system to quickly access and process data without repeatedly querying the underlying database, thereby improving response speed while keeping the system easy to operate.
3Measurement precision
If real-time data processing is implemented, then recommendation accuracy is improved and user engagement increases, but system complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple types of user behavior data (clicks, purchases, ratings, browsing) through a single integrated pipeline. The same processing modules handle both real-time and historical data, reducing system complexity while maintaining high recommendation accuracy through consistent processing logic.
Solution Approach 2:
The patent introduces intermediary components such as message queues, caching layers, and data buffering mechanisms that decouple the data acquisition, processing, and recommendation generation stages. These intermediaries simplify the overall system architecture by providing standardized interfaces and isolation between modules, reducing complexity while enabling real-time processing.
4Measurement precision
If real-time data processing is implemented, then recommendation accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements periodic action by processing user behavior data in time windows or batches rather than continuously processing every individual event. This allows the system to maintain real-time recommendation capability while reducing processing time through efficient batch operations and periodic updates of user profiles and recommendations.
Data Source
AI summary
Disclosed is a recommendation method and device. The recommendation method and device includes: acquiring current user behavior data in a currently operating network system in real time to obtain real-time user behavior data of users on the network, the current user behavior data representing currently occurring user rating activities of the users on the network; updating existing rating data of the users, the existing rating data representing user ratings of network objects made by the users during prior interactions with the network system or other network systems, the updating comprising modifying the user ratings in accordance with the real-time acquired user behavior data to obtain current user rating data of the users currently interacting with the network system; determining based upon the current user rating data, one or more similarities chosen from a group of similarities consisting of similarities between the users on the network, similarities between the network objects rated by the users, and between similarities between the users and the network objects; and sending to users recommendations for new recommended network objects to users according to one or more of the determined similarities, the recommendations being sent to users while the users are currently interacting with the network system. The present disclosure can increase recommendation precision and improve recommendation effect.