Abstract
Social information helps the traditional recommendation system to provide more personalized services. Autoencoder provides an unsupervised methodology for coding and decoding input data. Autoencoder balances the sensitivity of the inputs to reconstruct the same output without considering its redundancies. Online social networks of people maximize their social influences. The social influence within a friend circle is calculated based on his/her social characteristics or behaviors on social platforms. Handling large unstructured data is one of the challenges for a friend recommendation system. This paper shows a friend recommendation system using an autoencoder based on the transfer learning method. It focuses on transfer learning to capture semantic social information. Autoencoder’s work can be improved by adding more hidden layers to the existing system for better and more accurate results. Transfer learning methods are combined with textual regularization. Users’ social features are extracted from social media platforms. User’s social interaction potential features are differentiated as common behavior or uncommon behavior. These behavioral analyzers are analyzed for better recommendations. The important characteristics of trustable people are synthesized. These characteristics are transferred to the target user’s characterization. Transfer learning saves the time to recalculate the feature predictions.
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Rao, B., Karande, A. (2023). Friend Recommendation System Using Transfer Learning in the Autoencoder. In: Misra, R., et al. Advances in Data Science and Artificial Intelligence. ICDSAI 2022. Springer Proceedings in Mathematics & Statistics, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-031-16178-0_10
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