Joemon M. Jose

A Simple Convolutional Generative Network for Next Item Recommendation

2019

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into …

Self-Supervised Reinforcement Learning for Recommender Systems

2020 · arXiv (Cornell University)

In session-based or sequential recommendation, it is important to consider a number of factors like long-term user engagement, multiple types of user-item interactions such as clicks, purchases etc. The current state-of-the-art supervised approaches fail to …

Learning Robust Recommenders through Cross-Model Agreement

2022 · Proceedings of the ACM Web Conference 2022

Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while negative examples are sampled from uninteracted ones. However, noisy …

Capturing the Spectrum of Social Media Conflict: A Novel Multi-objective Classification Model

2024

Social media has emerged as a widespread phenomenon, with numerous users engaging in observing, creating, and distributing content. The growing content has led to user conflicts, encompassing bullying, aggression, harassment, and threats. Consequently, recent research …

Relational Collaborative Filtering

2019

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity - i.e., the item similarity evidenced by user interactions like ratings and purchases. Nevertheless, there exist multiple relations between items in real-world …