<abstract xmlns="http://www.w3.org/1999/xhtml"><p>With the advent of the era of big data, our lives generate huge amounts of data every day, and the field of e-commerce is no exception. It is particularly important to analyze these data and recommend products. It is reported that through the recommendation algorithm, Amazon has increased its sales by about 30%. Among the recommended algorithms, the collaborative filtering algorithm is currently relatively mature and has achieved very good results in various fields. But the traditional collaborative filtering algorithm is too rough when calculating the similarity and prediction score, and the efficiency is very low. We combine the traditional collaborative filtering algorithm with the decision tree algorithm, and improve the traditional recommendation algorithm, create a collaborative filtering decision tree algorithm to recommend products, and run the new collaborative filtering decision tree algorithm on the Hadoop platform on. Experiments show that the improved algorithm makes the accuracy of recommendation significantly improved.</p></abstract>

With the advent of the era of big data, our lives generate huge amounts of data every day, and the field of e-commerce is no exception. It is particularly important to analyze these data and recommend products. It is reported that through the recommendation algorithm, Amazon has increased its sales by about 30%. Among the recommended algorithms, the collaborative filtering algorithm is currently relatively mature and has achieved very good results in various fields. But the traditional collaborative filtering algorithm is too rough when calculating the similarity and prediction score, and the efficiency is very low. We combine the traditional collaborative filtering algorithm with the decision tree algorithm, and improve the traditional recommendation algorithm, create a collaborative filtering decision tree algorithm to recommend products, and run the new collaborative filtering decision tree algorithm on the Hadoop platform on. Experiments show that the improved algorithm makes the accuracy of recommendation significantly improved.

Research on Commodity Mixed Recommendation Algorithm

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research on Commodity Mixed Recommendation Algorithm"}

With the advent of the era of big data, our lives generate huge amounts of data every day, and the field of e-commerce is no exception. It is particularly...

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Research on Commodity Mixed Recommendation Algorithm

Online veröffentlicht: 14. Okt. 2020

Seitenbereich: 1 - 8

DOI: https://doi.org/10.21307/ijanmc-2020-021

Schlüsselwörter
E-Commerce, Recommendation Algorithm, Decision Tree, Collaborative Filtering

© 2020 Hao Chang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

RI VALUE TABLE

EXPERIMENTAL RESULTS OF FUSION ALGORITHM

RANDOM FOREST MODEL FINAL EXPERIMENTAL RESULTS

USER BEHAVIOR WEIGHT

Research on Commodity Mixed Recommendation Algorithm

Online veröffentlicht: 14. Okt. 2020

Seitenbereich: 1 - 8

DOI: https://doi.org/10.21307/ijanmc-2020-021

SchlüsselwörterE-Commerce, Recommendation Algorithm, Decision Tree, Collaborative Filtering

© 2020 Hao Chang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

RI VALUE TABLE

EXPERIMENTAL RESULTS OF FUSION ALGORITHM

RANDOM FOREST MODEL FINAL EXPERIMENTAL RESULTS

USER BEHAVIOR WEIGHT

Schlüsselwörter
E-Commerce, Recommendation Algorithm, Decision Tree, Collaborative Filtering