Collaborative Filtering for Personalized Information Recommendations
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Solution Overview
Problem
Existing cloud-based service platforms face challenges in providing personalized information recommendations due to limited user engagement data and inclusion of extraneous items, which compromises the relevance and engagement level of recommended data items.
Innovation Solution
A system utilizing a collaborative filtering model to identify a subset of users and information items based on predefined user and object classes, applying a trained model to enhance personalized recommendations by leveraging engagement information from similar users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based service platforms provide data items representative of information in response to user actions, then user engagement is improved, but the relevance and engagement level of recommended data items deteriorates due to limited user engagement data and inclusion of extraneous items
Solution Approach 1:
The patent introduces an intermediary approach by using a collaborative filtering model that aggregates engagement information from multiple users. Instead of relying solely on an individual user's limited engagement data, the system uses the collective engagement patterns of users with similar characteristics (derived from user action data) as a mediator to infer relevant recommendations. This intermediary aggregation layer transforms limited individual data into more robust recommendation signals.
Solution Approach 2:
The patent segments users into different groups based on their action patterns and engagement characteristics. By dividing the user base into segments with similar behaviors, the system can apply collaborative filtering within each segment to generate more relevant recommendations. This segmentation allows the system to overcome the limitation of individual user data by leveraging the collective wisdom of homogeneous user groups.
2Quantity of substance
If the system uses only user's own engagement information for recommendations, then the system complexity is reduced, but the quantity and quality of engagement data available for recommendations deteriorates
Solution Approach 1:
The patent applies universality by designing a collaborative filtering system that serves multiple functions: it processes user action data, identifies user segments, aggregates engagement information, and generates recommendations. The same underlying framework handles both data collection and recommendation generation, allowing the system to leverage engagement data from multiple users without proportionally increasing complexity. The universal model architecture enables the system to scale effectively.
3Measurement precision
If cloud-based service platforms rank data items to enhance visibility and accessibility, then user engagement is improved, but the accuracy and precision of recommendations deteriorates due to inclusion of extraneous items
Solution Approach 1:
The patent extracts relevant engagement information from the broader set of user actions by using collaborative filtering to identify patterns specific to users with similar characteristics. The system extracts and focuses on engagement data that is most indicative of genuine interest, filtering out extraneous or noise data. This extraction process enables the system to maintain high recommendation accuracy by concentrating on the most relevant signals rather than being diluted by extraneous items.
Data Source
AI summary
This application is directed to systems and methods for information recommendation and/or ranking. In some embodiments, a disclosed method includes obtaining a request to select a set of personalized items in a collection of information items to a user; identifying a subset of a plurality of users that are classified to a first user class and includes the user; identifying a subset of a collection of information items that are classified to a first object class having a predefined relationship with the first object class; applying a collaborative filtering model to identify one or more personalized information items in the subset of information items based on engagement information of the subset of users with the subset of information items; and in response to the request, enabling display of the one or more information items on a screen of a client device associated with the user.