On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems

The importance of identifying plant diseases has risen recently due to the adverse effect they have on agricultutal production. Plant diseases have been a big concern in agriculture, as they affect crop production, and constitute a major threat to global food security. In the domain of modern agriculture, effective plant disease management is vital to ensure healthy crop yields and sustainable practices. Traditional means of identifying plant disease are faced with lots of challenges and the need for better and efficient detection methods cannot be overemphazised. The emergence of advanced technologies, particularly deep learning and content-based filtering techniques, if integrated together can changed the way plant diseases are identified and treated. Such as speedy and correct identification of plant diseases and efficient treatment recommendations which are keys for sustainable food production. In this work, We try to investigate the current state of research, identified gaps and limitations in knowledge, and suggests future directions for researchers, experts and farmers that could help to provide better ways of mitigating plant disease problems.

Traditional Collaborative filtering (CF) is one of the techniques of recommender systems that has been successfully exploited in various applications, but sometimes they fail to provide accurate recommendations because they depend majorly on the rating matrix, which is always scanty and of very high dimension. Matrix factorization (MF) algorithms are variants of latent factor models, which are easy, fast, and efficient. This article reviews the related research and advances in the application of matrix factorization techniques in recommender systems. Popular matrix factorization algorithms utilized in recommender systems were reviewed. The peculiar challenges of using matrix factorization in recommender systems were also enumerated and discussed with the goal of identifying the different problems solved with the use of matrix factorization techniques as applied in recommender systems.

Agricultural productivity remains seriously threatened by the attacks of plant diseases, even though it is the bedrock of global food security. These diseases, if ignored, can lead to massive crop losses and economic setbacks. Therefore, the development of accurate and efficient plant disease detection systems is essential to preserve plants and promote sustainable agricultural practices. This study examined the potential of ensembled deep learning models that incorporates feature-level fusion to enhance the robustness and accuracy of plant disease detection. Specifically, we utilized pre-trained deep learning architecture (VGG16, Residual Network 50 (ResNet50), and GoogleNet (InceptionV3)) to extract distinctive features representations from plant leaf images. These extracted features were combined to enhance the performance of our ensemble model. For disease classification, the fused features were passed through a Dense layer with 128 units and ReLU activation, followed by a SoftMax classification layer to predict the probabilities of each plant disease class. Experiments were conducted using the New Plant Diseases Dataset. It contains 87,867 image samples of various plant disease species for 38 classes and 14 different crop species. The ensemble model achieved notable results, with an accuracy of 97.0%. This reveals the capability of feature-fusion ensembled learning in improving detection stability and accuracy. The knowledge and application of feature fusion in disease detection can help ensure more accurate, timely, and eco-friendly interventions. This, in turn, can support sustainable agricultural practices.

Recommender systems (RSs) effectively curb information overload by providing personalized suggestions of items to users across different online domains. Their widespread use in e-commerce enhances user engagement, personalizes shopping experiences, and drives sales growth. However, despite the effectiveness of these systems at generating recommendations for users, they still raise major privacy and security concerns as their data could be exploited for malicious purposes, which can lead to data breaches and misuse. Therefore, this paper presents a comprehensive and systematic review of the underlying causes of privacy and security challenges in RS. It also provides a detailed taxonomy categorizing these concerns based on their targets and the risks they create. It further presents potential solutions that have been used in the literature while identifying challenges and possible research directions to pursue in a bid to address privacy and security concerns in RSs. This paper will be a useful resource for current and upcoming researchers in the domain of RSs. It will support knowledge advancement and steer appropriate research directions.