Publication
AI-Powered Content Recommender for Online Shopping Systems Using Pre-Trained Models
| Summary: | This thesis explores the practical integration of large pre-trained language models (LLMs) into a real-time web-based recommendation system. While most existing studies emphasize the development and evaluation of new algorithms, there is limited research addressing the implementation challenges faced by non-expert developers seeking to use AI models via APIs. Using the Design Science Research Methodology (DSRM), this project designs, builds, and evaluates a prototype e-commerce platform that delivers personalized product recommendations powered by OpenAI’s GPT-3.5-turbo model. The system collects user behavior data (searches, clicks, favorites) and constructs prompts dynamically to generate real-time product suggestions. The system was tested both technically, through latency and concurrency testing, and with users, who gave feedback on satisfaction, trust, and how accurate the recommendations felt. Results show that the system delivers personalized and timely suggestions with acceptable latency under concurrent use. Overall, the study shows that pre-trained language models are well-suited to support recommendation systems in a simple and effective way, especially for developers who do not specialize in machine learning. |
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| Main Authors: | Adili, Rina |
| Subject: | Recommender System Large Language Models (LLMs) Pre-trained models OpenAI API Prompt engineering AI integration GPT-3.5-turbo SDG 4 - Quality education SDG 9 - Industry, innovation and infrastructure |
| Year: | 2025 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | open access |
| Associated institution: | Universidade Nova de Lisboa |
| Language: | English |
| Origin: | Repositório Institucional da UNL |
| Summary: | This thesis explores the practical integration of large pre-trained language models (LLMs) into a real-time web-based recommendation system. While most existing studies emphasize the development and evaluation of new algorithms, there is limited research addressing the implementation challenges faced by non-expert developers seeking to use AI models via APIs. Using the Design Science Research Methodology (DSRM), this project designs, builds, and evaluates a prototype e-commerce platform that delivers personalized product recommendations powered by OpenAI’s GPT-3.5-turbo model. The system collects user behavior data (searches, clicks, favorites) and constructs prompts dynamically to generate real-time product suggestions. The system was tested both technically, through latency and concurrency testing, and with users, who gave feedback on satisfaction, trust, and how accurate the recommendations felt. Results show that the system delivers personalized and timely suggestions with acceptable latency under concurrent use. Overall, the study shows that pre-trained language models are well-suited to support recommendation systems in a simple and effective way, especially for developers who do not specialize in machine learning. |
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