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An online recommendation system (RS) involves using information technology and customer information to tailor electronic commerce interactions between a business and individual customers. Extant information systems (IS) studies on RS have approached the phenomenon from many different perspectives, and our understanding of the nature and impacts of RS is fragmented. The current study reviews and synthesizes extant empirical IS studies to provide a coherent view of research on RS and identify gaps and future directions. Specifically, we review 40 empirical studies of RS published in 31 IS journals and five IS conference proceedings between 1990 and 2013. Using a recommendation process theoretical framework, we categorize these studies in three major areas addressed by RS research: understanding consumers, delivering recommendations, and the impacts of RS. We review and synthesize the extant literature in each area and across areas. Based on the review and synthesis, we surface research gaps and provide suggestions and potential directions for future research on recommendation systems.

E-commerce systems experience poor quality of performance when the number of records in the customer database increases due to the gradual growth of customers and products. Applying implicit hidden features into the recommender system (RS) plays an important role in enhancing its performance due to the original dataset’s sparseness. In particular, we can comprehend the relationship between products and customers by analyzing the hierarchically expressed hidden implicit features of them. Furthermore, the effectiveness of rating prediction and system customization increases when the customer-added tag information is combined with hierarchically structured hidden implicit features. For these reasons, we concentrate on early grouping of comparable customers using the clustering technique as a first step, and then, we further enhance the efficacy of recommendations by obtaining implicit hidden features and combining them via customer’s tag information, which regularizes the deep-factorization procedure. The idea behind the proposed method was to cluster customers early via a customer rating matrix and deeply factorize a basic WNMF (weighted nonnegative matrix factorization) model to generate customers preference’s hierarchically structured hidden implicit features and product characteristics in each cluster, which reveals a deep relationship between them and regularizes the prediction procedure via an auxiliary parameter (tag information). The testimonies and empirical findings supported the viability of the proposed approach. Especially, MAE of the rating prediction was 0.8011 with 60% training dataset size, while the error rate was equal to 0.7965 with 80% training dataset size. Moreover, MAE rates were 0.8781 and 0.9046 in new 50 and 100 customer cold-start scenarios, respectively. The proposed model outperformed other baseline models that independently employed the major properties of customers, products, or tags in the prediction process.

Music accompanies all phases of our lives, and when we reach old age, music becomes a direct symbol of nostalgia. Autobiographical memories are essential to an individual’s sense of identity, continuity, and meaning. But some pathologies, such as dementia, can interrupt the memory storage process. Music can help recall and evoke memories and can be used in alternative treatments for dementia. This work aims to propose an architecture for a music recommendation system capable of recommending music according to musical genre, with the aim of helping music therapists in therapies addressed to elderly people with dementia in initial states. Here we used data from the public music database Emotify, which is composed of 400 songs labeled by 1595 participants in 7975 sessions. Both channels of the songs were windowed using 10s windows with 5s overlap. The data from these windows were represented by 34 time and frequency features. Then, we assessed and compared the performance of classifiers based on support vector machines, decisions trees and Bayesian network. The most suitable architecture in this experimental study was the Random Forest with 250 trees, with an accuracy of 83.42% ± 1.72%, kappa statistic of 0.78 ± 0.02, AUC-ROC of 0.99 ± 0.00, sensitivity of 0.96 ± 0.02, and specificity of 0.94 ± 0.01. this exploratory study found promising results that indicates the possibility of building recommendation systems to support music therapy based on the automatic classification of songs according to the most appropriate musical genre for the patient.

With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user’s current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user’s current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system’s ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users’ emotional responses, resulting in a positive mood change for most users. Moreover, the system’s scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system’s ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human–computer interaction, psychology, and social sciences.

Hybrid recommendation systems are increasingly crucial for businesses aiming to boost revenue and customer engagement. These systems integrate various algorithms, each with unique strengths, to outperform traditional recommendation methods. Our study introduces a novel hybrid recommendation system, TriDeepRec, which effectively combines content-based and behavior-based data to enhance recommendation accuracy. We first introduce a Convolutional Autoencoder-based Recommendation System (CAERS), designed to process content data and extract complex, meaningful patterns, translating these into predictive ratings. Notably, CAERS tackles the cold-start problem by leveraging content information alone, making it robust in scenarios where historical user interaction data are sparse or unavailable. Next, we incorporate Neural Collaborative Filtering (NCF), a deep learning approach, to analyze past user behavior and predict ratings. The outputs from CAERS and NCF are then integrated using a Multilayer Perceptron (MLP), a type of neural network, to generate the final recommendations. Our methodology employs three deep learning techniques to create TriDeepRec, a system capable of utilizing both past interactions and content attributes. We evaluate our system using two datasets, focusing on both error-based metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and ranking quality through Normalized Discounted Cumulative Gain (NDCG). These metrics highlight the system’s performance in both prediction accuracy and ranking relevance. The results indicate improvements over both the individual components and other leading models in the field. This demonstrates that TriDeepRec, by harnessing the strengths of both content and behavior data, provides a more accurate, reliable, and effective recommendation system.

