DotMat: Solving Cold-start Problem and Alleviating Sparsity Problem for Recommender Systems
Cold-start and sparsity problem are two key intrinsic problems to recommender systems. During the past two decades, researchers and industrial practitioners have spent considerable amount of efforts trying to solve the problems. However, for cold-start problem, most research relies on importing side information to transfer knowledge. A notable exception is ZeroMat, which uses no extra input data. Sparsity is a lesser noticed problem. In this paper, we propose a new algorithm named DotMat that relies on no extra input data, but is capable of solving cold-start and sparsity problems. In experiments, we prove that like ZeroMat, DotMat can achieve competitive results with recommender systems with full data, such as the classic matrix factorization algorithm.
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsSimilar Papers 제목 키워드 기반
PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data
Matrix Factorization is one of the most successful recommender system techniques over the past decade. However, the classic probabilistic theory framework for matrix factorization is modeled using normal distributions. T…
Recommendation SystemsAlleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alleviating over-fits caused by data-sparsit…
Click-Through Rate PredictionVariational InferencePowerMat: context-aware recommender system without user item rating values that solves the cold-start problem
Recommender systems serves as an important technical asset in many modern companies. With the increasing demand for higher precision of the technology, more and more research and investment has been allocated to the fiel…
Recommendation SystemsLogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models
Recommender system is adored in the internet industry as one of the most profitable technologies. Unlike other sectors such as fraud detection in the Fintech industry, recommender system is both deep and broad. In recent…
Fraud DetectionMeta-LearningRecommendation SystemsTransfer LearningAlleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
Solving cold-start problems is indispensable to provide meaningful recommendation results for new users and items. Under sparsely observed data, unobserved user-item pairs are also a vital source for distilling latent us…