Abstract
The main aim of this work is to build a multi-agent based recommender system that comprises heterogeneous software agents, where various member agents interact among themselves to accomplish various tasks and achieve objectives of the system. The main objectives of this work are: first, it presents some basics of multi-agent based recommender system. Second, it reviews the main research developments and works previously performed in the field of recommender systems with machine learning. Third, it introduces a multi-agent based recommender system framework based on a collaborative approach by considering various agents to recommend movies to users, who have similar interests. Furthermore, the proposed framework has been experimentally assessed by implementing a cosine similarity algorithm to measure user–user similarity based on movie ratings. The multi-agent programming environment NetLogo is used to simulate the results.
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Kaur, H., Kaur, H., Singh, A. (2020). Multi-agent Based Recommender System for Netflix. In: Dutta, M., Krishna, C., Kumar, R., Kalra, M. (eds) Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India. Lecture Notes in Networks and Systems, vol 116. Springer, Singapore. https://doi.org/10.1007/978-981-15-3020-3_20
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