Understanding Quora's Topic Modeling Algorithm: Insights and Techniques

Quora, a popular question and answer platform, uses sophisticated algorithms to categorize questions into relevant topics. This process, often referred to as topic modeling, helps users find and engage with questions and answers more effectively. In this article, we delve into the technical aspects of topic modeling, focusing on the principles of the FastText algorithm, and provide insights into how Quora implements its own version of topic modeling.

Introduction to FastText and Its Application in Topic Modeling

The FastText algorithm, developed by research teams at Facebook and supported by the fastText library, is a popular choice for predicting the class of a sentence given its sequence of words. It uses a low-rank linear representation of basic word and n-gram features, making it efficient and scalable for large datasets. FastText has been successfully applied in various natural language processing (NLP) tasks, including topic modeling.

For the specific case of Quora's topic modeling, the core goal is to identify and categorize questions into relevant topics. However, unlike a standard classification problem where each question might belong to a single topic, Quora's dataset presents a more complex scenario. Questions often relate to multiple topics, which makes the task of assigning a single topic to a question moot. Therefore, Quora's approach to topic modeling must accommodate the presence of multiple correct answers per question.

Building a Topic Model for Quora: A Proposed Approach

To build a robust topic model for Quora, one can follow the following steps:

Define a Training Set: Begin with a large dataset of questions that have been curated by the community. This can be based on metrics such as followers, answers, and engagement levels. Train a FastText-like Model: Use a FastText-like model to predict the user-vetted topics. The model should be trained on a set of contrastive examples, where each topic is represented by a set of sentences that are randomly sampled to cover a wide range of related and unrelated topics. Implement Sigmoid Nonlinearity: Unlike standard softmax, which forces a monolithic decision, a sigmoid nonlinearity can be used to estimate the posterior probability of each topic. This approach allows multiple topics to be assigned to a question, reflecting the overlapping nature of the topics in Quora's dataset. Rank and Select Topics: Once the probabilities for each topic are estimated, rank the topics based on their association strength with the question and select the top few to assign to the question.

Quora's Current Topic Modeling Methodology

While the exact details of Quora's proprietary topic modeling algorithm are not publicly disclosed, we can make some educated guesses based on patterns observed in the data. Typically, Quora's algorithm assigns topics based on the words in the question title. The system likely evaluates each word and assigns a small number of topics, which are then ranked based on the strength of the association with the question. If the association is strong, the topic is assigned; otherwise, the next topic is evaluated.

It is not clear whether Quora uses single words or bigrams for topic assignment. However, it is evident that the algorithm has improved since it was first observed. Early implementations might have used only single words, but the current version likely incorporates bigrams to improve accuracy.

Real-World Examples and Insights

Based on recent observations, the topics assigned to a question on Quora such as “How does the Quora topic modeling work” are:

Topic Models Quora's Technical Infrastructure Quora Features Machine Learning Quora

Key words that populated these topics were “Quora,” “topic,” and “modeling.” These suggestions are derived from the question itself, and while it is possible to guess where some of these words came from, there are likely more ambiguous cases where the topic classification is less straightforward.

Conclusion

Quora's approach to topic modeling is a combination of both proprietary and publicly available techniques. While the FastText algorithm provides a strong foundation, the specific implementation details used by Quora remain a mystery. By understanding the principles underlying these methods, we can better appreciate the complexity and effectiveness of topic modeling in a platform like Quora.