Collaborative Browsing Session for Real-Time Shopping Recommendations
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Solution Overview
Problem
Electronic commerce lacks the collaborative shopping experience that physical stores provide, as users cannot see what friends are viewing in real-time, limiting recommendations to previously viewed products rather than contemporaneously browsed items.
Innovation Solution
A collaboration service initiates a shared electronic browsing session among users, enabling real-time or asynchronous joint browsing and chat, and recommends items based on the browsing and chat activities of users within a target user's social graph, using sentiment analysis to suggest items of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If electronic commerce provides recommendations based on previously viewed products, then the system can provide personalized recommendations, but it cannot capture collaborative shopping experiences from friends browsing contemporaneously
Solution Approach 1:
The patent introduces a collaboration service as an intermediary that connects multiple users in real-time. This service enables users to share browsing sessions and receive recommendations based on both individual and collaborative browsing history, thereby capturing the collaborative shopping experience without requiring physical presence in the same store.
Solution Approach 2:
The patent replaces the mechanical/physical presence of friends in a store with an electronic collaboration system. Instead of relying on physical interaction, the system uses digital communication to share browsing data and provide real-time recommendations, substituting the mechanical shopping environment with an electronic one.
2Ease of operation
If users shop alone at their computer, then electronic commerce provides convenience, but it lacks the social experience of collaborative shopping
Solution Approach 1:
The collaboration service performs multiple functions within a single system: it enables real-time communication between users, shares browsing histories, provides personalized recommendations, and facilitates collaborative decision-making. This multi-functionality allows users to enjoy both the convenience of online shopping and the social experience of collaborative browsing.
Solution Approach 2:
The collaboration service acts as an intermediary that enables social interaction between users who are physically separated. It provides the social experience of shopping together while maintaining the convenience and accessibility of electronic commerce, effectively bridging the gap between solitary online shopping and collaborative in-store shopping.
3Device complexity
If recommendations are limited to previously viewed products, then the system simplicity is maintained, but the recommendation accuracy and relevance are reduced
Solution Approach 1:
The system performs preliminary actions by collecting and storing browsing history data from multiple users before generating recommendations. It proactively gathers information about products viewed, searched, and interacted with, then uses this pre-collected data to generate more accurate and relevant recommendations, improving precision without requiring complex real-time processing.
Solution Approach 2:
The system incorporates feedback mechanisms where users can indicate their interest in recommended products, and this feedback is used to refine future recommendations. The collaboration service analyzes user interactions and adjusts recommendation algorithms accordingly, continuously improving accuracy while maintaining manageable system complexity through iterative refinement.
Data Source
AI summary
Techniques for initiating a shared electronic browsing session for a plurality of users are described herein. A computing device may initiate the session and associate browsing and chat activity of the users with the session to enable at least one of real-time, joint browsing and chat or asynchronous browsing and chat. Further, the computing device may determine a social graph for one of the users based on the session and may recommend items to the user based on content browsing and chat activities of users included in the social graph.