Missing Data Modeling based on Matrix Factorization of Implicit Feedback Dataset = 암시적 피드백 데이터의 행렬 분해 기반 누락 데이터 모델링

다국어 초록 (Multilingual Abstract)

Data sparsity is one of the main challenges for the recommender system. The recommender system contains massive data in which only a small part is the observed data and the others are missing data. Most studies assume that missing data is randomly missing from the dataset. Therefore, they only use observed data to train recommendation model, then recommend items to users. In actual case, however, missing data do not lost randomly. In our research, treat these missing data as negative examples of users' interest. Three sample methods are seamlessly integrated into SVD++ algorithm and then propose SVD++_W, SVD++_R and SVD++_KNN algorithm. Experimental results show that proposed sample methods effectively improve the precision in Top-N recommendation over the baseline algorithms. Among the three improved algorithms, SVD++_KNN has the best performance, which shows that the KNN sample method is a more effective way to extract the negative examples of the users' interest.

참고문헌 (Reference)

1 I. E. Kartoglu, "Two collaborative filtering recommender systems based on sparse dictionary coding" 57 (57): 709-720, 2018

2 H. S. Moon, "The impact of information amount on the performance of recommender systems" ACM 2016

3 R. Heckel, "The Sample Complexity of Online One-Class Collaborative Filtering"

4 V. Bajpai, "Survay Ppaer on Dynamic Recommendation System for e-Commerce" 9 (9): 774-777, 2018

5 D. Li, "Stable Matrix Approximation for Top-N Recommendation on Implicit Feedback Data" HICSS 1563-1572, 2018

6 W. Lu, "Recommender system based on scarce information mining" Elsevier 93 : 256-266, 2017

7 D. Jannach, "Recommendations with a purpose" ACM 7-10, 2016

8 D. -K. Chae, "On identifying k-nearest neighbors in neighborhood models for efficient and effective collaborative filtering" Elsevier 278 : 134-143, 2018

9 M. H. Abdi, "Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems : A Survey" 11 (11): 1-10, 2018

10 X. Zhao, "Improving top-N recommendation performance using missing data" 2015 : 380472-, 2015

11 Y. Koren, "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model" ACM 426-434, 2008

12 B. Marlin, "Collaborative filtering and the missing at random assumption"

13 I. Jordanov, "Classifiers Accuracy Improvement Based on Missing Data Imputation" 8 (8): 31-48, 2018