Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Most of the existing music recommendation systems use collaborative or content based recommendation engines. However, the music choice of a user is not only dependent to the historical preferences or music contents. But also dependent to the mood of that user. This paper proposes an emotion based music recommendation framework that learns the emotion of a user from the signals obtained via wearable physiological sensors. In particular, the emotion of a user is classified by a wearable computing device which is integrated with a galvanic skin response (GSR) and photo plethysmography (PPG) physiological sensors. This emotion information is feed to any collaborative or content based recommendation engine as a supplementary data. Thus, existing recommendation engine performances can be increased using these data. Therefore, in this paper emotion recognition problem is considered as arousal and valence prediction from multi-channel physiological signals. Experimental results are obtained on 32 subjects’ GSR and PPG signal data with/out feature fusion using decision tree, random forest, support vector machine and k-nearest neighbors algorithms. The results of comprehensive experiments on real data confirm the accuracy of the proposed emotion classification system that can be integrated to any recommendation engine.

Emotion Based Music Recommendation System Using Wearable Physiological Sensors

These days, many recommender systems (RS) are utilized for solving information overload problem in areas such as e-commerce, entertainment, and social media. Although classical methods of RS have achieved remarkable successes in providing item recommendations, they still suffer from many issues such as cold start and data sparsity. With the recent achievements of deep learning in various applications such as Natural Language Processing (NLP) and image processing, more efforts have been made by the researchers to exploit deep learning methods for improving the performance of RS. However, despite the several research works on deep learning based RS, very few secondary studies were conducted in the field. Therefore, this study aims to provide a systematic literature review (SLR) of deep learning based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the field. This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications. The paper particularly adopts an SLR approach based on the standard guidelines of the SLR designed by Kitchemen-ham which uses selection method and provides detail analysis of the research publications. Several publications were gathered and after inclusion/exclusion criteria and the quality assessment, the selected papers were finally used for the review. The results of the review indicated that autoencoder (AE) models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks (CNNs) and the Recurrent Neural Networks (RNNs) models. Also, the results showed that Movie Lenses is the most popularly used datasets for the deep learning-based RS evaluation followed by the Amazon review datasets. Based on the results, the movie and e-commerce have been indicated as the most common domains for RS and that precision and Root Mean Squared Error are the most commonly used metrics for evaluating the performance of the deep leaning based RSs.

Recommendation system based on deep learning methods: a systematic review and new directions

Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels

An effective collaborative movie recommender system with cuckoo search

https://cyberleninka.org/article/n/501521.pdf

Recommendation systems: Principles, methods and evaluation

Context-aware recommender system (CARS) is a promising technique for recommending research resources to users (researchers) by predicting their preferences (resources) under different situations. If the contextual information given to such a system is inappropriate, it will certainly have a negative effect on the nature of recommendation output generated by the system as well as making the system to have high dimensionality complexity. Currently, several CARS recommendation algorithms have been developed but they have failed to bring to bear the means and importance of experimentally validating the contextual information used in different domains of application of CARS. Hence, this paper experimentally validates the contextual variables in the domain of research resources by splitting a research resource (article) into three major sections (introduction, review and methodology). These sections are the contextual variables validated in order to authenticate their viability as context that could be used in recommending research resources based on the specific section of an article a researcher is interested in. The result of our experiment shows that irrespective of the domain of articles, journal articles have higher variability in their citations at introduction, very significant variability between the articles in the review and high variability in the methodology contextual variable respectively than the articles in the proceeding under the three contextual variables. This experiment shows that these three variables could be used as context .It also shows the percentage of splitting that could be used within journals and proceedings for context-aware research resources recommendations.

/pdf/experimental-validation-of-contextual-variables-for-research-20vcjaz3tt.pdf

Experimental Validation of Contextual Variables for Research Resources Recommender System

Apparently, most students in Nigeria are facing challenges as regards to the lack of portability, stress, time wastage and inadequate resources in terms of accessing the school libraries, as well as the inefficiency of the existing e-library, leading to the reduction in the number of students that do access the libraries. Research has shown that most students no longer believe in the physical libraries and have developed interest in electronic resources. Hence, an e-library system enhanced with book recommendation component could serve as a solution to these problems. Several researchers have repeatedly attempted to develop various solutions to this problem using various methodologies and approaches in order to provide a digital library that could address the aforementioned problems. In this work, an e-library system integrated with recommendation component was designed and implemented to help students locate relevant books. Also, an additional feature which adds books to the shelf for future reference was included to enhance accessibility and efficiency of the system. The web application was implemented on a live server (Namecheap) which is one of the most effective live servers in Nigeria. Furthermore, the system was evaluated with one hundred and fourteen (114) students, and the results of the evaluation carried out on the system emphasized its usefulness in terms user friendliness (77%), user experience (86%), interface appearance (75%), system loading speed (82%), platform compatibility (78%), recommendation accuracy (80%) and recommendation reliability (84%). Therefore, the system could be used to solve students’ problems with regards to the challenges faced with the use of physical and conventional e-libraries.

An E-Library System Integrated with Bookshelf and Recommendation Components

A Chabot is a conversational intelligent agent that has the capability of engaging in human-like interaction with its users. A lot of chatbots have been developed, but to the best of our knowledge, there are few or no chatbots that have been developed for hostel location integrated with a recommendation component to ease the cost, time, and stress of identifying suitable hostels for students, especially at higher institutions of learning. Therefore, this work develops a location-based chatbot system enhanced with recommendation capabilities to allow students to locate hostels that satisfy their needs in an easy and efficient way. The chatbot system was designed as a cross platform compatibility application with different tools and technologies which include Python, HTML and CSS with JavaScript to enhance the interactivity and attractiveness of the system. PHP provides access to MySQL database. The chatbot system provides good experience to its users in terms of loading speed, user friendliness, interface appearance, platform compatibility and recommendation accuracy as it allows them identify suitable hostel speedily and as well provides personalized recommendation of hostels to them.

Development of a Mobile-Based Hostel Location and Recommendation Chatbot System

Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases

Folasade Olubusola Isinkaye

Papers

Recommendation systems: Principles, methods and evaluation

Experimental Validation of Contextual Variables for Research Resources Recommender System

An E-Library System Integrated with Bookshelf and Recommendation Components

Development of a Mobile-Based Hostel Location and Recommendation Chatbot System

A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques