Welcome to PhD-seminar May 2025
2025-04-24
When? Friday May 16th 14-16
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/DKAh2iCN5EGEth9F6 by May 14th (especially important if you plan on attending onsite so we have fika for everyone)
Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Secure On-Premises Deployment of Large Language Models for Enhanced Patent Drafting – Homam Mawaldi, AWA
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Enhancing E-commerce Personalization with a Hybrid Recommendation and Advanced Search System – Kailash Chowdary Bodduluri, Enode
15.50 -16.00 Sum up and plan for our seminars in June
Abstracts
Secure On-Premises Deployment of Large Language Models for Enhanced Patent Drafting – Homam Mawaldi, AWA
Patent drafting is a complex and high-stakes process for securing intellectual property rights. During the patent prosecution phase, maintaining confidentiality is crucial, which makes cloud-based third-party services inadequate. This study explores the feasibility of AWACopilot, a secure, on-premise solution comprising a web service that leverages open-source large language models (LLMs) to assist patent attorneys in the intricate patent application drafting process. AWACopilot generates key patent sections such as background, abstract, detailed description, etc., from human-crafted claims, addressing the data security risks posed by cloud-based AI services. Its modular architecture enables customization and adaptability to different patent tasks. Although challenges remain—including reliance on LLM capabilities and the need for rigorous content verification—this study demonstrates the potential for secure, AI-driven solutions to enhance patent drafting workflows.
Enhancing E-commerce Personalization with a Hybrid Recommendation and Advanced Search System – Kailash Chowdary Bodduluri, Enode
In the evolving landscape of e-commerce, personalizing user experience through recommendation systems has become a way to boost user satisfaction and engagement. However, small-scale e-commerce platforms struggle with significant challenges, including data sparsity and user anonymity. These issues make it hard to effectively implement recommendation systems, resulting in difficulty in recommending the right products to users. This study introduces an innovative Hybrid Recommendation System (HRS) to address challenges in e-commerce personalization caused by data sparsity and user anonymity. By blending multiple dimensions of the data into one unified system for producing recommendations, this system represents a notable advancement in achieving personalized user experiences in the context of limited data. In addition to the recommendation system, we have also developed an effective search feature with capability of leveraging fuzzy matching, TF-IDF vectorization, and a Swedish language synonym model for query expansion. Our current research focuses on integrating these two independent systems—recommendations and search—to address their individual limitations and create a unified discovery ecosystem. By combining explicit search behaviors with implicit user preferences and exploring technologies such as large language models and sequential recommendation frameworks, we aim to further improve and optimize product discovery in data-sparse environments.