Context-Aware Video Recommendation System
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Solution Overview
Problem
Existing video recommendation techniques often provide inappropriate recommendations by ignoring user context, such as time of day and device type, leading to inefficient use of computing resources and poor user engagement.
Innovation Solution
The method identifies user interest in videos based on session context using historical data, incorporating factors like ratings, consumption percentages, and device-specific attributes to provide context-aware video recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing video recommendation techniques use collaborative filtering algorithms to analyze historical ratings and consumption data, then video recommendations can be generated based on user preferences, but the recommendations may be inappropriate when ignoring user context such as time of day and device type
Solution Approach 1:
The patent applies dynamics by making the recommendation system adaptive to changing user contexts. The system dynamically adjusts recommendations based on session context attributes such as time of day, device type, and user behavior patterns. This allows the recommendation engine to transition from static historical analysis to dynamic context-aware recommendations, resolving the contradiction between maintaining recommendation accuracy and adapting to varying user situations
Solution Approach 2:
The patent changes the parameters considered in the recommendation process by incorporating session context attributes (time of day, device type, behavior patterns) alongside traditional user preferences. This parameter expansion allows the system to maintain recommendation accuracy while becoming context-aware, directly addressing the technical contradiction
2Productivity
If video recommendations are provided without considering session context, then computing resources are expended on generating recommendations, but user engagement is not enhanced when recommendations are ill-suited to user context
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing session context attributes and user behavior patterns before the recommendation generation process. This allows the system to quickly filter and evaluate videos based on pre-analyzed context information, reducing computing resource expenditure during actual recommendation generation while maintaining high user engagement through context-appropriate recommendations
Solution Approach 2:
The patent changes the recommendation parameters to include session context attributes, which enables the system to improve user engagement without proportionally increasing computing resources. By incorporating context parameters like time of day and device type, the system generates more relevant recommendations more efficiently
3Reliability
If context-aware video recommendations are provided by analyzing session context and historical user behavior, then more accurate and engaging recommendations are achieved, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the recommendation system into distinct modules: one for collecting and storing session context attributes, another for analyzing historical user behavior patterns, and a final module for generating recommendations based on both inputs. This modular segmentation manages system complexity by organizing the context-aware recommendation process into manageable, independent components
Solution Approach 2:
The patent introduces an intermediary layer that processes session context attributes and historical behavior data before generating recommendations. This intermediary processing layer manages complexity by preprocessing and structuring the input data, making the overall system more manageable while maintaining high recommendation accuracy
Data Source
AI summary
Systems and methods for identifying, in a network environment in which users watch videos that are downloaded or streamed over a network, a video in which a user is likely to be interested based on session context. For example, a server or other computing system identifies prior session contexts in which prior users watched videos and session progress data for prior sessions in which these prior users watched the videos. The server or other computing system determines a session context of a user for whom a video is to be recommended. For this user, the server or other computing system generates a recommendation identifying one or more videos in which the user is likely to be interested, where the user has not previously watched the recommended videos. The recommendation is generated based on the prior session contexts, the session progress data, and the session context of the user.