Model Information Groups for E-commerce Search Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current e-commerce websites face challenges in efficiently processing and returning large volumes of merchandise information, leading to resource-intensive searches that can be time-consuming and may omit relevant results, due to the vast amount of data and complexity in navigating merchandise categories.
Innovation Solution
The system records user accesses and generates journal files to determine attribute information for merchandise categories, which are then used to create model information groups that assist in filtering search results, reducing unnecessary data transmission and improving query accuracy by presenting relevant information based on user intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large amounts of merchandise information are returned to users, then search completeness is improved, but server resource consumption and network bandwidth increase
Solution Approach 1:
The system performs preliminary actions by recording user access behavior data and pre-processing it to generate model information groups before actual search queries occur. These pre-computed groups are stored and ready for rapid retrieval during search operations, enabling the system to provide comprehensive results without real-time processing of all merchandise data.
Solution Approach 2:
The system extracts relevant information by filtering and selecting only the necessary merchandise information based on user behavior patterns and query context. Instead of returning all merchandise data, the system extracts and returns only the relevant portions matched against the model information groups, reducing server resource consumption while maintaining search completeness.
2Reliability
If all merchandise information nodes are returned for a query, then information completeness is improved, but search time and data transmission volume increase
Solution Approach 1:
The system performs preliminary analysis of user access behavior to pre-compute model information groups that characterize typical user interests and search patterns. These pre-computed models are stored and can be quickly applied to new queries, enabling rapid retrieval of relevant information without time-consuming analysis during the actual search operation.
Solution Approach 2:
The model information groups serve as an intermediary between the user query and the full merchandise database. Instead of directly querying all merchandise nodes, the system uses these intermediary groups as a filtering mechanism to quickly identify and return only the relevant information, significantly reducing search time while maintaining completeness.
3Loss of energy
If the number of returned merchandise categories is limited, then server resource usage is reduced, but relevant merchandise information may be omitted
Solution Approach 1:
The system uses feedback from recorded user access behavior to continuously refine and update model information groups. This feedback mechanism allows the system to learn from actual user interactions and improve its ability to predict relevant merchandise categories, ensuring that limited returned results are highly relevant while maintaining server resource efficiency.
Solution Approach 2:
The system changes the parameters of information retrieval by using behavior-based model information groups as filtering criteria. Instead of returning a fixed number of categories or all categories, the system dynamically adjusts the retrieval parameters based on user behavior patterns, returning an optimized number of categories that balances resource usage with information relevance.
Data Source
AI summary
Managing user accesses is disclosed, including: generating journal files over a predetermined period of time, wherein a journal file includes merchandise information associated with an accessed webpage; determining, for a merchandise category, information related to an attribute based at least in part on the generated journal files; aggregating information associated with the attribute from journal files associated with the merchandise category, wherein the aggregated information is to be included in a model information group associated with the merchandise category; and returning, in a subsequent search of merchandise information corresponding to the merchandise category, search results that are based at least in part on information associated with the model information group. Conducting searches of merchandise information is disclosed, including: determining a merchandise category associated with a query based at least in part on received keywords associated with the query; retrieving a model information group corresponding to the determined merchandise category; extracting attribute information from the retrieved model information group based at least in part on a quantity associated with an attribute; and presenting the extracted attribute information.