View Details

Registration for Computer Science Department's Seminars in 2023

12-04-2023

☘️1. Speaker: Dr. Nguyen Tien Huy

- Time: 10 AM - 11:30 AM, December 09, 2023 (Saturday)

- Topics:

a. Prompt Engineering for LLMs

Prompt engineering involves strategically crafting and refining input queries to generative models, amplifying their precision, adaptability, and performance. Exploring the nuanced art of fine-tuning prompts, this abstract delves into how this technique empowers users with granular control, enabling customization for specific tasks and domains.

b. EnTube: Exploring Key Video Features for Advancing YouTube Engagement

The ever-growing popularity of video sharing on the internet, such as on YouTube, Twitter, and TikTok, has increased the emphasis on viewership ratings and video engagement. Via the engagement analysis, the research demonstrates promising potential in extracting robust characteristics that enable informed decisions regarding engagement.


Interested students can register at https://forms.gle/GgQcvuAfu3TtJtoP7 

Registration deadline: Until 3 PM Friday, December 08, 2023


☘️ 2. Speaker: Dr. Nguyen Ngoc Thao

- Time: 2 PM - 3:30 PM, December 11, 2023 (Monday)

- Topic: Graph Convolution Collaborative Filtering with Dense Embeddings

Recommender systems have been a vital part of many ecommerce and online services. These systems support profound user personalization by mining the semantic interactions between users and items in the database, thereby increasing customer satisfaction and revenue. Furthermore, collaborative filtering research has significantly advanced in recent years with the compelling expressiveness of graph-based neural networks. In this seminar, we introduce a novel collaborative filtering approach that represents users and items as graph embeddings and effectively exploits the knowledge from these embeddings. First, the feature vectors for users and items are refined with multiple embedding propagation layers. We then use many dense layers to get extra information as much as possible, main purpose is to support embedding vectors in the propagation process and allow our model to better learning of the useritem interaction, making a noticeable difference in performance from prior works. Finally, we combine them before mapping to a predicted score. The proposed method has been empirically proven superior to the baselines and competitive with modern approaches on public benchmarks.


Interested students can register at: https://forms.gle/cis8fQdGW7ZBHsNh9 

Registration deadline: Until 3 PM Sunday, December 10, 2023


- Location: Room I.81 - 227 Nguyen Van Cu, District 5

- Number of participants: 15 students per session


Note: The registration link will close early when the maximum number of participants is reached.


Contact person: Ms. Nguyen Tran Thuc Uyen (Email: nttuyen@fit.hcmus.edu.vn)

Older Posts