Re-watching Content Recommendation via Watch History Analysis
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Solution Overview
Problem
The proliferation of streaming content has made it difficult for users to find previously viewed content they wish to re-watch, as existing content distribution systems lack effective tools for recommending re-watchable content.
Innovation Solution
A system and method that analyzes watch histories of multiple users to identify frequently re-watched content, recommending videos that a user has previously watched or liked, using a crawling component to gather data, an identification component to analyze user watch histories, and a recommendation component to suggest content based on inferred user interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content distribution systems provide unlimited streaming content options, then user viewing choices increase, but the difficulty of finding previously viewed content for re-watching increases
Solution Approach 1:
The system performs preliminary actions by automatically analyzing user watch histories and pre-identifying frequently re-watched content before the user needs to search for it. The crawling component continuously monitors and stores watch history data, and the identification component pre-processes this data to flag content with high re-watch probability, so when the user accesses the system, the recommended content is already prepared and ready to present.
Solution Approach 2:
The system implements feedback mechanisms by monitoring user viewing behavior patterns and using this feedback to refine recommendations. The crawling component collects data on what users watch and how long they spend on content, the identification component analyzes this feedback data to identify re-watch patterns, and the recommendation component uses this refined feedback to personalize content suggestions, creating a continuous improvement loop.
2Measurement precision
If content distribution systems track and analyze user watch histories, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex recommendation task into three distinct functional components: a crawling component that collects and stores watch history data, an identification component that analyzes the data to identify re-watch patterns, and a recommendation component that presents personalized suggestions. This segmentation allows each component to focus on a specific function, making the overall system more manageable and easier to maintain while achieving high measurement precision.
Solution Approach 2:
The identification component acts as an intermediary between the data collection (crawling) and recommendation generation stages. It processes raw watch history data into meaningful insights about user preferences and re-watch patterns, translating complex behavioral data into actionable recommendation signals. This intermediary layer simplifies the system architecture by separating data processing from recommendation logic.
Data Source
AI summary
Systems and methods for recommending content to a user that the user previously viewed for viewing again are presented. A system includes a crawling component configured to crawl watch histories of a plurality of users, wherein the watch histories reflect videos watched by respective users of the plurality of users, and identify based on the watch histories, a set of videos that have been watched more than once by individual users of the plurality of users. An identification component further analyzes watch history of a first user, wherein the watch history of the first user reflects videos watched by the first user, and identifies based on the watch history of the first user, one or more videos watched by the first user that are included in the set of videos, and a recommendation component recommends the one or more videos to the first user for re-watching.