원문정보
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
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