Abstract
The production, promotion, and distribution of content in the electronic media and entertainment sector must adapt to new strategies. The reason for this is that current customers can search for and access content on any device, at any time, from anywhere. The world has entered a media-rich information period due to the proliferation of online services and flexible innovations. An efficient recommender system always makes sure to record the users’ actual preferences and only makes recommendations for items that the user genuinely wants. Recommender systems have been used for two decades to recommend goods, contents, and services to online users in a variety of applications. Although recommender systems have been successful in many application domains, there are still a number of problems that limit their effectiveness. This paper suggests a hybridized algorithm for a movie recommendation system that uses a particle swarm optimization-based crow search algorithm. The suggested model considered both the most recent and previous user ratings and conducted statistical analysis on a real dataset. Additionally, when performing particle swarm optimization, the items’ contents are taken into consideration. As a result, the recommender system’s data sparsity issue is greatly diminished. More individualized movie track recommendations are made possible by the concept of hybridization.
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Dataset: MovieLens available on https://grouplens.org/datasets/movielens/tag-genome-2021. Last accessed September 01, 2022.
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Agarwal, G., Dinkar, S.K., Agarwal, A. (2023). An Intelligent Framework for Movie Recommendation Through Online Social Media. In: Sharma, D.K., Peng, SL., Sharma, R., Jeon, G. (eds) Micro-Electronics and Telecommunication Engineering . Lecture Notes in Networks and Systems, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-19-9512-5_59
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DOI: https://doi.org/10.1007/978-981-19-9512-5_59
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