Cross-Genre Recommendation System Using Complementary Store Prioritization
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Solution Overview
Problem
Conventional recommendation systems fail to effectively recommend stores or content across multiple genres, such as restaurants and hotels, to users, leading to limited user satisfaction and potential loss of business opportunities by recommending competing stores.
Innovation Solution
An information processing apparatus that obtains user preference information across multiple genres and determines recommendation items by analyzing user identification, location, and relationship information to prioritize complementary store recommendations, reducing the frequency of competitive store recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendation systems recommend stores across multiple genres, then user satisfaction and business opportunities increase, but the risk of recommending competing stores increases
Solution Approach 1:
The patent segments stores by genre (restaurants, hotels, etc.) and analyzes user preferences within each genre separately. The recommendation system divides the recommendation process into genre-specific evaluations, allowing it to recommend across genres while maintaining awareness of competitive relationships within each genre category.
Solution Approach 2:
The patent introduces relationship information as an intermediary factor that mediates between user preferences and store recommendations. This relationship information indicates competitive or complementary relationships between stores, allowing the system to filter out harmful competitive recommendations while preserving beneficial cross-genre recommendations.
2Reliability
If conventional recommendation systems recommend competing stores, then user may find alternatives, but business opportunities for target stores are lost
Solution Approach 1:
The patent converts potentially harmful competitive store recommendations into beneficial complementary store recommendations by utilizing relationship information. Instead of simply excluding competing stores, the system uses relationship data to identify and prioritize complementary stores, turning a negative outcome into a positive business opportunity.
3Object-affected harmful factors
If detailed adjustments are made in conventional recommendation methods to avoid competing stores, then harmful recommendations are reduced, but operational complexity increases
Solution Approach 1:
The patent enables the recommendation system to automatically adjust its behavior by incorporating relationship information into the recommendation algorithm. The system self-regulates by using the relationship data to automatically filter competitive stores and prioritize complementary ones, eliminating the need for manual detailed adjustments while maintaining low operational complexity.
Data Source
AI summary
According to one embodiment, an information processing apparatus includes a preference information obtainer that obtains preference information indicating preferences of a target user to which recommendation is to be made, the preferences ranging over a plurality of genres. The information processing apparatus includes a recommendation item determiner that determines, using the preference information of the target user and recommendation candidate information about a plurality of recommendation candidates ranging over a plurality of genres, a recommendation item to be recommended to the target user from the plurality of recommendation candidates, the recommendation candidate information being stored in an accessible storage apparatus.