Explore the vast range of titles available.

Recommender systems have become indispensable tools in various domains, such as e-commerce, entertainment, and social media, for delivering personalized user experiences. Collaborative Filtering (CF) is an essential technique in RS that leverages user similarity patterns to suggest items which align with individual preferences. This study presents an experimental comparative analysis of collaborative filtering-based recommender system methods including memory-based methods (KNN variants), model-based approaches (SVD, SVD++, co-clustering), and techniques based on neural networks (NCF, DeepFM, LightGCN). We conduct a thorough evaluation of these methods on the MovieLens benchmark datasets (100K, 1M, 25M) utilizing various metrics, such as RMSE, MAE, FCP, NDCG@10, Precision@10, Recall@10, and F1@10 Score, aiming to identify the most effective approaches and understand the advantages and disadvantages of each approach. Additionally, we provide detailed insights into the working mechanisms of each model. Our comprehensive analysis reveals the strengths and limitations of each method, offering critical insights for practitioners in selecting the most suitable recommender system technique based on specific requirements and constraints. The findings indicate that, on large datasets, neural and graph-based models achieve measurable improvements in both rating accuracy and top-k ranking tasks, with ranking gains observed upto 15%. Nonetheless, more straightforward approaches (KNN, SVD) continue to hold their ground in smaller datasets or low-resource environments because of their straightforward implementation and clarity in interpretation. The results highlight the importance of achieving a balance between computational expense, scalability, and model intricacy when choosing collaborative filtering techniques for practical implementations of recommender systems. We offer practical insights to assist professionals in selecting models that are suited to particular application needs and data attributes. This research enhances the understanding of collaborative filtering techniques and offers valuable insights for improving the performance of RS across diverse domains.

As a result of a huge volume of implicit feedback such as browsing and clicks, many researchers are involving in designing recommender systems (RSs) based on implicit feedback. Though implicit feedback is too challenging, it is highly applicable to use in building recommendation systems. Conventional collaborative filtering techniques such as matrix decomposition, which consider user preferences as a linear combination of user and item latent features, have limited learning capacities, hence suffer from a cold start and data sparsity problems. To tackle these problems, the research direction towards considering the integration of conventional collaborative filtering with deep neural networks to maps user and item features. Conversely, the scalability and the sparsity of the data affect the performance of the methods and limit the worthiness of the results of the recommendations. Therefore, the authors proposed a multi-model deep learning (MMDL) approach by integrating user and item functions to construct a hybrid RS and significant improvement. The MMDL approach combines deep autoencoder with a one-dimensional convolution neural network model that learns user and item features to predict user preferences. From detail experimentation on two real-world datasets, the proposed work exhibits substantial performance when compared to the existing methods.

The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.

Recommender systems became indispensable for assisting customers, users, and businesses in various domains. Collaborative Filtering (CF) is a widely used technique for generating recommendations considering user and item interactions. Many existing recommenders, such as Single Value Decomposition (SVD) and correlation, to mention a few, are based on the CF technique. These approaches suffer from two significant drawbacks. The first one is that they are computationally expensive, while the second one is the inability to cope with newly arrived user-item interactions. This leads to a situation where users' known preferences do not change over time. However, for all practical purposes in real-time applications, there is a need to update user preferences dynamically. In this paper, we proposed a novel approach known as co-clustering-based CF that performs real-time CF considering newly arrived items, users, and ratings in rapid succession. It systematically clusters rows (users) and columns (items) with an incremental mining model. Specifically, we proposed an Efficient Co-Clustering-Based Product Recommender (ECPR) algorithm for dynamically generating recommendations that reflect the latest state of user-items-ratings dynamics. The framework is evaluated on the benchmark MovieLens dataset comprising 100,000 ratings from 943 users on 1,682 items. Comparative evaluation with existing CF methods, including SVD and Non-Negative Matrix Factorization (NNMF), demonstrates that ECPR achieves up to 3.3% improvement in Mean Absolute Error (MAE) and reduces training time by up to 60%. ECPR outperforms existing CF methods regarding computational cost and accuracy in generating recommendations.

Background Understanding student engagement and academic performance is crucial in AI-driven e-learning environments. Many learning management systems (LMS) lack effective collaborative course recommendation strategies, limiting support for personalised learning experiences. Methods This study developed and evaluated collaborative filtering and machine learning models to generate course recommendations. Machine learning models such as K-Nearest Neighbours (KNN), Singular Value Decomposition (SVD), and Neural Collaborative Filtering (NCF) were applied. Two education-related datasets from Kaggle were used. The first contains 100,000 course reviews from Coursera, and the second dataset includes 209,000 course details and comments from Udemy. Data preprocessing was conducted to clean and structure both datasets. The model effectiveness was evaluated using Mean Absolute Error (MAE), Hit Rate (HR), and Average Reciprocal Hit Ranking (ARHR). Results K-Nearest Neighbours showed the highest performance on the Coursera dataset, while Singular Value Decomposition and Neural Collaborative Filtering maintained stable predictive accuracy across both datasets. The findings indicate that dataset characteristics influenced model performance. K-Nearest Neighbours worked effectively with structured and consistent data, while Singular Value Decomposition and Neural Collaborative Filtering produced consistent outcomes across diverse datasets. Conclusions This study contributes to e-learning research by demonstrating the potential of collaborative filtering and machine learning in enhancing course recommendations and promoting engagement in the learning management system. Limitations include the use of two datasets and a limited set of machine learning models. Future work aims to integrate learning styles and evaluate the framework across more diverse educational contexts to support adaptive and collaborative learning.

