Movie Recommender System Using K-Nearest Neighbors Variants

Airen, Sonu; Agrawal, Jitendra

doi:10.1007/s40009-021-01051-0

Cited by 22 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second component applies the mentioned calculated model to acquire top-K recommendations for a user. This method also shows a high performance in comparison to other similar CF approaches [60][61][62].…”

mentioning

confidence: 74%

A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

2022

View full text Add to dashboard Cite

Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over traditional methods such as matrix factorization due to their ability to represent the complex relationships between users and items and to incorporate additional information. The fact that these data have a graph structure and the greater capability of Graph Neural Networks (GNNs) to learn from these structures has led to their successful incorporation into recommender systems. However, the bias amplification issue needs to be investigated while using these algorithms. Bias results in unfair decisions, which can negatively affect the company’s reputation and financial status due to societal disappointment and environmental harm. In this paper, we aim to comprehensively study this problem through a literature review and an analysis of the behavior against biases of different GNN-based algorithms compared to state-of-the-art methods. We also intend to explore appropriate solutions to tackle this issue with the least possible impact on the model’s performance.

show abstract

mentioning

confidence: 74%

A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

2022

View full text Add to dashboard Cite

show abstract

“…We use two notable algorithms for collaborative filtering. The first algorithm for collaborative filtering is the KNN classifier [60][61][62]. The KNN algorithm is one example of lazy learning since it memorizes the data sets instead of learning a discriminative function from the data sets.…”

Section: Collaborative Filteringmentioning

confidence: 99%

Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students

Kim,

Lim

2023

Applied Sciences

View full text Add to dashboard Cite

Various services and applications based on information and communications technology (ICT) are converging with cultural aspects of historical implementations. At the same time, developing a convergence course for non-ICT majors is becoming increasingly popular in universities. In this paper, we develop an AI application course for non-ICT major university students toward convergence with recommendation systems and Silk Road studies. Based on our five-year research on the martial arts, dance, and play of seven Silk Road countries, we have created and categorized an accessible database for 177 items in those countries. For our convergence course, we measure the similarity between the items for summary and perform collaborative filtering based on alternating least squares (ALS) matrix factorization so that our prototyped intelligent recommendation engine can predict the items in which a user might be interested. The course is designed to teach non-ICT major university students not only historical aspects of the Silk Road but also implementation aspects of recommendation systems with web services.

show abstract

“…For the movie RecSys, several modifications of the KNN method with various similarity metrics have been presented in [49]. These various KNN method modifications have been applied to real data from the MovieLens dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing a Personalized E-Learning and MOOC Recommender System in IoT-Enabled Smart Education

Amin,

Uddin,

Mashwani

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Smart strategies and intelligent technologies are enabling the designing of a smart learning environment that successfully supports the development of personalized learning and adaptive learning. This trend towards integration is in line with the growing prevalence of Internet of Things (IoT)-enabled smart education systems, which can leverage Machine Learning (ML) techniques to provide Personalized Course Recommendations (PCR) to students. Furthermore, the existing recommendation techniques are based on either explicit or implicit feedback and fail to capture the changes in learners' preferences while integrating implicit or explicit feedback. To this end, this paper proposes a new model for personalized learning and PCR that is enabled by a smart E-Learning (EL) platform. The model aims to gather data on students' academic performance, interests, and learning preferences and utilize this data to recommend the courses that will be most beneficial to each student. The proposed approach makes suggestions based on the learner's interactions with the system and the cosine similarity in related contents by combining explicit (user ratings) and implicit (views and behavior) methodologies. The suggested method makes use of ML algorithms and an EL Recommender System (RecSys) based on Collaborative Filtering (CF).This includes Random Forest Regressor (RFR), Decision Tree Regressor (DTR), K-Nearest Neighbors (KNN), Singular Value Decomposition (SVD), eXtreme Gradient Boosting Regressor (XGBR), and Linear Regression (LR). The proposed solution is benchmarked against existing approaches on both predictive accuracy and running time. Experimental results are conducted based on two benchmark datasets (Coursera and Udemy). The proposed model outperforms existing top-K recommendations techniques in terms of accuracy metrics such as precision@k, Mean Average Precision (MAP)@k, recall@k, Normalized Discounted Cumulative Gain (NDCG)@k, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) for PCR. From the experiments, it can be shown that SVD can perform well in terms of higher accuracy and MAP and NDCG and lower MAE, RMSE, and MSE values when contrasted to other proposed algorithms because it is better suited to capture complex student-course interactions. The proposed solutions are promising on two different datasets and can be applied to various RecSys domains.

show abstract

Movie Recommender System Using K-Nearest Neighbors Variants

Cited by 22 publications

References 13 publications

A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students

Developing a Personalized E-Learning and MOOC Recommender System in IoT-Enabled Smart Education

Contact Info

Product

Resources

About