Probabilistic User Model for Dynamic Recommendation Scoring
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Solution Overview
Problem
Conventional computer-based systems for providing purchase recommendations on websites rely on incomplete customer-profile data and predetermined selling point messages, leading to ineffective recommendation selection and presentation, often wasting recommendations on already familiar items or failing to customize messages for users.
Innovation Solution
A system and method that uses a probabilistic framework to assess the incremental margin of recommendations by considering historical associations, site layout, product complexity, user behavior, and demographics, optimizing the selection and scoring of recommendations and selling point messages to increase purchase likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional recommendation systems use predetermined selling point messages for all users, then implementation complexity is reduced, but recommendation effectiveness and user customization are worsened
Solution Approach 1:
The patent implements dynamic selling point messages that adapt to individual user characteristics, purchase history, and contextual factors. The system generates customized recommendations in real-time based on probabilistic user models, transforming static predetermined messages into dynamic personalized content that evolves with user behavior and preferences
Solution Approach 2:
The system changes multiple parameters simultaneously including user profile attributes, purchase probability scores, margin calculations, and contextual factors to generate optimized recommendations. By adjusting these parameters based on probabilistic modeling, the system achieves high customization without requiring complete redesign of the recommendation framework
2Ease of manufacture
If recommendation systems rely on customer-input profile information, then data collection is simplified, but recommendation accuracy and completeness are worsened due to incomplete customer data
Solution Approach 1:
The patent introduces probabilistic user models as intermediary representations that bridge the gap between incomplete customer input data and accurate recommendation needs. These models infer missing profile attributes and preferences through probabilistic reasoning, acting as mediators that translate limited explicit data into comprehensive user understanding without requiring direct customer input for all attributes
Solution Approach 2:
The system continuously refines customer profiles through feedback loops that incorporate purchase history, browsing behavior, and recommendation responses. By using historical transaction data and observed user interactions to update probabilistic models, the system progressively improves profile accuracy over time, transforming initial incomplete data into increasingly precise customer representations
3Ease of operation
If conventional systems use predetermined selling point messages, then message selection is simplified, but purchase conversion is worsened due to lack of message optimization for individual users
Solution Approach 1:
The patent performs preliminary optimization of selling point messages by pre-calculating the most effective messages for different user segments and contexts using probabilistic modeling. Before presenting recommendations to users, the system pre-selects and optimizes messages based on anticipated user responses and purchase probabilities, so that when recommendations are delivered, they are already optimized for maximum conversion effectiveness
Solution Approach 2:
The system implements dynamic message selection that adapts to individual user characteristics, session context, and real-time behavioral signals. Instead of static predetermined messages, the system generates and selects selling point messages dynamically based on probabilistic assessments of what will resonate with each specific user in their current context, continuously adapting to user responses and changing conditions
Data Source
AI summary
A data processing system generates recommendations for on-line shopping by scoring recommendations matching the customer's cart contents using by assessing and ranking each candidate recommendation by the expected incremental margin associated with the recommendation being issued (as compared to the expected margin associated with the recommendation not being issued) by taking into consideration historical associations, knowledge of the layout of the site, the complexity of the product being sold, the user's session behavior, the quality of the selling point messages, product life cycle, substitutability, demographics and/or other considerations relating to the customer purchase environment. In an illustrative implementation, scoring inputs for each candidate recommendation (such as relevance, exposure, clarity and/or pitch strength) are included in a probabilistic framework (such as a Bayesian network) to score the effectiveness of the candidate recommendation and/or associated selling point messages by comparing a recommendation outcome (e.g., purchase likelihood or expected margin resulting from a given recommendation) against a non-recommendation outcome (e.g., the purchase likelihood or expected margin if no recommendation is issued). In addition, a probabilistic framework may also be used to select a selling point message for inclusion with a selected candidate recommendation by assessing the relative strength of the selling point messages by factoring in a user profile match factor (e.g., the relative likelihood that the customer matches the various user case profiles).