Earticle

초록

영어

Collaborative filtering algorithm is the most used items recommendation algorithm. We find the k neighbors with the highest similarity by calculating user similarity and recommend items for users by the score of the neighbors of the items. In the paper, we propose a hybrid recommendation algorithm based on user similarity and attribute weights to solve user ratings sparsity. We obtained the weights of users like properties through learning user ratings records and combined with the user similarity for users to recommend item. Finally, we transplant the algorithm to HADOOP platform. Through the experiment, the improved collaborative filtering algorithm is better than the original algorithm in precision and parallel attribute.

목차

Abstract
 1. Introduction
 2. Steps of User-Based Collaborative Filtering Algorithm
  2.1. Expression of User Information
  2.2. Calculation of Similarity
 3. Prediction Recommendation Algorithm Based on Attribute Weight
 4. Combination Recommendation Algorithm Based on User Similarity and Attribute Value Prediction
 5. Parallel Improvement
  5.1. Data Combing
  5.2. Prediction of Rating
 6 Experiment Design and Discussion
  6.1. Experimental Data
  6.2. Evaluation Criteria
  6.3. Selection of Similarity Formula
  6.4. MAE Comparison of Algorithms
 7. Conclusion
 References

키워드

저자

• Jingxia Guo [ Bao Tou Medical College, BaoTou 014060, china ]
• Jinniu Bai [ Bao Tou Medical College, BaoTou 014060, china ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605