Search Results for author:
Found 12 papers, 2 papers with code
Towards a Theoretical Understanding of Two-Stage Recommender Systems
Production-grade recommender systems rely heavily on a large-scale corpus used by online media services, including Netflix, Pinterest, and Amazon.
FakeClaim: A Multiple Platform-driven Dataset for Identification of Fake News on 2023 Israel-Hamas War
We contribute the first publicly available dataset of factual claims from different platforms and fake YouTube videos on the 2023 Israel-Hamas war for automatic fake YouTube video classification.
Towards Subject Agnostic Affective Emotion Recognition
Our domain adaptation approach is augmented through meta-learning, which consists of a recurrent neural network, a classifier, and a distributional shift controller based on a sum-decomposable function.
A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings
However, these methods lack a modelling mechanism to directly reflect user interests within the learned item representations.
Lightweight Adaptation of Neural Language Models via Subspace Embedding
Traditional neural word embeddings are usually dependent on a richer diversity of vocabulary.
A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease
We chose SCD as a model disease condition due to the presence of diverse erythrocyte morphologies in the blood samples of SCD patients.
Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages
This paper presents the HASOC subtrack for English, Hindi, and Marathi.
Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages
HASOC has two sub-task for all three languages: task A is a binary classification problem (Hate and Not Offensive) while task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY.
Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach
Understanding an information forager's actions during interaction is very important for the study of interactive information retrieval.
Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation
In the first part, we hypothesise that the users' perception is improved with visual cues in the images as behavioural signals that provide users' information scent during information seeking.
Effects of Foraging in Personalized Content-based Image Recommendation
We investigate a personalized content-based image recommendation system to understand what affects user attention by reinforcing visual attention cues based on IFT.
Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
Pathologists find tedious to examine the status of the sentinel lymph node on a large number of pathological scans.
