With the increase in numbers of multimedia technologies around us, movies and videos on social media and OTT platforms are growing, making it confusing for users to decide which one to watch for. For this, movie recommendation systems are widely used. It has been observed that two-thirds of the films watched on Netflix are the recommended ones to its users. The target of this work is to use implicit feedback given by other users to recommend movies, i.e., ratings given by them. Implicit feedback will help to enhance Data Sparsity as for a replacement logged-in user, the system won't have details of their past liked movies. So, matching the similarity with other users is often a plus point to recommend movies that they would like. The anticipated result will depend upon the positive attitude; i.e., if the predicted rating is high, then it'll be recommended; otherwise it'll not be recommended. The performance of the methodology is measured with accuracy and precision values for different strategies. It gives the best accuracy and highest precision values using Logistic Regression (LR) and lowest recall value as compared to other algorithms. This technique gives an accuracy, precision, and recall value of 81.9%, 69.82%, and 32.5%, respectively, using LR.

中文翻译：

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使用协同过滤方法解决数据稀疏性的电影推荐系统

随着我们周围多媒体技术数量的增加，社交媒体和 OTT 平台上的电影和视频越来越多，让用户在决定观看哪一个时感到困惑。为此，电影推荐系统被广泛使用。据观察，在 Netflix 上观看的电影中有三分之二是向其用户推荐的电影。这项工作的目标是使用其他用户给出的隐式反馈来推荐电影，即他们给出的评分。隐式反馈将有助于增强数据稀疏性，因为对于替换的登录用户，系统不会有他们过去喜欢的电影的详细信息。因此，将相似度与其他用户匹配通常是推荐他们喜欢的电影的加分点。预期的结果将取决于积极的态度；即，如果预测的评级很高，那么它' 会被推荐；否则不推荐。该方法的性能通过不同策略的准确度和精度值来衡量。与其他算法相比，它使用逻辑回归 (LR) 和最低召回值提供最佳准确度和最高精度值。该技术使用 LR 分别给出了 81.9%、69.82% 和 32.5% 的准确率、精确度和召回率值。