Curating Your Digital Feed: Techniques for a Tech-Focused and Positive Online Experience

Many individuals find themselves overwhelmed by the sheer volume and often negative tone of online news, wishing for a way to focus on specific interests like technology and entrepreneurship without the constant barrage of distressing updates. This desire for a more curated and positive information diet has led to various innovative solutions, ranging from do-it-yourself tools to specialized platforms.

DIY Filtering with Modern AI

One suggested approach is to leverage the power of Large Language Models (LLMs) by developing a browser extension. Such an extension could process the content of a news feed and automatically hide articles that don't align with the user's defined interests, effectively filtering out political news, catastrophes, or other unwanted topics.

Advanced Personalization: Building a Custom ML-Powered Filter

A more sophisticated solution involves creating a custom content filtering system, exemplified by one user's self-built RSS reader named YOShInOn. This system demonstrates a practical application of machine learning for personalized content curation.

The Core Idea: Predicting User Preferences

The heart of this custom reader is a classification model (built with Python and scikit-learn) that predicts the probability of the user liking a particular item. This model learns from the user's explicit judgments (e.g., thumbs up/down) on articles.

Technical Implementation Details

The system runs as a script alongside a web server (Python-based, using Flask and HTMX for the front end) where the user views articles and provides feedback. While initially using ArangoDB, there's a plan to migrate to PostgreSQL, employing a custom library to emulate ArangoDB's features. The user interface for making judgments is designed to be fast and easy, akin to platforms like TikTok or Tinder, acknowledging that thousands of judgments are needed to train effective models.

Training the Model and Refining Selections

A batch job periodically retrains the model. It retrieves user judgments, converts them into a NumPy matrix, and uses scikit-learn to train the classifier. Recommendations are then generated by sampling the top N/k documents from various content clusters, blended with a percentage (e.g., 30%) of randomly sampled documents to ensure the training data remains representative and avoids overly narrow filtering.

Challenges and Future Directions

Content classification based on personal preference is inherently a "fuzzy problem" because a user's interest in an article can vary over time, placing an upper limit on achievable accuracy. Future explorations include a "centaur use case," where models assist humans in more clearly defined classification tasks (e.g., determining an author's emotional tone, identifying sports game reports). There's also interest in developing a general-purpose text classification toolkit and applying filtering based on emotional characteristics to social media content to improve user experience by minimizing exposure to negative behavior.

Alternative: Curated Communities

For those seeking a simpler solution without building custom tools, platforms like lobste.rs offer a highly curated environment. These communities often have strict posting guidelines and a strong focus on specific topics, such as technology, providing a pre-filtered content stream.