Post Topic Classification via ANN Vector Clustering
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Solution Overview
Problem
Social networking systems face challenges in effectively categorizing and filtering posts based on their topics, leading to irrelevant content being displayed to users and difficulties in managing advertisement campaigns.
Innovation Solution
An artificial neural network (ANN) is trained to determine the topic of a post by mapping input vectors to a common vector space, allowing posts with similar topics to be clustered and identified, and multiple ANNs (shards) are used to improve accuracy and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple ANNs (shards) are trained on different training pages, then measurement precision of topic determination is improved, but device complexity increases
Solution Approach 1:
The system divides the topic determination task into multiple specialized ANNs (shards), where each ANN is trained on a specific subset of training pages. This segmentation allows each ANN to become an expert in its domain, improving overall measurement precision while distributing the computational complexity across multiple manageable components rather than requiring one monolithic complex system
Solution Approach 2:
The system creates a universal framework that can handle diverse topic determination needs through multiple ANNs. Each ANN shard serves multiple purposes: it can independently determine topics for its specialized domain, contribute to the common vector space, and be combined with other shards to handle broader classification tasks, thus achieving multi-functionality
2Adaptability or versatility
If a common vector space is created by mapping training-page vectors from multiple ANNs, then adaptability of the system is improved, but device complexity increases
Solution Approach 1:
The system merges the output vectors from multiple specialized ANNs into a single common vector space. This merging process integrates the specialized knowledge from each ANN shard while maintaining a unified representation format, enabling the system to adapt to various topic determination tasks by combining different expert perspectives without requiring separate processing pipelines for each ANN
Solution Approach 2:
The common vector space acts as an intermediary layer between the specialized ANNs and the final topic determination process. This intermediary structure allows vectors from different ANN shards to be harmonized and integrated, providing a standardized interface that simplifies the complexity of coordinating multiple specialized systems while enhancing overall system adaptability
Data Source
AI summary
In one embodiment, a method includes accessing an input vector representing an input post, wherein: the vector space comprises clusters each associated with a topic; each cluster was determined based on a clustering of training-page vectors corresponding to training pages that each comprise training posts, each training post submitted by a user to a training page and comprises content selected by the user; and each training-page vector was generated by an ANN that was trained, based on the training posts of training pages associated with the ANN, to receive a post and then output a probability that the received post is related to the training posts of the training pages; determining that the input vector is located within a particular cluster in the vector space; and determining a topic of the input post based on the topic associated with the particular cluster that the input vector is located within.