Automated Recommendation System Bandwidth Optimization
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Solution Overview
Problem
Existing systems face inefficiencies and increased bandwidth usage when retrieving search results, leading to a bottleneck in providing recommendations to users, which can hinder the user experience on eCommerce websites.
Innovation Solution
A system comprising processing modules and storage modules that receive search queries, analyze user interactions, and determine purchase probabilities to rank and display items, while clustering items and user actions to provide personalized recommendations, thereby reducing system resource demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system retrieves search results using traditional methods, then search functionality is provided, but system bandwidth becomes slow and bottlenecked
Solution Approach 1:
The system performs preliminary actions by generating automated recommendations and pre-fetching item information before users actually search for or request it. The recommendation engine proactively identifies items users are likely to search for and prepares this information in advance, reducing the need for users to conduct multiple searches and thereby decreasing overall system bandwidth consumption while maintaining or improving search efficiency
Solution Approach 2:
The system implements self-service by using collaborative filtering and item similarity algorithms to automatically generate recommendations without requiring users to manually search through extensive catalogs. The system serves itself by identifying patterns in user behavior and autonomously curating personalized item lists, which reduces the burden on system bandwidth compared to traditional on-demand search retrieval
2Ease of operation
If users conduct numerous user actions to find items, then users can locate desired items, but system efficiency decreases due to increased item information retrieval
Solution Approach 1:
The system introduces an intermediary recommendation layer between the user and the item database. Instead of users directly querying the database through multiple search actions, the recommendation engine acts as an intermediary that filters and pre-selects relevant items based on collaborative filtering and item similarity. This intermediary layer reduces the number of direct database queries needed while improving ease of item discovery
Solution Approach 2:
The system performs preliminary filtering and ranking of items based on user profiles and item similarities before users request them. By pre-processing and organizing item information according to likely user interests, the system reduces the number of user actions needed to find items while maintaining system efficiency, as the heavy lifting of information organization is done in advance rather than in response to each user query
Data Source
AI summary
In some embodiments, the method can comprise receiving one or more clicks on one or more items by a user during a browse session, measuring a distance between the one or more items, and determining a relationship between the one or more items based at least in part on the distance. In various embodiments, the method can further comprise clustering the one or more items based at least in part on the relationship into one or more clusters and presenting to the user a recommendation. In a number of embodiments, the recommendation can comprise at least one of one or more search terms related to at least one cluster of the one or more clusters or a set of items related to the at least one cluster of the one or more clusters, the set of items comprising at least a portion of the one or more items. Other embodiments of related methods and systems are also provided.