Search Results for author:
Found 11 papers, 2 papers with code
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning
In practical scenarios, the effectiveness of sequential recommendation systems is hindered by the user cold-start problem, which arises due to limited interactions for accurately determining user preferences.
A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management
We have proposed a new method using experts signals and historical price data to feed into our reinforcement learning framework.
Multi-View Approach to Suggest Moderation Actions in Community Question Answering Sites
Our results clearly demonstrate the high influence of our feature generation components on the overall success of the classifier.
Tag Recommendation for Online Q&A Communities based on BERT Pre-Training Technique
Online Q&A and open source communities use tags and keywords to index, categorize, and search for specific content.
Ranked #1 on
Multi-Label Text Classification
on Freecode
Using Experts' Opinions in Machine Learning Tasks
In machine learning tasks, especially in the tasks of prediction, scientists tend to rely solely on available historical data and disregard unproven insights, such as experts' opinions, polls, and betting odds.
A Scalable Architecture for Monitoring IoT Devices Using Ethereum and Fog Computing
Monitoring this huge number of IoT devices that are heterogeneous in terms of underlying communication protocols and data format is challenging.
Distributed, Parallel, and Cluster Computing
Predicting Subjective Features of Questions of QA Websites using BERT
Community Question-Answering websites, such as StackOverflow and Quora, expect users to follow specific guidelines in order to maintain content quality.
Ranked #1 on
Question Quality Assessment
on CrowdSource QA
A Comprehensive Analysis of Twitter Trending Topics
In Twitter, a name, phrase, or topic that is mentioned at a greater rate than others is called a "trending topic" or simply "trend".
Hybrid-Learning approach toward situation recognition and handling
Obtaining knowledge from the environment is often through sensors, and the response to a particular circumstance is offered by actuators.
Learning an Integrated Distance Metric for Comparing Structure of Complex Networks
According to natural witnesses of network similarities (such as network categories) the distance metric is learned by the means of a dataset of some labeled real networks.
Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier
Real networks exhibit nontrivial topological features such as heavy-tailed degree distribution, high clustering, and small-worldness.
