Store Recommendation Using Visit Familiarity Filtering
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Solution Overview
Problem
Existing store recommendation systems fail to consider the user's familiarity with the store, leading to inefficient and unnecessary recommendations.
Innovation Solution
A store derivation device that includes a visit history storage unit, calculation unit, and store derivation unit to determine a user's degree of familiarity with a store, thereby recommending appropriate visit candidates based on this familiarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If store recommendations are made without considering user familiarity, then recommendation coverage is improved, but recommendation efficiency deteriorates due to unnecessary recommendations
Solution Approach 1:
The system performs preliminary calculation of the user's degree of familiarity with the store before generating recommendations. The calculation unit computes familiarity metrics based on visit history data stored in the visit history storage unit, and the store derivation unit uses this pre-calculated familiarity information to filter and select appropriate recommendation targets, avoiding unnecessary recommendations to users who are already familiar with stores.
2Measurement precision
If user action history is analyzed to calculate familiarity, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the recommendation process into distinct functional units: a visit history storage unit that stores user action history, a calculation unit that calculates degree of familiarity based on this history, and a store derivation unit that derives recommendation targets. This segmentation allows each unit to perform its specific function independently, managing complexity through modular architecture while enabling precise measurement of user familiarity.
Data Source
AI summary
An object is to provide a store derivation device that derives an appropriate recommendation target to enable efficient recommendation. A recommendation system 100 that functions as a store derivation device of the present disclosure includes a visit history storage unit 106 configured to store visit history information of a store as an action history of a user. Further, a store acquisition unit 101 acquires a familiar store (degree of familiarity) of the user with respect to the store on the basis of the visit history information. The store acquisition unit 101 derives a visit candidate store fx1 that the user is recommended to visit, on the basis of the familiar store (degree of familiarity).