A new prediction approach based on linear regression for collaborative filtering

2011 ◽  
Author(s):  
Xinyang Ge ◽  
Jia Liu ◽  
Qi Qi ◽  
Zhenyu Chen
Keyword(s):  
Linear Regression ◽  
Collaborative Filtering ◽  
Prediction Approach

scholarly journals A Score Prediction Approach for Optional Course Recommendation via Cross-User-Domain Collaborative Filtering

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 19550-19563 ◽  
Author(s):  
Ling Huang ◽  
Chang-Dong Wang ◽  
Hong-Yang Chao ◽  
Jian-Huang Lai ◽  
Philip S. Yu
Keyword(s):  
Collaborative Filtering ◽  
Prediction Approach

scholarly journals A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xu Yu ◽  
Jun-yu Lin ◽  
Feng Jiang ◽  
Jun-wei Du ◽  
Ji-zhong Han
Keyword(s):  
Linear Regression ◽  
Collaborative Filtering ◽  
Feature Construction ◽  
Target Domain ◽  
Regression Problem ◽  
Filtering Algorithm ◽  
Weighted Linear Regression ◽  
Cross Domain ◽  
Collaborative Filtering Algorithm ◽  
Sparsity Problem

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

scholarly journals Smart Yield Accuracy Prediction using Linear Regression and Collaborative Filtration

2019 ◽  
Vol 8 (3) ◽  
pp. 664-670
Keyword(s):  
Linear Regression ◽  
Soil Fertility ◽  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
Time Data ◽  
Real Time Data ◽  
Crops Yield

India is mainly based on farming. Agriculture is the main source of economy in India, but the farmers are suffering with many problems such as lack of crops yield, lack of water, soil fertility etc. To address those issues this recommendation system is proposed, and it significantly influences the crops yields. The need for the accessible data on the accomplishment for getting crops in good yields are investigated. To accomplish that, real-time data are collected from the farmers from different places of Karnataka. In this paper linear regression and collaborative filtering are used, and results are compared to draw an inference for more accurate recommendation system.

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