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

Xu Yu1, Jun-Yu Lin2, Feng Jiang1

  • 1School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China.

Computational Intelligence and Neuroscience
|April 7, 2018
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This summary is machine-generated.

This study introduces a new cross-domain collaborative filtering (CDCF) algorithm, FCLWLR, to improve recommendation systems. FCLWLR effectively transfers knowledge from auxiliary domains, overcoming data sparsity and enhancing accuracy.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Data sparsity is a major challenge in collaborative filtering (CF).
  • Cross-domain collaborative filtering (CDCF) leverages auxiliary domains to mitigate sparsity.
  • Existing CDCF methods struggle to effectively evaluate the importance of different auxiliary domains.

Purpose of the Study:

  • To propose a novel CDCF algorithm, Feature Construction and Locally Weighted Linear Regression (FCLWLR).
  • To effectively evaluate and utilize the significance of auxiliary domains in CDCF.
  • To address the data sparsity problem in recommendation systems.

Main Methods:

  • Feature construction to represent auxiliary domains.
  • Converting cross-domain recommendation into a regression problem.
  • Employing Locally Weighted Linear Regression (LWLR), a non-parametric method, to solve the regression problem.

Main Results:

  • FCLWLR effectively transfers knowledge from auxiliary domains.
  • The algorithm demonstrates superior performance in addressing data sparsity compared to state-of-the-art methods.
  • LWLR avoids underfitting and overfitting issues common in parametric regression.

Conclusions:

  • FCLWLR is an effective CDCF algorithm for tackling data sparsity.
  • The proposed method enhances recommendation accuracy by intelligently transferring knowledge across domains.
  • Feature construction and LWLR provide a robust framework for CDCF.