Cold Start Problem

The cold start problem in machine learning refers to the difficulty of making accurate predictions or recommendations when limited data is available for new users, items, or contexts. Current research focuses on mitigating this challenge through various approaches, including improved algorithms like variations of Upper Confidence Bound (UCB) and the incorporation of transfer learning, meta-learning, and self-supervised learning techniques to leverage existing knowledge and side information. Addressing the cold start problem is crucial for enhancing the performance and usability of numerous applications, such as recommender systems, content generation, and automatic speech scoring, ultimately improving user experience and efficiency in data-driven systems.

Papers

November 10, 2024

Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues
Yuanshuai Luo, Rui Wang, Yaxin Liang, Ankai Liang, Wenyi Liu
Metric Learning Recommendation Algorithm Recommendation Quality Traditional Recommender Cold Start Problem Content Based Recommendation Tag Aware Recommendation

November 3, 2024

G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning
Yahel Jacobs, Reut Dayan, Uri Shaham
Graph Drawing Link Prediction Graph Representation Graph Learning Spectral Embedding Cold Start Cold Start Problem

April 16, 2024

HELLINGER-UCB: A novel algorithm for stochastic multi-armed bandit problem and cold start problem in recommender system
Ruibo Yang, Jiazhou Wang, Andrew Mullhaupt
Recommender System Multi Armed Bandit Wasserstein Distance Novel Algorithm Confidence Bound Upper Confidence Bound Cold Start Problem

February 15, 2024

Zero-Shot Reasoning: Personalized Content Generation Without the Cold Start Problem
Davor Hafnar, Jure Demšar
Complex Reasoning Procedural Content Generation Game Content Game Data Cold Start Problem

August 16, 2023

Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?
Davide Buffelli, Ashish Gupta, Agnieszka Strzalka, Vassilis Plachouras
Recommender System Constructive Approach Real World Meta Learning Cold Start Problem

June 25, 2023

Addressing Cold Start Problem for End-to-end Automatic Speech Scoring
Jungbae Park, Seungtaek Choi
Prompt Embeddings Question Representation Cold Start Problem Speech Scoring

May 17, 2023

Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning
Evelyn J. Mannix, Howard D. Bondell
Semi Supervised Learning Self Supervised Model Novel Class Discovery Cold Start Problem

December 6, 2022

PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data
Hao Wang
Recommender System Probabilistic Model Matrix Factorization Poisson Noise Recommendation Problem Cold Start Problem

October 5, 2022

Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
Liangyu Chen, Yutong Bai, Siyu Huang, Yongyi Lu, Bihan Wen, Alan L. Yuille, Zongwei Zhou
Contrastive Learning Active Learning Value Laden Choice Active Vision Efficient Annotation Contrastive Feature Cold Start Problem

April 27, 2022

Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems
Yuanbo Xu, Yongjian Yang, En Wang
Recommender System Cold Start Problem

March 20, 2022

Cluster & Tune: Boost Cold Start Performance in Text Classification
Eyal Shnarch, Ariel Gera, Alon Halfon, Lena Dankin, Leshem Choshen, Ranit Aharonov, Noam Slonim
Pre Trained Model Text Classification Cluster Structure Text Classification Task Hyper Tune Topic Classification Cold Start Problem

January 17, 2022

Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
Xiaoxiao Xu, Chen Yang, Qian Yu, Zhiwei Fang, Jiaxing Wang, Chaosheng Fan, Yang He, Changping Peng, Zhangang Lin, Jingping Shao
Variational Inference Variational Method Learning Framework CTR Prediction Probabilistic Embeddings Cold Start Problem

January 16, 2022

Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferring
Tri Minh Nguyen, Thin Nguyen, Truyen Tran
Drug Discovery Drug Target Interaction Drug Target Cold Start Problem Knowledge Interaction Drug Target Affinity Prediction

Cold Start Problem

Papers

Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues

G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning

HELLINGER-UCB: A novel algorithm for stochastic multi-armed bandit problem and cold start problem in recommender system

Zero-Shot Reasoning: Personalized Content Generation Without the Cold Start Problem

Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?

Addressing Cold Start Problem for End-to-end Automatic Speech Scoring

Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning

PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data

Making Your First Choice: To Address Cold Start Problem in Vision Active Learning

Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems

Cluster & Tune: Boost Cold Start Performance in Text Classification

Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework

Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferring