Adaptive Content Filtering for Browsing Context
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Solution Overview
Problem
Existing recommendation systems for digital content struggle to accurately determine user browsing context and intent, leading to suboptimal item recommendations, as they fail to differentiate between discovery and consumption modes effectively.
Innovation Solution
The system uses contextual information such as device type, activity data, and historical trends to determine the user's browsing context, applying adaptive filtering to rank and re-rank item recommendations, ensuring that items already on the watch list are promoted during consumption mode and new items are suggested during discovery mode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation systems use general recommendation algorithms without contextual differentiation, then they can provide a broad range of item recommendations, but they fail to accurately match user intent leading to suboptimal recommendations
Solution Approach 1:
The patent segments user browsing behavior into distinct modes (discovery mode and consumption mode) based on contextual parameters. By dividing the recommendation problem into mode-specific sub-problems, the system can apply different recommendation strategies tailored to each mode, improving accuracy without requiring a completely complex new system architecture.
Solution Approach 2:
The system dynamically adjusts recommendation strategies based on the detected browsing mode. Rather than using a static recommendation approach, the system transitions between different recommendation behaviors (exploration vs. exploitation) depending on the user's current context, allowing adaptive response to changing user needs.
2Productivity
If the system promotes items already on the watch list during consumption mode, then user engagement improves, but the system may miss opportunities to introduce new relevant content
Solution Approach 1:
The system dynamically switches between exploitation (promoting watch list items) and exploration (suggesting new items) based on the detected browsing mode. During consumption mode, it exploits known preferences by promoting watch list items, while during discovery mode, it explores new content possibilities, thus adapting to user needs in real-time.
Solution Approach 2:
The system uses user interaction patterns as feedback to determine browsing mode and adjust recommendations accordingly. By monitoring user behavior signals, the system receives feedback about user intent and adapts its recommendation strategy, balancing between promoting familiar content and introducing new items based on observed user responses.
Data Source
AI summary
This disclosure provides examples of computer-implemented systems and processes for determining a user browsing session context, intent, or activity based on contextual information associated with a user's browsing session, and using this determined context as a basis to filter or improve selection of content items for the user. For example, if the user is building up a watch list (for example, discovering new titles), then it may be desirable to filter out items for recommendation which are already on that watch list. However, when the user is searching for something to watch in the current session (for example, consuming content), then it may be more appropriate to show items already added to the watch list as recommended content. Item recommendations can thus be improved or filtered based in part on parameters of the user's browsing activity with respect to content items.