Contextual Model for Collaborative Filtering Recommendations
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Solution Overview
Problem
Current collaborative-filtering based recommendation systems do not effectively utilize all available online information, leading to a need for a more comprehensive approach that considers both explicit and implicit user ratings and item information to provide personalized recommendations.
Innovation Solution
A network recommendation system that uses preference information from multiple users to create contextual and content models, incorporating explicit and implicit ratings, and item metadata to recommend items by calculating similarity measurements and optimizing for specific objective functions like click-through-rate or conversion rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If collaborative filtering methods use only traditional explicit ratings to generate recommendations, then the system is simple to implement, but the recommendation accuracy and comprehensiveness are limited
Solution Approach 1:
The patent combines multiple data sources including explicit ratings, implicit ratings, and item information into a unified collaborative filtering model. This merging of diverse data types enriches the recommendation basis and improves accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The recommendation system uses a composite approach by integrating different types of user feedback (explicit and implicit ratings) along with item metadata to create a comprehensive recommendation model. This composite data structure enables more accurate recommendations by leveraging multiple information dimensions simultaneously.
2Loss of information
If the recommendation system considers all available online information including implicit ratings and item metadata, then the comprehensiveness of recommendations improves, but the computational complexity increases
Solution Approach 1:
The patent segments the comprehensive data into distinct components: explicit ratings, implicit ratings, and item information. Each component is processed separately through specific modules before being integrated into the final recommendation model. This segmentation manages computational complexity by breaking down the processing of large-scale data into manageable segments.
Solution Approach 2:
The system employs an intermediary similarity model that processes and transforms raw data from multiple sources into meaningful similarity measurements. This intermediary layer handles the computational complexity by mediating between raw data inputs and final recommendations, making the system more manageable while充分利用 all available information.
3Adaptability or versatility
If the system collects and processes diverse user behaviors and item attributes, then the personalization of recommendations improves, but the data processing time increases
Solution Approach 1:
The patent implements preliminary computation of similarity measurements between items and users during off-peak periods or in advance. These pre-computed similarities are stored and reused during recommendation generation, reducing real-time processing requirements while maintaining high personalization capability across diverse user preferences and item attributes.
Data Source
AI summary
Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.