CS6101

Conversational Recommendation Systems

Participation

26 students in 10 teams

Guest Registration

1419 guests registered

Projects

CS6101-01

Explore Multiple Response Modalities of DialogWAE

This project is focusing on assessing and interpreting the GMM prior components in DialogWAE

CS6101-02

FiBiNET

Combining feature importance and bilinear feature interaction for click-through rate prediction.

CS6101-03

Feedback-guided Preference Adaptation Network (FPAN)

Multi-round conversational recommender systems (CRS), which interact with users by asking questions about attributes and recommending items multiple times in one conversation.

CS6101-04

Causal Estimation for Conversational Recommender System

In this project, we study popularity bias in Recommender System (RecSys), Conversational Recsys, and their interplays.

CS6101-05

Counterfactual Recommender

Relevance Matrix Factorization and Asymmetric Tri-training are employed to build a recommendation system. Its algorithm is evaluated by using Coat dataset to simulate a scenario that we have only “biased” observational data for model training while evaluating on “unbiased” data.

CS6101-06

Diversifying Dialogue Generation with Non-Conversational Text

Implementation for the paper Diversifying Dialogue Generation with Non-Conversational Text on English

CS6101-07

Extending Neural Collaborative Filtering

Extending the Neural Collaborative Filtering Framework to improve model understanding and robustness. Using additional Convolutional layers, Pairwise Loss Function and Auxiliary Information Embedding to explore potential model improvements.

CS6101-08

NN for Ad Recommendation

The project extends the popular DeepFM neural network to better predict users' click-through-rate of advertisements.

CS6101-09

Beyond IGMC

We extend the state-of-the-art Inductive Graph Matrix Completion recommender system by introducing Graph Normalization and Layer Aggregation variants, and explore the models' potent transfer learning capabilities.

CS6101-10

KGRecSys

Implementation of the paper "KGAT: Knowledge Graph Attention Network for Recommendation"

Comments

Chan Guan Hao4 years ago

IGMC is the best project! Interesting GNN extensions :)

"Tan Hin Khai, Stephen"4 years ago

IGMC has a very interesting and compelling use-case

BEH KHA SIM4 years ago

Eduscape has a really big potential for growth and expansion.