Upgrade Recommendations Using Social Graph Data
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Solution Overview
Problem
Existing online shopping systems fail to provide effective upgrade recommendations for downloadable content items like video games, music, and movies, as they typically rely on general product categories or unrelated purchases, neglecting the user's specific features and social connections.
Innovation Solution
Implementing a system that uses social graph information and purchase histories to recommend upgraded versions of content items during the checkout process, utilizing interstitial displays for easy comparison and selection, and prioritizing versions based on frequency of purchase and social affiliations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If general product category-based recommendations are used, then the recommendation system is simple to implement, but the recommendation accuracy and relevance to user needs deteriorates
Solution Approach 1:
The system transitions from using general product category parameters to utilizing multiple refined parameters including social graph data, purchase history, feature availability, and version comparison metrics to generate more accurate upgrade recommendations
Solution Approach 2:
The recommendation system adds new dimensions by incorporating social network relationships and user purchase behavior patterns alongside traditional product attributes, creating a multi-dimensional recommendation space that improves accuracy
2Productivity
If upgrade recommendations are provided during checkout, then the opportunity to increase sales is improved, but the checkout process complexity increases
Solution Approach 1:
The system performs upgrade recommendation calculations and prepares comparison information in advance during product browsing, so that during checkout only the pre-prepared recommendations are displayed, minimizing additional user burden and process complexity
Solution Approach 2:
The system introduces an intermediary recommendation module that sits between the product catalog and checkout process, handling the complexity of upgrade analysis separately while presenting simplified options to the user during checkout
3Adaptability or versatility
If social graph information is used for recommendations, then the personalization and relevance of recommendations is improved, but the data processing requirements and system complexity increases
Solution Approach 1:
The system extracts only the necessary social graph elements (friends who have purchased similar items) and purchase history patterns needed for recommendations, rather than processing the entire social network data set, reducing computational complexity while maintaining personalization
Data Source
AI summary
The subject disclosure relates systems and methods for making upgrade recommendations, such as recommendations for version upgrades to a purchasable media-content item. A process of implementing the subject technology can include steps for authenticating a user associated, identifying affiliates of the user, wherein the affiliates are associated with the user via a social network or online gaming platform, receiving a user selection of a content item, and identifying an upgrade version of the content item, wherein the upgrade version is associated with a second version identifier and a second price indicator. In some aspects, the process can further include steps for providing a recommendation to the user, wherein the recommendation indicates the upgrade version of the content item and the second price indicator. Systems and machine-readable media are also provided.