Social Deal Recommendation System Using Connection Actions
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Solution Overview
Problem
Providers in social networking systems face inefficiencies in directing deals to users who are most likely to be interested, leading to wasted efforts and reduced sales, as existing methods do not effectively leverage social networking data to personalize deal offerings.
Innovation Solution
The system presents deals to users based on social information, such as actions taken by their connections, social groups, events, and interests, categorizing related deals together to enhance user engagement and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If providers direct deals to users without leveraging social networking data, then deal distribution is simple, but user interest and sales effectiveness are reduced
Solution Approach 1:
The social networking system acts as an intermediary between providers and users, using social graphs and user connection data to bridge the gap. The system analyzes social relationships and recommends deals based on connections' actions, transforming simple deal distribution into targeted recommendations without requiring providers to build complex targeting systems themselves
Solution Approach 2:
The system implements feedback loops where user actions (purchasing, commenting, liking deals) are tracked and used to refine future deal recommendations. This feedback mechanism continuously improves targeting accuracy by learning from actual user behavior patterns within the social network
2Loss of energy
If providers offer deals to all users, then deal visibility is high, but resource waste increases due to uninterested users
Solution Approach 1:
Instead of uniform deal distribution to all users, the system applies local quality by tailoring deal recommendations to individual users based on their specific social connections and behavior patterns. Each user receives a customized subset of deals that are locally optimized for their interests and social context
Solution Approach 2:
The system changes the parameters of deal distribution by incorporating multiple variables such as user connections, connection actions, user interests, and social context. These parameter changes transform deal targeting from a binary approach to a multi-dimensional recommendation system that dynamically adjusts based on various factors
3Ease of operation
If social information is used to personalize deals, then user interest increases, but data processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing social graph data, user profiles, and connection information in structured formats. This advance preparation of data enables faster real-time recommendation generation without requiring complex processing during actual deal presentation
Solution Approach 2:
The system segments the complex data processing task into distinct modules: social graph analysis, user profile matching, action history evaluation, and deal relevance scoring. This segmentation allows each component to handle specific aspects of data processing independently, reducing overall system complexity while maintaining comprehensive analysis
Data Source
AI summary
A social networking system suggests deals relevant to a user. The deals are selected for suggestion based on social information associated with the user. Social information used for selecting candidate deals for a user includes information describing other users connected to the user and their associations with the candidate deals or with related deals, for example, deals from the same provider. Associations of connections of the user with the candidate deals may be determined based on actions associated with the candidate deals performed by the connections. The actions performed by the connections may be weighted based on types of the actions to determine a measure of relevance of the candidate deal for the user. Candidate deals are selected from a set of deals by applying deal targeting criteria received from deal providers. The deal targeting criteria specify attributes describing users to be targeted for a particular deal.