Radio Broadcast Recommendations Using Time and Location Context
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Solution Overview
Problem
Users face difficulty in finding and accessing radio stations playing their preferred music genres at specific times due to the numerous available programs and varying radio stations across different geographic locations.
Innovation Solution
A system and method that analyze user listening activities to recommend music and radio stations based on detected listening habits, program information, and geographic location, using processors to determine receivable radio stations, identify audio content metadata, and generate time-specific recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually search through numerous radio stations and programs to find preferred content, then they can access their desired music genre, but this process consumes significant time and effort
Solution Approach 1:
The system performs preliminary actions by automatically collecting radio station information, program schedules, and user listening preferences in advance. It pre-processes this data to identify and recommend suitable radio stations before users need them, eliminating the manual search process and reducing time loss.
Solution Approach 2:
The system enables self-service by automatically analyzing user listening habits, detecting preferred music genres, and generating personalized radio station recommendations without requiring user intervention. The system serves itself by continuously monitoring and adapting to user preferences, freeing users from manual searching.
2Measurement precision
If the system collects and analyzes extensive radio station and program information across multiple geographic locations, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of radio recommendation into distinct modules: geographic location detection, receivable station identification, program information collection, user preference analysis, and recommendation generation. Each module handles a specific aspect independently, reducing overall system complexity while maintaining high recommendation accuracy through coordinated operation of specialized components.
3Adaptability or versatility
If the system provides personalized recommendations based on detailed user listening history, then recommendation relevance improves, but data processing requirements increase
Solution Approach 1:
The system applies partial action by focusing data processing efforts on the most relevant aspects of user listening history rather than analyzing every single data point. It identifies key patterns in user preferences and processes only the essential information needed for accurate recommendations, reducing energy consumption while maintaining personalization quality.
Data Source
AI summary
Generally disclosed herein is a mechanism to provide music and radio station recommendations based on observing radio and music listening activities of a user over time. In some examples, information about the radio stations receivable by a user device and programs broadcast by such radio stations are collected over time. Recommendations for different music and/or radio stations may be generated based on a single song or radio program that the user listens to. The recommended music and/or radio stations may be updated based on program information related to the radio stations receivable by the user device at a specific time at a specific geographic location.