Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization

Luo, Xin; Xia, Yunni; Zhu, Qingsheng

doi:10.1016/j.knosys.2011.09.006

Cited by 178 publications

(86 citation statements)

References 16 publications

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“…Thus the approximated correlation coefficient is given by: i is a movie with a relatively high standard, so its rating is 0.5 stars higher than the average rating. In addition, the movie solves the problem by solving the least-squares problem [9][10][11] . The cost function formula is as follows: Because the proposed model of this paper is different from the traditional one, the data matrix cannot be directly applied to the training of the model.…”

Section: Improved Similarity Measuresmentioning

confidence: 99%

A Collaborative Filtering Recommendation Algorithm with Improved Similarity Calculation

Yang

Liu

Zhao

2018

International Journal of Advanced Network, Monitoring and Controls

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Abstract-In order to improve the accuracy of the proposed algorithm in collaborative filtering recommendation system, an Improved Pearson collaborative filtering (IP-CF) algorithm is proposed in this paper. The algorithm uses the user portrait, item characteristics and data of user behavior to compute the baseline predictors model. Instead of the traditional algorithm's similarity calculation, the prediction model is used to improve the accuracy of the recommendation algorithm. Experimental results on Moivelens dataset show that the IP-CF algorithm significantly improves the accuracy of the recommended results, and the RMSE and MAE evaluation results are better than the traditional algorithms.

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Section: Improved Similarity Measuresmentioning

confidence: 99%

A Collaborative Filtering Recommendation Algorithm with Improved Similarity Calculation

Yang

Liu

Zhao

2018

International Journal of Advanced Network, Monitoring and Controls

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“…A probabilistic latent semantic analysis is used to extract the latent features of the historical rating data. Luo et al [11] implemented an incremental CF recommender system based on Regularized Matrix Factorization. This method supports incremental updates for the trained parameters as new ratings arrive.…”

Section: Model-based Cfmentioning

confidence: 99%

Adaptive Collaborative Filtering Based on Scalable Clustering for Big Recommender Systems

2016

APH

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“…The Netflix price was won by a team that proposed a factorization algorithm, [22]. The scalability of these algorithms was emphasized in [23]. However, recommenders was not the only application tackled with a factorization approach; in [24], the authors presented an algorithm for dimensionality reduction in Machine Learning.…”

Section: Related Workmentioning

confidence: 99%

A factorization approach to evaluate open-response assignments in MOOCs using preference learning on peer assessments

Luaces¹,

Díez²,

Alonso‐Betanzos³

et al. 2015

Knowledge-Based Systems

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Evaluating open-response assignments in Massive Open Online Courses is a difficult task because of the huge number of students involved. Peer grading is an effective method to address this problem. There are two basic approaches in the literature: cardinal and ordinal. The first case uses grades assigned by student-graders to a set of assignments of other colleagues. In the ordinal approach, the raw materials used by grading systems are the relative orders that graders appreciate in the assignments that they evaluate. In this paper we present a factorization method that seeks a trade-off between cardinal and ordinal approaches. The algorithm learns from preference judgments to avoid the subjectivity of the numeric grades. But in addition to preferences expressed by student-graders, we include other preferences: those induced from assignments with significantly different average grades. The paper includes a report of the results obtained using this approach in a real world dataset collected in 3 Universities of Spain, A Coruña, Pablo de Olavide at Sevilla, and Oviedo at Gijón. Additionally, we studied the sensitivity of the method with respect to the number of assignments graded by each student. Our method achieves similar or better scores than staff instructors when we measure the discrepancies with other instructor's grades. * Corresponding author: Tel: +34 985 182 028

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Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization

Cited by 178 publications

References 16 publications

A Collaborative Filtering Recommendation Algorithm with Improved Similarity Calculation

A Collaborative Filtering Recommendation Algorithm with Improved Similarity Calculation

Adaptive Collaborative Filtering Based on Scalable Clustering for Big Recommender Systems

A factorization approach to evaluate open-response assignments in MOOCs using preference learning on peer assessments

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