Digital Recommendation System with Pre-selected Auxiliary Items
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Solution Overview
Problem
Current online recommendation platforms face computational demands that lead to latency issues when determining new digital item recommendations in real-time, affecting user satisfaction due to the need for immediate updates based on user interactions.
Innovation Solution
The method involves pre-determining smaller sets of digital item recommendations, including a subset of core items and auxiliary items, which are pre-selected based on past user interactions, allowing for efficient generation and presentation without significant computational delays. When a user interacts with a core item, the auxiliary item is provided, and a new set is generated without real-time re-determination, reducing server latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If new recommended digital items are determined online in real-time based on user interactions, then the recommendations better accommodate user interests, but the server latency increases and user satisfaction decreases
Solution Approach 1:
The system pre-determines a set of candidate recommended digital items before user interactions occur. These candidate items are prepared in advance based on historical data and item characteristics, so when a user interacts with the platform, the system can quickly select from the pre-prepared candidates rather than computing recommendations in real-time, thus reducing server latency while maintaining recommendation quality
Solution Approach 2:
The recommendation process is divided into two stages: an offline stage where candidate items are pre-determined and prepared, and an online stage where the system quickly selects from the pre-prepared candidates based on current user interactions. This segmentation allows computationally intensive operations to be performed in advance while keeping real-time response fast
2Quantity of substance
If a large number of digital items are hosted on the platform, then the platform offers more variety to users, but the computational resources required to process recommendations increase
Solution Approach 1:
The system extracts a smaller subset of candidate recommended items from the large pool of all digital items hosted on the platform. Instead of processing and evaluating all available items in real-time, the system pre-selects a manageable number of candidate items that are most likely to be recommended, thereby significantly reducing computational resource requirements while still maintaining access to the full catalog
Solution Approach 2:
The system performs preliminary filtering and selection of candidate items in advance, creating a reduced set of potential recommendations from the full catalog. This pre-processing step reduces the computational burden during online operations, allowing the system to handle large numbers of digital items without proportionally increasing real-time computational resource usage
Data Source
AI summary
A method and a server for generating digital item recommendations for a given user of an online recommendation platform are provided. The method comprises: generating a set of digital items to be recommended to the given user, the set of digital items including: a sub-set of core digital items, for a given core digital item of the sub-set of core digital items, the set of digital items further including: at least one auxiliary digital item to be provided to the given user in response to a respective user interaction of the given user with the given core digital item during appreciation thereof, the at least one auxiliary digital item having been determined based at least on an assumption that the given user has executed the respective user interaction with the given core digital item; and transmitting the set of digital items to an electronic device associated with the given user.