Context-Aware Recommendation Engine for Cold Start Users
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Content providers face challenges in providing relevant item recommendations to new or unidentified users, known as the 'cold start' problem, as they lack prior browsing history or identifiable user-specific preferences.
Innovation Solution
The system generates item recommendations by analyzing clickstream data from known users to create context and item matrices, calculating affinity scores, and presenting items with the highest purchase likelihood based on the unknown user's context, including time, location, and device type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If item recommendations are provided to new or unidentified users based on generalized popularity data, then the system can provide recommendations without user-specific information, but the recommendations lack personalization and relevance to individual user contexts
Solution Approach 1:
The patent introduces context information (time, location, device type) as an intermediary to bridge the gap between generalized popularity data and personalized recommendations. By using context as a mediator, the system can infer user preferences without direct user-specific browsing history, resolving the contradiction between providing personalized recommendations and lacking user-specific information
Solution Approach 2:
The system copies or replicates the browsing behavior patterns of known users who share similar context characteristics and applies them to unidentified users. By copying contextual patterns from identified users rather than relying on direct user-specific data, the system achieves personalization without requiring prior user-specific preferences
2Measurement precision
If the system collects and analyzes detailed clickstream data from known users to improve recommendation accuracy, then personalized recommendations can be provided, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the recommendation problem into distinct context dimensions (time, location, device type) and processes each dimension separately. By segmenting the complex clickstream data into manageable contextual categories, the system reduces processing complexity while maintaining recommendation accuracy through structured analysis of each segment
Solution Approach 2:
The system transforms raw clickstream data into structured context parameters with specific values (e.g., time of day, geographic location, device category). By changing the parameter representation from unstructured browsing data to structured contextual parameters, the system simplifies data processing while preserving the information needed for accurate recommendations
Data Source
AI summary
Methods and systems for recommending items to an unknown user of a website are disclosed. In one aspect, browsing activity of known users is analyzed to determine which items or categories of items are most popular in a given context (time, place, device, etc.). The known user browsing activity (clickstream data) is used to generate a multi-dimensional attribute matrix. Matrix factorization and clustering are used to generate affinity scores for items based on user context. These scores are used to generate item recommendations for an unknown user in a particular browsing context. In some embodiments, personalized item recommendations are updated based on interactions made between the unknown user and content of the website.