Movie Recommendation System to Solve Data Sparsity Using Collaborative Filtering Approach

Abstract

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.

Prediction of Students’ Grade by Combining Educational Knowledge Graph and Collaborative Filtering

“…Collaborative Filtering Recommendation Algorithm includes User-Based Collaborative Filtering [23] and Item-Based Collaborative Filtering [24], which are widely used in various fields such as video, e-commerce websites, music, and education recommendations. The main idea of this algorithm is to calculate the similarity between users or items based on users' historical rating records for items.…”

Heterogeneous Multi-Behavior Recommendation Based on Graph Convolutional Networks

“…Traditional recommendation models [1][2][3] consider single behavior type of user-item interaction. In order to predict user preferences in a single-behavior user data, many traditional recommendation models approaches are devoted to Collaborative Filtering (CF) [4][5][6].…”