The tourism industry is all around keeping tourists happy, occupied and equipped with the things they need during their time away from home. On the other hand, mobile technologies have a considerable impact on user experience, particularly in the tourist and entertainment areas. This paper presents a tourist and entertainment-related mobile application. It utilizes a personalized experience approach and seeks to provide good user experiences by making it adaptable to their unique interests while considering many criteria such as the user's gender, age, location, and other characteristics. The system will propose locations to visit or activities to do in any city to the user. As the user continues to use the application, the suggestions offered will constantly be improved; it will learn more about the user's preferences by recording the user's past and what they enjoy. The application implements and integrates two types of recommender systems, the item-item collaborative filtering algorithm and the user-user collaborative filtering algorithm. The user acceptance testing was conducted on 10 users from a variety of backgrounds and ages. Each participant has performed a set of 17 asks that covers the functionality of the application. Effectiveness results showed that about 70% of the tasks were completed without errors by all participants. And the tasks that were completed with some errors had an average of errors ranges from (0 - 0.4) which is a promising result when compared to the normal average number of errors per which is 0.7. Regarding the efficiency, results show that the longest completion time was in 3 tasks (register task, log-in, and edit profile) which is expected since they require the entry of detailed information. On the other hand, for the remaining tasks the average completion time was 5.4s which is accepted. User satisfaction was measured through a System Usability Scale (SUS) survey, the achieved score was 87.75 which is higher than the threshold to pass the SUS test which is 68, thus LOCUS has fulfilled the user satisfaction measure.

The recommender systems are used to mainly suggest recommendations to the online users by utilizing the user preferences recorded during the item purchase. No matter how, the performance of the recommendation quality seems to be inevitable and far satisfactory. In this paper, a new approach based on the mathematical model, Formal Concept Analysis (FCA) is used to improve the rating prediction of the unknown users which can certainly overcome the issues of the existing approaches like data sparsity, high dimensionality of data, performance of the recommendation generated for top n recommendation. The FCA method is applied using Boolean Matrix Factorization (ie. optimal formal concepts) in predicting the rating of the unknown users in the available user-item interaction matrix which proves to be more efficientin tackling the problem of computational complexity managing the high dimensionality of data. The proposed method is applied using item-based collaborative filtering technique and the experiment is conducted on the Movielens dataset which shows the satisfactory results. The experiments results are evaluated using the related error metrics and performance metrics. The experimental results are also compared with existing item-based Collaborative Filtering techniques which demonstrate that the performance of recommendation quality gradually improved with state-of-the-art existing techniques.

Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering (CF) algorithms. The effect of this subsampling on the computing time and accuracy of CF is not fully understood, and clear guidelines for selecting optimal or even appropriate subsampling levels are not available. In this paper, we present a Density-based Random Stratified Subsampling using Clustering (DRSC) algorithm in which the desired Fraction of Users Dropped (FUD) and Fraction of Items Dropped (FID) are specified, and the overall density during subsampling is maintained. Subsequently, we develop simple models of the Training Time Improvement (TTI) and the Accuracy Loss (AL) as functions of FUD and FID, based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens, Yahoo Music Rating, and Amazon Automotive data. Simulations show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven methods. The TTI linear regression of a CF method appears to be same for all datasets. Extensive simulations illustrate that TTI can be estimated reliably with FUD and FID only, but AL requires considering additional dataset characteristics. The derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL. A simple sub-optimal approximation was found, in which the optimal AL is proportional to the optimal Training Time Reduction Factor (TTRF) for higher values of TTRF, and the optimal subsampling levels, like optimal FID/(1-FID), are proportional to the square root of TTRF.

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.

In recent years, Recommender System (RS) research work has covered a wide variety of Artificial Intelligence techniques, ranging from traditional Matrix Factorization (MF) to complex Deep Neural Networks (DNN). Traditional Collaborative Filtering (CF) recommendation methods such as MF, have limited learning capabilities as it only considers the linear combination between user and item vectors. For learning non-linear relationships, methods like Neural Collaborative Filtering (NCF) incorporate DNN into CF methods. Though, CF methods still suffer from cold start and data sparsity. This paper proposes an improved hybrid-based RS, namely Neural Matrix Factorization++ (NeuMF++), for effectively learning user and item features to improve recommendation accuracy and alleviate cold start and data sparsity. NeuMF++ is proposed by incorporating effective latent representation into NeuMF via Stacked Denoising Autoencoders (SDAE). NeuMF++ can also be seen as the fusion of GMF++ and MLP++. NeuMF is an NCF framework which associates with GMF (Generalized Matrix Factorization) and MLP (Multilayer Perceptrons). NeuMF achieves state-of-the-art results due to the integration of GMF linearity and MLP non-linearity. Concurrently, incorporating latent representations has shown tremendous improvement in GMF and MLP, which result in GMF++ and MLP++. Latent representation obtained through the SDAEs' latent space allows NeuMF++ to effectively learn user and item features, significantly enhancing its learning capability. However, sharing feature extractions among GMF++ and MLP++ in NeuMF++ might hinder its performance. Hence, allowing GMF++ and MLP++ to learn separate features provides more flexibility and greatly improves its performance. Experiments performed on a real-world dataset have demonstrated that NeuMF++ achieves an outstanding result of a test root-mean-square error of 0.8681. In future work, we can extend NeuMF++ by introducing other auxiliary information like text or images. Different neural network building blocks can also be integrated into NeuMF++ to form a more robust recommendation model.