Diversified Item Advertisement Recommendation System
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Solution Overview
Problem
Existing digital advertisement recommendation systems often display irrelevant items to customers, leading to lost sales and a poor user experience, as they focus on cross-selling similar items rather than providing diverse and relevant recommendations.
Innovation Solution
A system that determines relevancy and diversity values for recommended items based on category affinity scores and distance models, selecting a diversified subset of items to display alongside an anchor item, thereby enhancing user interest and increasing sales conversions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cross-sell recommendations for similar items are provided, then the advertisement recommendation system can display relevant items, but the items become irrelevant to the customer and lead to lost sales
Solution Approach 1:
The patent changes the selection parameters from simple similarity-based cross-sell to a diversified selection criteria that balances relevance and variety. The system uses a diversity score calculation that incorporates multiple parameters (category distance, price distance, brand distance) to select advertisements, transforming the single-parameter similarity approach into a multi-parameter optimization problem that resolves the contradiction between relevance and customer interest
Solution Approach 2:
The patent introduces a new dimension of diversity measurement by calculating distances across multiple attributes (category, price, brand) in addition to relevance. This multi-dimensional approach allows the system to select items that are not only relevant but also diverse across different product attributes, effectively adding dimensional complexity to resolve the contradiction between similarity and variety
2Productivity
If diversified item advertisements are provided, then customer interest and sales conversions increase, but the system complexity increases
Solution Approach 1:
The patent segments the advertisement selection process into distinct computational stages: relevance scoring, diversity score calculation across multiple dimensions (category, price, brand distances), and final selection based on combined criteria. This segmentation of the recommendation process into modular components manages system complexity by breaking down the complex diversification task into manageable, independent calculation steps
Solution Approach 2:
The patent introduces intermediary calculation components that compute diversity scores and distance metrics as intermediate results before final advertisement selection. These intermediary calculations (category distance, price distance, brand distance) act as mediators between the raw item data and the final diversified advertisement selection, managing complexity through structured intermediate processing steps
Data Source
AI summary
This application relates to apparatus and methods for automatically diversifying item advertisements, such as advertisements for items displayed on a website. In some examples, a computing device receives a plurality of recommended items to advertise with an anchor item. The computing device may determine a relevancy between each of the plurality of recommended items and the anchor item. The computing device may also determine a distance between each of the plurality of recommended items. The distances may be determined based on, for example, a category of each item. The computing device may then execute a diversity determination model based on the relevancies and distances to determine a diversified subset of the recommended items. Advertisements for the diversified subset of recommended items may then be displayed, such as on a webpage dedicated to the anchor item.