Item-based collaborative filtering recommendation algorithm based on MapReduce

Hu, Weiming; Yang, Fanchang; Feng, Zhi Ming

doi:10.1201/b18512-22

Cited by 3 publications

(1 citation statement)

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“…Collaborative filtering recommendation algorithm is the most widely used recommendation algorithm. There are many kinds of collaborative filtering recommendation algorithms, and the most widely used are user-based collaborative filtering recommendation algorithms [ 16 ] (UB-CF) and item-based collaborative filtering recommendation algorithms [ 17 ] (IB-CF). Both algorithms are based on user item scoring tables, and the difference can be understood as follows: UB-CF corresponds to “groups of people,” and IB-CF corresponds to “birds of a feather flock together.” The difference is that UB-CF seeks the similarity of rows and IB-CF seeks the similarity of columns.…”

Section: Pertinent Techniquesmentioning

confidence: 99%

Personalized Item Recommendation Algorithm for Outdoor Sports

Lei

Shan

Jiang

2022

Computational Intelligence and Neuroscience

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With the rapid development of China’s economy, people are eager for an effective way to relieve work pressure and strengthen their health at the same time. Outdoor sport is one of the best choices for people. However, the amount of recommended data on the network is very large. As a result, when people understand outdoor sports through the network, they cannot effectively obtain the information they want. This is the problem of “information overload,” and personalized recommendation system can effectively alleviate this problem. In order to effectively recommend outdoor sports to users, a useful attempt was made in the personalized recommendation system for outdoor sports in this paper. The specific work of this paper is as follows: firstly, the current situation of outdoor sports in China was summarized, and the related technologies of the recommendation system were studied, including user modeling technology, recommendation target modeling technology, and recommendation algorithm. In order to obtain better recommendation effect, this paper proposes to mix user-based collaborative filtering recommendation algorithm, project-based collaborative filtering recommendation algorithm, and content-based recommendation algorithm. The hybrid algorithm adopts the way of feature expansion and weighted combination. Firstly, the hybrid model (model 1) of user-based collaborative filtering recommendation and content-based recommendation is obtained. Secondly, the hybrid model (model 2) based on project collaborative filtering recommendation and content-based recommendation was obtained. Finally, model 1 and model 2 were combined together to get a hybrid model with better final recommendation effect. For the common cold start problem in the recommendation system, the system adopts content-based recommendation algorithm to solve it.

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Section: Pertinent Techniquesmentioning

confidence: 99%