A Collaborative Filtering Recommendation Algorithm Based on Adjusted Cosine Similarity

doi:10.3969/j.issn.1006-2475.2020.01.013

Abstract

Abstract: In the traditional collaborative filtering recommendation algorithm, there exists a problem of low accuracy of classification in the case of sparse data. To solve this problem, a collaborative filtering recommendation algorithm based on adjusted cosine similarity is proposed. Converting the data to the feature matrix by the embedded layer, the mean square error between the adjusted cosine similarity matrix obtained by the calculation and the unit matrix is used as the loss function for improving the accuracy of classification of the proposed algorithm in the case of sparse data. Experimental results indicate that, compared to the FM, FFM and DeepFM models, AUC and the Logloss of the proposed collaborative filtering recommendation algorithm based on adjusted cosine similarity are better within the acceptable range of training time.

Key words: adjusted cosine similarity, collaborative filtering, recommendation algorithm, deep factorization machine

CLC Number:

TP305

LI Yi-ye 1,2, DENG Hao-jiang1,2. A Collaborative Filtering Recommendation Algorithm Based on Adjusted Cosine Similarity[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2020.01.013.

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