EPG Recommendation via Semantic Graph Clique Detection
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Solution Overview
Problem
The increasing complexity of electronic program guide (EPG) information makes it time-consuming for users to find interesting TV programs using traditional set-top box interfaces, as users need to browse through multiple channels daily.
Innovation Solution
An EPG generating method and apparatus that receives a keyword group, generates a program name keyword group, constructs a semantic similarity degree matrix, builds an undirected graph, finds a maximum clique set, and determines a recommendation sequence based on a match score to recommend programs, thereby simplifying the user's search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users browse through traditional set-top box interfaces to find TV programs, then they can access comprehensive program information, but it takes a considerable amount of time to find interesting programs
Solution Approach 1:
The system automatically generates program recommendations based on user preferences and historical data, eliminating the need for users to manually browse through extensive program lists. The EPG generating apparatus autonomously processes program information, identifies relevant content, and presents personalized recommendations, thereby saving users significant time and effort in finding interesting programs.
2Adaptability or versatility
If EPG information becomes more versatile and complicated to accommodate more program categories and providers, then the system becomes more comprehensive, but it becomes more difficult for users to navigate and find programs
Solution Approach 1:
The system extracts and isolates relevant program information from the complex EPG data structure based on user preferences and search queries. By filtering and extracting only the most pertinent program details rather than presenting all available information, the system maintains comprehensive coverage while simplifying the user interface and reducing cognitive load during program selection.
Solution Approach 2:
The EPG information is segmented into manageable categories and sections based on user preferences, program types, and viewing history. This segmentation organizes the complex information into digestible portions, making it easier for users to navigate and find programs of interest without being overwhelmed by the full complexity of the EPG system.
3Measurement precision
If the system processes more program information to provide accurate recommendations, then the recommendation accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of program information by pre-processing metadata, extracting key features, and building user preference profiles in advance. This preliminary action prepares the data structure to enable fast and accurate recommendations during actual user queries, reducing the computational resources needed at the time of recommendation generation while maintaining high accuracy.
Data Source
AI summary
An apparatus and a method for generating electronic program guide (EPG) are provided. The EPG generating apparatus comprises a receiving interface and a processor. The receiving interface receives an inputting keyword group. The processor generates a program name keyword group according to the program name corresponding to a program description. The processor constructs a semantic similarity degree matrix according to the inputting keyword group and the program name keyword group, and constructs an undirected graph according to the semantic similarity degree matrix. The processor finds a maximum clique set of the undirected graph, and generates a match score according to the program description and the maximum clique set. The processor determines a recommendation sequence of the program name in the EPG according to the match score.