Abstract
Recommending appropriate products has become the key to success of E-commerce systems. Collaborative filtering (CF) is the most widely used technology for recommender systems. However, it has several problems such as cold start problem that occurs due to insufficient data, lack of personalisation and scalability. The proposed method is a personalised structure balance theory-based hybrid method (personalised SBT), which attempts to solve the mentioned drawbacks of the traditional CF based system. The problem caused by the lack of data is handled by the application of rules of SBT. The system develops models of user preferences and product features, which are used for personalised recommendation. As it is a model-based system, it is highly scalable to handle Big Data. The process is implemented on Movie Lens-1M dataset, and the rating data of the dataset is used to develop the models that are used for recommendation. Mean Average Error (MAE) is used to evaluate the accuracy of the predicted rating, and Recall is used to evaluate the efficiency of the recommendation.
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Acknowledgements
This research work was funded by the Department of Science and Technology, India, under the project ‘Design and Development of ICT-Enabled Cloud based mobile application for the self-promotion of products developed by Self Help Groups’.
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Sivakumar, A., Amedapu, N., Avuthu, V., Brindha, M. (2021). Personalised Structure Balance Theory-Based Movie Recommendation System. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_5
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DOI: https://doi.org/10.1007/978-981-15-5788-0_5
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