Media Resource Recommendation via Feature Word Weight Matrix
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Solution Overview
Problem
Current media resource recommending methods are limited in recommending diverse types of resources, often relying on manual annotation, lacking semantic information, and failing to utilize user feedback, resulting in unidirectional recommendations that primarily focus on similar media types.
Innovation Solution
A system that collects background media resource database information, extracts feature words from media resources, generates a feature word weight matrix, calculates clustering similarity, and provides a media resource recommendation list based on this similarity, incorporating user feedback to adjust weights and improve recommendation diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual annotation is used to acquire words for resource recommendation, then semantic information can be obtained, but the operational process becomes complex and time-consuming
Solution Approach 1:
The system automatically extracts words from media resource metadata (titles, descriptions, tags) without requiring manual annotation. The controller autonomously processes the metadata to identify candidate words and calculate their weights based on frequency and relevance, eliminating the need for human operators to manually annotate semantic information.
Solution Approach 2:
The manual annotation process is replaced with an automated computational system that uses text processing algorithms to extract and weight words from media resource metadata. This substitution transforms the mechanical manual work into an automated information processing task.
2Ease of operation
If traditional keyword-based recommendation is used, then the recommendation process is simple, but the system can only recommend the same type of resources and lacks diversity
Solution Approach 1:
The system assigns different weights to different words based on their local importance within the media resource metadata. By calculating word frequencies and relevance scores for each resource, the system identifies distinctive features of each media item, enabling diverse recommendations while maintaining operational simplicity through automated weight-based filtering.
Solution Approach 2:
The system dynamically adjusts word weights as parameters based on their frequency and relevance calculations. By changing the weight parameters of different words in the metadata, the system can adaptively recommend diverse resource types while keeping the overall process simple and automated.
3Device complexity
If unidirectional recommendation methods are used, then the system structure is simple, but user feedback cannot be utilized to improve recommendations
Solution Approach 1:
The system incorporates user feedback by monitoring which recommended media resources users select and watch. This feedback information is used to adjust the word weights and refine future recommendations, creating a continuous improvement loop that utilizes user behavior data to enhance recommendation quality.
4Extent of automation
If feature words are extracted from media resource metadata, then semantic information is acquired automatically, but the word collection process may be limited to morphological information and lack semantic depth
Solution Approach 1:
The system uses multiple types of media resource metadata (titles, descriptions, tags, categories) as universal sources for word extraction. By processing various metadata fields simultaneously, the system gathers comprehensive semantic information automatically, overcoming the limitation of relying on a single metadata type.
Data Source
AI summary
Provided is a display device for providing a media resource. The display device includes a communicator and a controller. The communicator collects background media resource database (DB) information. The controller extracts text information from each of media resources included in a background media resource DB, acquires one or more feature words based on the extracted text information, generates a feature word weight matrix of the background media resource DB which includes a respective weight of each acquired feature word, calculates a clustering similarity between each media resource included in the background media resource DB and a current media resource, which is being watched by a user, by using the feature word weight matrix, and provides a media resource recommendation list which includes one or more media resources based on the clustering similarity.