Keyword-Based Item Recommendations for New Websites
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Solution Overview
Problem
New websites lack user preference data, hindering their ability to provide personalized content or product suggestions to visitors due to limited view histories and purchase histories.
Innovation Solution
A system that extracts keywords from user behavioral data across multiple domains, forming keyword-user associations, which are then ranked and used to generate item recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a website is new with no user history data, then the website can operate without data collection infrastructure, but the website cannot provide personalized recommendations to users
Solution Approach 1:
The system performs preliminary actions by pre-defining keyword templates and association structures before user interactions occur. Keyword templates are prepared in advance with predefined patterns that can automatically match user-generated content, enabling the system to begin forming associations immediately upon receiving user input without requiring extensive historical data
Solution Approach 2:
Keywords serve as intermediaries between user behavior and recommendation generation. Instead of directly analyzing complex user behavior patterns, the system extracts keywords from user actions and uses these keywords as mediators to identify associations and generate recommendations, simplifying the personalization process for new websites
2Measurement precision
If the system extracts and processes keywords from user behavioral data across multiple domains, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system extracts only the essential keyword elements from user behavioral data across multiple domains, separating these keywords from the complex raw data. This extraction process isolates the critical preference indicators while discarding unnecessary data complexity, enabling accurate preference detection without processing the entire raw dataset
Solution Approach 2:
The keyword association system serves multiple functions simultaneously: it detects user preferences, identifies item associations, generates recommendations, and adapts to different domains all through a single unified keyword-based framework. This multi-functionality reduces the need for separate complex systems for each task
3Reliability
If the system gathers data from multiple domains to extract keywords, then the quality of recommendations improves, but the amount of data to be processed increases
Solution Approach 1:
The system extracts only the relevant keyword information from multi-domain data sources, separating essential preference indicators from the large volume of raw behavioral data. This selective extraction maintains recommendation quality by focusing on key signals while reducing the amount of data that needs to be stored and processed
Solution Approach 2:
The system applies different keyword extraction and association rules tailored to specific domains while maintaining a unified recommendation framework. Each domain's data is processed with domain-appropriate keyword patterns, ensuring high-quality recommendations for each domain without requiring a single complex processing approach for all data
Data Source
AI summary
Disclosed are various embodiments for generating recommendations based at least in part on keywords associated with users. In some embodiments, among others, a system includes at least one computing device and a recommendation generator executable in the at least one computing device. The recommendation generator comprises logic that generates a plurality of pools of keywords based at least in part on a plurality of behavioral histories. Each pool corresponds to a behavioral history of a user across a plurality of domains. The recommendation generator also comprises logic that clusters at least a number of the keywords in a cluster across at least two pools including the same keyword and logic that recommends an item based at least in part on the cluster of keywords.