Content-Based Media Recommendation via Synopsis Vector Clustering
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Solution Overview
Problem
Existing online e-commerce systems face challenges in providing accurate product recommendations for media products with scarce purchase data, such as newly released movies or low-selling books, as collaborative filtering methods require sufficient user data and perform poorly with cross-category recommendations.
Innovation Solution
A content analysis engine that performs vectorization of media product synopses using word embedding techniques to generate media vectors, allowing for content-based analysis and recommendations by comparing seed media products to a pool of media vectors, and filtering recommendations through clustering to avoid similar products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If collaborative filtering methods are used for product recommendations, then recommendations can be generated based on user purchase data, but the system fails when there is insufficient data about the product, target user, or other users
Solution Approach 1:
The patent introduces content features (synopsis, genre, director, actors) as an intermediary between user preferences and product recommendations. Instead of directly comparing user purchase histories, the system uses content-based features to bridge the gap, allowing recommendations to be generated even when collaborative filtering data is insufficient. This mediator enables the system to function reliably in data-scarce scenarios.
Solution Approach 2:
The system changes the parameters used for recommendation from purely collaborative filtering metrics (user-item interaction data) to include content-based parameters (synopsis vectors, genre tags, metadata). By transforming the recommendation problem into a content-based similarity problem, the system can generate reliable recommendations without requiring large quantities of user purchase data.
2Adaptability or versatility
If collaborative filtering is used for recommendations, then cross-category recommendations are attempted, but the system performs poorly with cross-category recommendations
Solution Approach 1:
The patent creates a universal recommendation system that works across different product categories by using content-based features rather than category-specific purchase patterns. The content analysis engine processes synopses and metadata from various media types (books, movies, music) using the same vectorization approach, enabling the system to provide reliable cross-category recommendations without being constrained by category boundaries.
Solution Approach 2:
The system transitions from a one-dimensional collaborative filtering approach (based on user-item interaction matrices) to a multi-dimensional content-based approach. By representing products in a high-dimensional vector space based on their content features, the system can identify similarities across different categories that would be invisible in traditional collaborative filtering, thereby improving cross-category recommendation reliability.
3Reliability
If content-based analysis using word embedding is implemented, then recommendations can be generated for products with limited data, but the system complexity increases due to vectorization and clustering operations
Solution Approach 1:
The system performs preliminary vectorization of product synopses and content features in advance, storing these vectors for later recommendation queries. By pre-processing the content data and creating the vector representations before they are needed for recommendations, the system reduces the computational complexity during actual recommendation generation, making the content-based approach more efficient and manageable despite the inherent complexity of word embedding operations.
Data Source
AI summary
A media analysis system includes one or more hardware processors, a memory storing synopses associated with catalog books, and a content analysis engine. The content analysis engine generates a media vector for each catalog book based on the associated synopsis by generating a word vector for each word in the synopsis, combining the plurality of word vectors into a mean vector for the catalog book, and storing the mean vector as the media vector associated with the catalog book. The content analysis engine also identifies a target book associated with a seed media vector, determines R nearest neighbors for the target book from the plurality of catalog books based on (1) the seed media vector and (2) the media vectors associated with the plurality of catalog books, clusters the R nearest neighbors into K clusters, and selects catalog books for recommendation to a user based on the K clusters.