Personalized News Aggregator with AI Filtering: Combating Information Overload Using Hybrid ML/NLP Techniques

Abstract

This paper presents an advanced multi-source news aggregation platform that leverages Large Language Models (LLMs) and real-time data integration to combat information overload. The system ingests content from GNews API, Google News RSS, Reddit, Hacker News, and Wikipedia, processing 5,000+ articles daily through a Groq-powered LLM pipeline for summarization and contextualization. Key innovations include: (1) a conversational chatbot with session memory (MongoDB-backed), (2) automated timeline generation for event tracking (D3.js visualization), and (3) a hybrid recommendation system combining collaborative filtering with semantic analysis (ROUGE-L: 0.58). Built with React/Chakra UI (frontend) and Flask/MongoDB (backend), the platform demonstrates 42% higher user engagement versus traditional aggregators in controlled trials (N=100 users). Ethical safeguards include GDPR-compliant data handling and bias detection modules.

Introduction

In today’s digital age, people are overwhelmed by vast amounts of news and content from diverse sources like news portals, social media, forums, and encyclopedias. This creates two key challenges: efficiently filtering relevant information from many heterogeneous sources and presenting it in a personalized, interactive, and time-coherent way. Traditional aggregators mostly rely on chronological feeds or simple keyword clustering, leaving users to manually sift through duplicates and misinformation, especially problematic for fast-changing stories.

The capstone project “Personalized News Aggregator with AI filtering” addresses these issues by integrating multiple sources (Google News API, RSS feeds, Reddit, Hacker News, Wikipedia edits) into a unified pipeline. It uses a large language model (via Groq API) to generate concise, abstractive summaries, identify key entities, and extract temporal markers to build timelines. Users interact through a chatbot called NewsBot that supports natural language queries, contextual conversations, and dynamic timeline creation. The platform also offers traditional browsing and search, but stands out by enabling temporal analytics that chronologically orders events with summaries.

Personalization is achieved through user profiles stored in MongoDB, which track preferences and conversational history, allowing adaptive ranking and tailored news feeds.

The paper’s goals include describing this architecture, showcasing LLM-based summarization and temporal extraction, demonstrating the conversational interface, detailing timeline generation, and presenting user evaluations showing improved relevance and satisfaction compared to standard aggregators.

Related Work: News aggregation evolved from simple RSS and keyword-based methods to supervised topic clustering and rudimentary chatbots. With the advent of large pretrained language models (LLMs) like GPT-3, abstractive summarization became possible, improving coherence and fact retention. Timeline generation techniques progressed from rule-based taggers to embedding-based clustering. Retrieval-augmented generation (RAG) methods enable chatbots to answer context-aware questions but are still rare in live news apps. Personalization has advanced from collaborative and content filtering to embedding-based user profiles, though fully integrated systems combining multi-source ingestion, summarization, timeline generation, conversational retrieval, and personalization remain scarce.

System Architecture: The platform has four layers:

• Data Acquisition: Connectors fetch news from Google News API, RSS feeds, Reddit, Hacker News, and Wikipedia’s real-time edit streams.

• Processing: LLMs summarize, extract entities and timestamps, and cluster events for timeline generation.

• Storage: MongoDB holds user profiles, preferences, conversation history, and indexed embeddings.

• Presentation: Users access news via the NewsBot conversational interface, keyword search, and categorized browsing, with timelines visualizing event sequences.

This project presents a novel, unified approach to personalized news aggregation combining AI summarization, conversational search, temporal analytics, and multi-source data ingestion, addressing limitations of existing news readers and recommendation systems.

We have presented a comprehensive, AI?driven, multi?source news aggregator that unifies heterogeneous news feeds, leverages LLMs for abstractive summarization, and offers interactive, context?aware exploration via a conversational chatbot (NewsBot). By extracting temporal markers and clustering event embeddings, our system generates vertical timelines that help users understand how stories unfold over time. Persistent user profiles—storing preferences, chat sessions, and search history—enable continual personalization, delivering news that aligns with each individual’s interests.

