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| Cross-Domain Recommendation Combining Heterogeneous Information Network Representation Learning |
| Yi Ming, Liu Ming, Feng Cuicui |
| School of Information Management, Central China Normal University, Wuhan 430079 |
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Abstract The propose of this study was to put forward a cross-domain recommendation model that uses rating information and feature information to solve the data sparseness and user's cold-start problems in a single domain. The heterogeneous network representation learning framework, which focuses on source domain and target domain, was used to generate network embedding via the meta-path and DeepWalk algorithms. Then, personalized nonlinear fusion was used to output the feature information embedding of the source and target domains. Moreover, a neural network was used to generate rating information embedding for users and items by simulating collective matrix factorization (CMF). After that, a mapping function named multilayer perceptron (MLP) was introduced to highlight the differences of user features in different domains. Based on the loss function and learning the parameters of the model, the gradient descent method was used to predict user ratings for items. The experimental results showed that the cross-domain recommendation model combining heterogeneous information network embedding achieved excellent results in improving the recommendation effect and solving the user's cold-start problem on both the Douban and Amazon datasets. In terms of improving the recommendation effect, root mean squared error (RMSE) and mean absolute error (MAE) in the target domain dropped by 1%-15%, and in the source domain they dropped by 1%-19%. In terms of the user's cold-start problem in the target domain, RMSE and MAE dropped by 1%-14%.
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Received: 14 January 2021
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