ELARA: An Interpretable Context-Aware Recommendation Engine Based on Large Language Models and RAG

The ELARA project, led by Sarah Sohaib, integrates Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) technologies. It aims to address the black-box problem of traditional recommendation systems and build a transparent recommendation system with natural language reasoning capabilities. Its core design philosophy is to make recommendations a meaningful dialogue—ensuring both recommendation accuracy and human-friendly interpretability, thus providing new ideas for the interpretability of recommendation algorithms.

Recommendation systems have been deeply integrated into digital life, but traditional systems face the black-box problem: users are often confused about the reasons for recommendations, and systems cannot provide easy-to-understand explanations, which harms user experience and trust. Existing interpretability methods are mostly technical and hard for ordinary users to understand. The rise of LLM brings new possibilities—using its natural language generation capabilities to provide humanized, context-aware explanations.

ELARA adopts a front-end and back-end separation architecture: the back-end uses FastAPI to build high-performance APIs, and the front-end uses React + Vite to achieve a smooth interface. Core technologies include:

1. Semantic Retrieval Layer: Uses embedding models to understand the deep meaning of queries and achieve precise matching;
2. RAG Enhancement Layer: Retrieves real content from the knowledge base to solve the LLM hallucination problem and ensure recommendation reliability;
3. Context-Aware Module: Integrates user historical behavior and real-time status (mood, type preferences, etc.) to meet current needs.

ELARA is designed with a user-centric approach:

• A natural language query box that supports expressing needs in daily language;
• Multi-dimensional filters (mood, type, era) for refined control of recommendations;
• Explanation Panel: Each recommendation comes with a plain-language explanation to clarify the reason for the recommendation;
• Match Degree Visualization: Uses a score ring to show the degree of fit, enhancing trust.

ELARA is applicable to multiple fields: content recommendation (streaming media, news), e-commerce (improving conversion rates), and education (personalized learning resources). Macroscopically, it represents the direction of recommendation systems from algorithm optimization to human-machine collaboration—when users understand the recommendation logic, they are more likely to trust the system, forming a positive cycle.

ELARA adopts good engineering practices: Git branch collaboration, semantic commit specifications; clear front-end and back-end API contracts, unified encapsulation and calling via api.js; choosing the MIT open-source license to share technical solutions and promote community development.

ELARA can explore in the future: introducing multi-modal understanding (processing images, audio), enhancing conversational interaction (supporting follow-up questions for clarification), and integrating reinforcement learning (optimizing explanation strategies). Interpretability is a necessary condition for trustworthy AI, and ELARA's practice predicts a new paradigm for recommendation technology—shifting from "what to show users" to "exploring with users".