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Resolving cold start and sparse data challenge in recommender systems using multi-level singular value decomposition
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AI
DOI：10.1016/j.compeleceng.2021.107361.png)
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Abstract
En 中文
Recommender systems estimate users' tendency and recommend a list of suitable items. Two fundamental challenges in recommender systems include cold start and sparse data. In order to overcome these challenges, employing contextual similarity measures, utilizing the features of the users and items, and applying machine learning methods have been presented. However, a method called the context feature singular value decomposition is presented as the first step. In this method, the user-context feature matrix, the item-context feature matrix, and the context similarity matrix are created to mitigate cold start. In the second step, matrices obtained in the previous step are applied as components of a multi-level singular value decomposition matrix and momentum stochastic gradient descent feature to reduce sparse data. The results obtained from the presented method are compared to those of existing approaches, indicating that the proposed method improves the accuracy of suggested entities in recommender systems
Keywords:
Recommender systems
Singular value decomposition
Contextual information
Cold start
Sparse data
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