Broadcast Recommendation Engine Using Viewer Count Learning
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Solution Overview
Problem
Existing program recommendation methods, such as initial interest registration and viewing history-based methods, fail to accurately reflect the preferences of multiple users simultaneously viewing a broadcast program, leading to suboptimal recommendations due to overemphasis on individual preferences and cumbersome user registration processes.
Innovation Solution
An information processing apparatus that integrates user preference learning by considering the number of viewers and actual viewing time, generating a user preference vector that prioritizes programs preferred by the majority of users, using a broadcast signal processing unit, viewer information obtaining unit, feature information obtaining unit, user preference information storage unit, and recommended program determining unit to recommend programs based on integrated user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the initial interest registration method is used to obtain user preference information, then the system can perform program recommendation based on registered interests, but the registration operation becomes complicated and only fixed interests at initial time can be reflected
Solution Approach 1:
The system automatically learns user preferences by analyzing viewing history and metadata without requiring manual registration. The control unit accumulates viewing history information and automatically generates user preference information, eliminating the need for complex user registration operations while maintaining high recommendation accuracy.
Solution Approach 2:
The system uses viewing history feedback to continuously update and refine user preference information. By analyzing what programs users actually watch and their viewing patterns, the system automatically adjusts preference data to improve recommendation accuracy over time without requiring re-registration.
2Ease of operation
If viewing history is used to generate user preference information, then cumbersome registration operation is eliminated, but the number of users simultaneously viewing a program is not taken into consideration leading to excessive reflection of individual preferences
Solution Approach 1:
The control unit selectively processes viewing history data based on the number of simultaneous viewers. It applies different weighting strategies: when few users are viewing, individual preferences are captured in detail; when many users are viewing, the system aggregates preferences more uniformly to avoid over-emphasizing any single user's preferences, thus improving overall accuracy.
Solution Approach 2:
The system dynamically adjusts how user preference information is generated based on the number of simultaneous viewers. The control unit modifies the learning process and preference integration method according to real-time viewing conditions, transitioning between individual-focused and group-focused preference aggregation strategies.
3Measurement precision
If detailed information is registered to improve recommendation accuracy, then the accuracy of selecting a program meeting user preference becomes higher, but the input operation complexity increases
Solution Approach 1:
The system automatically extracts and processes detailed program metadata (genre, performer, category, etc.) without requiring users to manually input this information. The control unit uses these automatically obtained detailed attributes to perform accurate program matching and recommendation, achieving high selection accuracy without increasing user input complexity.
Solution Approach 2:
The system extracts necessary detailed information from program metadata and viewing history automatically. By separating the information extraction function from user input, the system obtains detailed program attributes (genre, performer, category) needed for accurate recommendation without burdening users with complex registration tasks.
Data Source
AI summary
Provided is an information processing apparatus including: a broadcast signal processing portion to receive and reproduce a broadcast program; a viewer information obtaining portion to obtain the number of users viewing the reproduced broadcast program as a viewer number; a feature information obtaining portion to obtain feature information of broadcast programs on the air; a user preference information storage portion to store user preference information in which preferences of users are integrated; a user preference information generating portion to perform learning of the user preference information, which is stored in the user preference information storage portion, based at least on the feature information about the reproduced broadcast program and on the viewer number; and a recommended program determining portion to determine a recommended program among the broadcast programs on the air based on the user preference information.