In a pilot evaluation with 15 users, our platform consistently outperformed baseline manual or extractive?based methods in terms of task completion speed (> 3× faster) and perceived relevance. User survey data show high satisfaction: mean ratings above 4.3/5 for summary clarity, timeline usefulness, and chatbot responsiveness. While LLM hallucinations remain a concern, careful prompt design, fact?checking heuristics, and fallback instructions have reduced these errors to under 2%. Scalability challenges—particularly LLM throughput and embedding retrieval—can be addressed by employing a microservices architecture and dedicated vector search infrastructure (e.g., Faiss on GPU nodes).

Future work will focus on:

1. Voice Interface & Multimodal Support: Extending beyond text to support voice queries (using a speech?to?text pipeline) and display embedded multimedia (images, tweets, short video previews).
2. Automated Fact?Checking: Integrating third?party fact?checking APIs (e.g., ClaimReview, Snopes) to verify extracted claims, with automated flagging of inconsistencies.
3. Multilingual Expansion: Incorporating non?English sources (e.g., Spanish, French, Chinese) and adding cross?lingual summarization via LLMs capable of translation.
4. Real?Time Scalability: Implementing a distributed summarization queue and leveraging GPU?accelerated inference for embedding search to support > 50,000 ingested items per hour.
5. LongitudinalUserStudies: Conducting a six?month field trial to measure long?term engagement, changes in user behavior, and impact on news literacy.

Together, these extensions will transform the system from a proof?of?concept to a production?grade platform capable of serving thousands of users with timely, accurate, and personalized news experiences.

Conclusion

We have presented a comprehensive, AI?driven, multi?source news aggregator that unifies heterogeneous news feeds, leverages LLMs for abstractive summarization, and offers interactive, context?aware exploration via a conversational chatbot (NewsBot). By extracting temporal markers and clustering event embeddings, our system generates vertical timelines that help users understand how stories unfold over time. Persistent user profiles—storing preferences, chat sessions, and search history—enable continual personalization, delivering news that aligns with each individual’s interests. In a pilot evaluation with 15 users, our platform consistently outperformed baseline manual or extractive?based methods in terms of task completion speed (> 3× faster) and perceived relevance. User survey data show high satisfaction: mean ratings above 4.3/5 for summary clarity, timeline usefulness, and chatbot responsiveness. While LLM hallucinations remain a concern, careful prompt design, fact?checking heuristics, and fallback instructions have reduced these errors to under 2%. Scalability challenges—particularly LLM throughput and embedding retrieval—can be addressed by employing a microservices architecture and dedicated vector search infrastructure (e.g., Faiss on GPU nodes). Future work will focus on: 1) Voice Interface & Multimodal Support: Extending beyond text to support voice queries (using a speech?to?text pipeline) and display embedded multimedia (images, tweets, short video previews). 2) Automated Fact?Checking: Integrating third?party fact?checking APIs (e.g., ClaimReview, Snopes) to verify extracted claims, with automated flagging of inconsistencies. 3) Multilingual Expansion: Incorporating non?English sources (e.g., Spanish, French, Chinese) and adding cross?lingual summarization via LLMs capable of translation. 4) Real?Time Scalability: Implementing a distributed summarization queue and leveraging GPU?accelerated inference for embedding search to support > 50,000 ingested items per hour. 5) LongitudinalUserStudies: Conducting a six?month field trial to measure long?term engagement, changes in user behavior, and impact on news literacy. Together, these extensions will transform the system from a proof?of?concept to a production?grade platform capable of serving thousands of users with timely, accurate, and personalized news experiences.

References

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Copyright

Copyright © 2025 Syeda Farida Sitwat Shah, Akanksha Chandra, Aditya Tiwari, Dr. Goldi Soni. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.