Mixed-Model Leisure Recommender for Multi-Category Queries
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Solution Overview
Problem
Conventional recommender systems are limited in generating recommendations across multiple categories of items, such as books and movies, and cannot handle queries for leisure activities that encompass a broad range of activities like watching a movie, dining, or visiting a museum simultaneously.
Innovation Solution
A recommender system incorporating four models: collaborative filtering, soft query, content preference, and distance models to compute composite scores for leisure activities, allowing users to receive recommendations across multiple categories by calculating weighted averages of these scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional recommender system is used, then it can generate recommendations for single category items, but it cannot handle queries for leisure activities across multiple categories simultaneously
Solution Approach 1:
The system divides the recommendation task into four independent scoring models (collaborative filtering, soft query, content preference, and distance models), each handling specific aspects of leisure activity recommendation. This segmentation allows the system to address multiple activity categories effectively while maintaining manageable complexity through modular design.
Solution Approach 2:
The recommender system is designed as a universal platform that can handle multiple types of leisure activities (dining, entertainment, shopping, etc.) through a unified framework. The four scoring models work together to provide comprehensive recommendations across diverse activity categories, making the system multi-functional and adaptable to various user needs.
2Measurement precision
If multiple scoring models are integrated to provide comprehensive recommendations, then recommendation accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the recommendation evaluation into four distinct scoring components (collaborative filtering score, soft query score, content preference score, and distance score). Each model focuses on specific recommendation aspects, improving overall accuracy while allowing independent optimization and management of computational resources for each component.
Solution Approach 2:
The system employs parameter weighting to balance the contributions of different scoring models. By adjusting the weight parameters (w1, w2, w3, w4) in the composite score calculation, the system can optimize recommendation accuracy for different scenarios while controlling computational complexity through parameter tuning rather than structural changes.
Data Source
AI summary
One embodiment of the present invention provides a method for recommending leisure activities to a user. During operation, the system receives at least one query for leisure activities. The system then determines a collaborative filtering score of a candidate activity based on a collaborative filtering model, a soft query score for the candidate activity based on a soft query model, a content preference score for the candidate activity based on a content preference model and the user's past behavior, and a distance score for the candidate activity based on a distance model. Next, the system generates a composite score for the candidate activity by calculating a weighted average of the collaborative filtering score, the soft query score, the content preference score, and the distance score. The system further returns a recommendation list containing the activities with the highest composite scores.