Abstract
In recent years, with the increasing importance of education and the development of technology such as the internet and big data, more and more researchers have applied big data research to the field of education. Although big data contains high value, it can easily lead to information overload. Therefore, researchers have developed an education recommendation system that combines education with big data to find valuable information from massive data. Based on the user's historical behavior, the recommendation system can find out the user's interest characteristics, analyze the user's needs and interests, and thereby recommend content that the user is interested in. But when it comes to new users or new products, the recommendation system can not find information about their preferences, which can easily cause data sparseness and cold start problem. In this paper, we propose a cold-start recommendation model for interests, which can enhance the usability of user-related data. The model first uses the Pearson coefficient to calculate the learning interest perception relationship between users with temporal context. Second, based on this model, we use neural network to analyze the semantics of relevant data. In order to find suitable relevant data for recommendation, we use the KNN algorithm by reducing the number of neural network outputs. Finally, the experimental results are proved from the real data of Douban book, experiments show that cold-start recommendation model for learning interests solves the problem of data sparseness and cold start to a certain extent that is compared with the traditional recommendation model based on collaborative filtering algorithm.
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Acknowledgement
This research was supported by the National Key Research and Development Program of China under Grant No. 2019YFB1406002, the Joint Funds of the National Natural Science Foundation of China under Grant No. U1811261, the National Natural Science Foundation of China No. 61702345, 61702346, 61702381, the Project of Liaoning Provincial Public Opinion and Network Security Big Data System Engineering Laboratory.
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Du, Y., Yan, T., Li, X., Zhou, J., Wang, Y., Shan, J. (2021). A Learning Interests Oriented Model for Cold Start Recommendation. In: Chen, Q., Li, J. (eds) Web and Big Data. APWeb-WAIM 2020 International Workshops. APWeb-WAIM 2020. Communications in Computer and Information Science, vol 1373. Springer, Singapore. https://doi.org/10.1007/978-981-16-0479-9_7
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DOI: https://doi.org/10.1007/978-981-16-0479-9_7
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