Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm...

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Bibliographic Details
Published in:Applied sciences 2021-10, Vol.11 (20), p.9554
Main Authors: Ni, Jianjun, Cai, Yu, Tang, Guangyi, Xie, Yingjuan
Format: Article
Language:English
Subjects:
Accuracy
Adaptive algorithms
Algorithms
Cold
Collaboration
collaborative recommendation
Filtration
fuzzy membership function
Methods
Ratings & rankings
Recommender systems
Similarity
Sparsity
TF-IDF method
user characteristics
weighted fusion
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Summary:The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11209554