Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction
Recommender system recommends interesting items to users based on users' past information history. Researchers have been paying attention to improvement of algorithmic performance such as MAE and precision@K. Major techniques such as matrix factorization and learning to rank are optimized based on such evaluation metrics. However, the intrinsic Matthew Effect problem poses great threat to the fairness of the recommender system, and the unfairness problem cannot be resolved by optimization of traditional metrics. In this paper, we propose a novel algorithm that incorporates Matthew Effect reduction with the matrix factorization framework. We demonstrate that our approach can boost the fairness of the algorithm and enhances performance evaluated by traditional metrics.
Code (1)
Tasks
FairnessLearning-To-RankRecommendation SystemsSimilar Papers 제목 키워드 기반
RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement
Matrix Factorization is a widely adopted technique in the field of recommender system. Matrix Factorization techniques range from SVD, LDA, pLSA, SVD++, MatRec, Zipf Matrix Factorization and Item2Vec. In recent years, di…
FairnessRecommendation SystemsWord EmbeddingsPoissonMat: 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 SystemsApproximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior
We derive analytical expression of matrix factorization/completion solution by variational Bayes method, under the assumption that observed matrix is originally the product of low-rank dense and sparse matrices with addi…
Matrix CompletionOnline Matrix Factorization via Broyden Updates
In this paper, we propose an online algorithm to compute matrix factorizations. Proposed algorithm updates the dictionary matrix and associated coefficients using a single observation at each time. The algorithm performs…
PowerMat: 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 Systems