Suggested Browsing Session Prefetching via User Behavior Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network communication systems lack the ability to predict and prefetch content sequences that users are likely to find interesting based on their previous requests and interactions, leading to inefficient browsing experiences.
Innovation Solution
The system analyzes user behavior patterns to identify likely content sequences, which are then prefetched and presented as 'suggested browsing sessions' on the client device, incorporating features like automatic playback and user interaction simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system prefetches content based on predicted user requests, then browsing efficiency is improved, but the accuracy of predicting user interest deteriorates
Solution Approach 1:
The system performs preliminary actions by prefetching content before users actually request it. The browser predicts which content users will need next and retrieves it in advance, storing it in the cache. This preliminary action reduces future latency and improves browsing efficiency while managing cache resources effectively.
Solution Approach 2:
The system implements feedback mechanisms by monitoring actual user browsing behavior and using this information to refine prediction accuracy. The browser analyzes patterns in user requests, cache hit rates, and browsing habits to continuously improve its prediction algorithms, ensuring that prefetching decisions become more accurate over time.
2Speed
If the system caches content locally, then request speed is improved, but cache memory consumption increases
Solution Approach 1:
The system applies partial action by selectively caching only the most likely needed content rather than all possible content. The browser predicts which specific resources users will request next and prefetches only those, rather than caching everything. This partial approach optimizes the balance between cache utilization and request speed improvement.
Solution Approach 2:
The system changes parameters dynamically by adjusting cache retention policies, prefetching aggressiveness, and cache size allocation based on available memory, user behavior patterns, and network conditions. This allows the system to optimize cache performance while adapting to changing system resources and user needs.
Data Source
AI summary
Features are disclosed for determining a sequence of content, including but not limited to web pages, that a user of a client device is likely to request or otherwise find interesting based on previous content requests (of that user and/or other users), and to making the sequence of content available for viewing on the client device. The identified pattern may later be used as a basis to prefetch the content, and to make the sequence of prefetched content available on the client device as a “suggested browsing session.” The suggested browsing session may include a sequence of content pages typically requested by the user, and/or may include linked pages and recommendations that the user is otherwise likely to find interesting.