Recommender System Using Activity Distribution for Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing recommender systems face challenges in accurately predicting and recommending future goal-oriented activities, especially when explicit user data is limited or unavailable, making it difficult to provide personalized and satisfactory recommendations.
Innovation Solution
A method that determines an activity-type distribution based on a user's personal profile and population prior information, using contextual factors like time, location, and past activities to predict future activities and recommend venues, while also considering co-presence with other users and their preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system uses user surveys or past selections to derive user information, then it can obtain some user preferences, but accurate prediction of user needs remains challenging when explicit user data is limited
Solution Approach 1:
The patent combines multiple indirect data sources including location information, calendar entries, email content, and communication patterns to infer user preferences and predict future activities. This merging of diverse data types compensates for limited explicit user feedback and improves prediction accuracy.
Solution Approach 2:
The system performs preliminary analysis of user patterns and preferences by continuously processing available data in the background. This preliminary action enables the system to make accurate predictions even when explicit user data is limited, as the predictive model is continuously refined with indirect observations.
2Measurement precision
If the system collects and processes multiple sources of user information to improve recommendation accuracy, then prediction quality improves, but system complexity increases
Solution Approach 1:
The patent segments the complex recommendation system into distinct functional modules: location analysis module, calendar processing module, email analysis module, and pattern recognition module. Each module handles specific data types independently, making the overall system more manageable and maintainable while achieving high prediction accuracy.
Solution Approach 2:
The system introduces intermediary processing layers that transform raw data from multiple sources into structured user profiles and activity patterns. These intermediaries simplify the complexity by creating standardized representations of user preferences that can be easily processed by the recommendation engine.
3Adaptability or versatility
If the system analyzes detailed user context including location, time, and content to predict future activities, then recommendation personalization improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing and indexing of user data as it becomes available, creating pre-computed user profiles and activity patterns. This preliminary action reduces the computational burden during real-time recommendation generation, enabling fast personalized recommendations even with detailed context analysis.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as analysis depth, data sampling rate, and model complexity based on context and available resources. This allows the system to maintain high personalization quality while adapting processing time to meet real-time requirements.
Data Source
AI summary
One embodiment of the present invention provides a method for recommending activities to a user. During operation, the system determines an activity-type distribution based on the user's personal profile and/or population prior information, thereby facilitating prediction of future activities for the user. The system further searches for and receives one or more activities based on the activity-type distribution. The system then scores each received activity and recommends a number of activities to be performed by the user in the future and a number of corresponding venues, based on the activity-type distribution and the weight distribution.