Recommendation systems are crucial in boosting companies’ revenues by implementing various strategies to engage customers and encourage them to invest in products or services. Businesses constantly desire to enhance these systems through different approaches. One effective method involves using hybrid recommendation systems, known for their ability to create high-performance models. We introduce a hybrid recommendation system that leverages two types of recommendation systems: first, a novel deep learning-based recommendation system that utilizes users’ and items’ content data, and second, a traditional recommendation system that employs users’ past behaviour data. We introduce a novel deep learning-based recommendation system called convolutional autoencoder recommendation system (CAERS). It uses a convolutional autoencoder (CAE) to capture high-order meaningful relationships between users’ and items’ content information and decode them to predict ratings. Subsequently, we design a traditional model-based collaborative filtering recommendation system (CF) that leverages users’ past behaviour data, utilizing singular value decomposition (SVD). Finally, in the last step, we combine the two method’s predictions with linear regression. We determine the optimal weight for each prediction generated by the collaborative filtering and the deep learning-based recommendation system. Our main objective is to introduce a hybrid model called CAERS-CF that leverages the strengths of the two mentioned approaches. For experimental purposes, we utilize two movie datasets to showcase the performance of CAERS-CF. Our model outperforms each constituent model individually and other state-of-the-art deep learning or hybrid models. Across both datasets, the hybrid CAERS-CF model demonstrates an average RMSE improvement of approximately 3.70% and an average MAE improvement of approximately 5.96% compared to the next best models.

Movies are one of the integral components of our everyday entertainment. In today’s world, people prefer to watch movies on their personal devices. Many movies are available on all popular Over the Top (OTT) platforms. Multiple new movies are released onto these platforms every day. The recommendation system is beneficial for guiding the user to a choice from among the overloaded contents. Most of the research on these recommendation systems has been conducted based on existing movies. We need a recommendation system for forthcoming movies in order to help viewers make a personalized decision regarding which upcoming new movies to watch. In this article, we have proposed a framework combining sentiment analysis and a hybrid recommendation system for recommending movies that are not yet released, but the trailer has been released. In the first module, we extracted comments about the movie trailer from the official YouTube channel for Netflix, computed the overall sentiment, and predicted the rating of the upcoming movies. Next, in the second module, our proposed hybrid recommendation system produced a list of preferred upcoming movies for individual users. In the third module, we finally were able to offer recommendations regarding potentially popular forthcoming movies to the user, according to their personal preferences. This method fuses the predicted rating and preferred list of upcoming movies from modules one and two. This study used publicly available data from The Movie Database (TMDb). We also created a dataset of new movies by randomly selecting a list of one hundred movies released between 2020 and 2021 on Netflix. Our experimental results established that the predicted rating of unreleased movies had the lowest error. Additionally, we showed that the proposed hybrid recommendation system recommends movies according to the user’s preferences and potentially promising forthcoming movies.

In the era of digital platforms and abundant data, food recommender systems have been essential tools for guiding individuals to discover preferences and perfect meals. Nowadays, the wide variety of available food options presents a challenge for consumers seeking personalized meals and relevant recommendations. By dynamically allocating evaluations based on user behaviour and item characteristics, the system aims to increase the variety and precision of dietary recommendations. Furthermore, the system will implement continuous learning mechanisms to responds to fluctuations in user preferences over time, ensuring sustained high levels of user satisfaction. Therefore, the primary objective of this paper is to design and implement the recommender system, test and evaluate the hybrid recommender system and explore the various recommendation techniques. Besides that, this paper will discuss the combination of various algorithms: collaborative filtering, content-based filtering, and hybrid approaches. The expected outcome of this research is a robust recommender system that provides accurate and relevant food recommendations to individual preferences. In conclusion, a system with a graphical user interface will be implemented so that the end-user and administrator can visualize it for better insight into decision-making.

Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R -squared and area under the curve are applied to evaluate the model. Finally, the ML.Recommend model was published on Microsoft’s NuGet system, so that other researchers could use and extend this model.

To perform their daily tasks, developers intensively make use of existing resources by consulting open source software (OSS) repositories. Such platforms contain rich data sources, e.g., code snippets, documentations, and user discussions, that can be useful for supporting development activities. Over the last decades, several techniques and tools have been promoted to provide developers with innovative features, aiming to bring in improvements in terms of development effort, cost savings, and productivity. In the context of the EU H2020 CROSSMINER project, a set of recommendation systems has been conceived to assist software programmers in different phases of the development process. The systems provide developers with various artifacts, such as third-party libraries, documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This paper is an experience report to present the knowledge pertinent to the set of recommendation systems developed through the CROSSMINER project. We explain in detail the challenges we had to deal with, together with the related lessons learned when developing and evaluating these systems. Our aim is to provide the research community with concrete takeaway messages that are expected to be useful for those who want to develop or customize their own recommendation systems. The reported experiences can facilitate interesting discussions and research work, which in the end contribute to the advancement of recommendation systems applied to solve different issues in Software Engineering.