Context-Sensitive Matching Algorithm for Mobile Catalog Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge in mobile commerce is to provide an optimal way to retrieve and display product catalogue information on mobile devices, which are constrained by limited memory, processing power, small screen size, and limited user input modes, making browsing difficult for customers.
Innovation Solution
A method that generates a search key using location and contextual information to search a database for a compiled product catalogue, allowing for the retrieval and display of customized product information on mobile devices, utilizing a knowledge base and context-aware algorithms to optimize data delivery based on user preferences, device capabilities, and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional product catalogue browsing is implemented on mobile devices, then customers can access product information anytime anywhere, but the limited memory, processing power, small screen size, and limited user input modes make browsing tedious and difficult
Solution Approach 1:
The system performs preliminary actions by proactively generating and pushing product catalogue information to mobile devices before users request it. The server monitors user profiles, contextual information, and product database changes to pre-compile customized catalogues and deliver them to users' devices, eliminating the need for users to manually browse through extensive product lists.
Solution Approach 2:
The system implements self-service by automatically generating customized product catalogues based on user profiles, contextual information, and product database changes without requiring user intervention. The server autonomously compiles, formats, and pushes relevant product information to users' mobile devices, freeing users from the tedious task of manually filtering and searching through extensive product catalogues.
2Loss of information
If comprehensive product catalogue information is provided to mobile devices, then customers have access to complete product data, but the limited memory and processing power of mobile devices cannot handle large amounts of data efficiently
Solution Approach 1:
The system applies local quality by customizing product catalogue information according to each user's specific needs, preferences, and contextual information. Instead of providing uniform comprehensive data to all users, the server filters and compiles only the relevant subset of product information for each user based on their profile, purchase history, and current context, delivering tailored catalogues that are information-complete but volume-optimized for each user's device.
Solution Approach 2:
The system changes parameters by dynamically adjusting the content, format, and detail level of product catalogue information based on user profiles, contextual factors, and device capabilities. The server transforms comprehensive product database entries into customized, condensed catalogue representations that maintain essential product information while reducing data volume to suitable levels for mobile device processing and display.
3Measurement precision
If manual user input is required for product selection, then users can specify their requirements, but the limited user input modes such as small keyboard make the process time-consuming
Solution Approach 1:
The system performs preliminary action by pre-defining user requirements and preferences during initial profile setup and continuously updating them based on purchase history and behavioral data. The server maintains ready-to-use user requirement profiles that automatically guide product catalogue generation, eliminating the need for users to manually re-specify requirements each time they browse products.
Solution Approach 2:
The system implements self-service by automatically capturing and processing user requirements through minimal input channels. The server monitors user interactions, purchase history, and contextual information to autonomously update user profiles and generate customized product catalogues without requiring users to manually input detailed requirements, thus maintaining precision while minimizing time investment from users.
Data Source
AI summary
A hybrid context information matching approach may produce a customized product catalogue based on the user's context and the mobile device the user is using. A Knowledge Base (KB) and a KB manager, along with various processes perform specific collaborative tasks in order to achieve the overall goal of producing a customized product catalogue. In addition, the effort builds and/or updates the KB. Various contextual inputs are provided from both the user environment and data repositories. Hybrid matching is performed in order to determine optimal search results based on the contextual input provided.