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DOI: 10.4230/OASIcs.SLATE.2026.15

A Silent Multi-Agent Recommendation Engine with Graph-Augmented Memory for Immersive B2B E-Commerce

Authors: Davi Silva Eleuterio, Pedro Filipe Oliveira, and Paulo Matos

Published in: OASIcs, Volume 144, 15th Symposium on Languages, Applications and Technologies (SLATE 2026)

Abstract

Currently, e-commerce platforms are transitioning from static 2D catalogues to immersive environments, requiring recommendation systems to process complex behavioural signals. Traditional collaborative filtering struggles with cold-start scenarios and lacks semantic understanding of product catalogues. This paper presents a silent multi-agent recommendation engine developed for the VIMOS (Virtual Integrated Market Online Shopping) project, designed to operate without explicit textual input. Orchestrated via a LangGraph Directed Acyclic Graph, the system utilises Agentic Retrieval-Augmented Generation (RAG) to dynamically synthesise behavioural signals, dense vector retrieval (Qdrant), and knowledge graph traversal (Apache AGE). A locally hosted Llama 3.1 8B model autonomously extracts implicit user preferences to update a persistent graph database and generates deterministic, human-readable explanations for each recommendation. A two-shot verifier engineering pattern is introduced to resolve the structural incompatibility between dynamic candidate sets and static graph execution. The architecture is validated using a controlled synthetic dataset of B2B industrial products - sufficient for functional validation of all agent routing paths - demonstrating high retrieval accuracy, strict constraint adherence (stock and availability verification), and sub-4-second end-to-end latency (when the generative semantic justification step is bypassed) with no external API dependencies.

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Davi Silva Eleuterio, Pedro Filipe Oliveira, and Paulo Matos. A Silent Multi-Agent Recommendation Engine with Graph-Augmented Memory for Immersive B2B E-Commerce. In 15th Symposium on Languages, Applications and Technologies (SLATE 2026). Open Access Series in Informatics (OASIcs), Volume 144, pp. 15:1-15:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{eleuterio_et_al:OASIcs.SLATE.2026.15,
  author =	{Eleuterio, Davi Silva and Oliveira, Pedro Filipe and Matos, Paulo},
  title =	{{A Silent Multi-Agent Recommendation Engine with Graph-Augmented Memory for Immersive B2B E-Commerce}},
  booktitle =	{15th Symposium on Languages, Applications and Technologies (SLATE 2026)},
  pages =	{15:1--15:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-440-6},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{144},
  editor =	{Batista, Fernando and Ribeiro, Eug\'{e}nio and Ribeiro, Ricardo and Santos, Andr\'{e} L.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2026.15},
  URN =		{urn:nbn:de:0030-drops-267131},
  doi =		{10.4230/OASIcs.SLATE.2026.15},
  annote =	{Keywords: multi-agent systems, agentic RAG, product recommendation, LangGraph}
}

Document

DOI: 10.4230/OASIcs.SLATE.2025.7

A Chatbot to Help Promoting Financial Literacy

Authors: Davi Silva Eleuterio, Pedro Filipe Oliveira, Paulo Matos, and Jorge Manuel Afonso Alves

Published in: OASIcs, Volume 135, 14th Symposium on Languages, Applications and Technologies (SLATE 2025)

Abstract

Currently, governments and many other institutions have been making significant efforts to promote financial literacy. However, a considerable portion of the population still lacks basic financial knowledge, highlighting the need for updated strategies to enhance financial education. In today’s digital world - where people often search for quick and convenient solutions - the development of a reliable and intelligent chatbot to answer questions related to financial concepts and decision-making can be very beneficial. This paper proposes the implementation of an automated web scraper to extract content from a trustworthy financial education website with plenty of useful concepts about finances, using this collected data to develop a chatbot which provides accurate and helpful responses to users. The solution is built using technologies such as Streamlit, Langchain, and OpenAI.

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Davi Silva Eleuterio, Pedro Filipe Oliveira, Paulo Matos, and Jorge Manuel Afonso Alves. A Chatbot to Help Promoting Financial Literacy. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 7:1-7:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{eleuterio_et_al:OASIcs.SLATE.2025.7,
  author =	{Eleuterio, Davi Silva and Oliveira, Pedro Filipe and Matos, Paulo and Alves, Jorge Manuel Afonso},
  title =	{{A Chatbot to Help Promoting Financial Literacy}},
  booktitle =	{14th Symposium on Languages, Applications and Technologies (SLATE 2025)},
  pages =	{7:1--7:9},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-387-4},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{135},
  editor =	{Baptista, Jorge and Barateiro, Jos\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2025.7},
  URN =		{urn:nbn:de:0030-drops-236870},
  doi =		{10.4230/OASIcs.SLATE.2025.7},
  annote =	{Keywords: chatbot, financial literacy, web scraper, LLM, RAG}
}

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Documents authored by Eleuterio, Davi Silva

A Silent Multi-Agent Recommendation Engine with Graph-Augmented Memory for Immersive B2B E-Commerce

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A Chatbot to Help Promoting Financial Literacy

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