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Recommender systems have existed for decades, shaping how people consume digital content, receive information, and engage in day-to-day activities, among others. Undoubtably, recommender systems also play a crucial role ...

Generative Artificial Intelligence (GAI) is changing what can be done with Recommender Systems (RS) in e-commerce by allowing much more interactive, situationally aware, and highly tailored experiences for users. The pur...

With the advent of the current digital era, individuals across the developed world are commonly equipped with devices that can access vast amounts of information at their fingertips. What was considered an impossible fea...

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types...

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, coll...

As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional informati...

This work presents a systematic literature review on the application of Machine Learning algorithms in the development of effective movie recommender systems. With the increasing popularity of movie recommender systems i...

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a liveinteraction. These systems, especially the k-nearest neigh...

Abstract Collaborative filtering and neural recommendation models have advanced substantially over the past decade, yet they frequently struggle to capture the fine-grained structure of user preferences and the rich, mul...

Maintaining a balanced diet is essential for overall well-being, yet many individuals face challenges in meal planning due to time constraints, limited nutritional knowledge, and difficulty aligning meals with personal d...

The necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movi...

Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Wi...

The rapid growth of e-commerce has led to product overload, which has resulted in personalized product discovery becoming a crucial problem for consumers. Classic recommender systems (RS), which rely heavily on content-b...

The February 2026 issue presents 17 research papers within its regular section, covering a wide range of areas including Artificial Intelligence (AI), Machine Learning (ML), Web and Cloud Technologies, Recommender System...

The October 2025 issue of the Journal of Informatics and Web Engineering (JIWE) concludes the journal’s triannual publication cycle and precedes its transition to a quarterly schedule commencing in 2026. This issu...

Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This June 2025 release contains 28 papers within the regular sectio...

"The selection of a suitable master’s program has become an increasingly complex task due to the growing diversity of offerings and the lack of standardized comparable information across institutions. This thesis propo...

Information systems play an ever-increasing key role in our society. In particular, data intensive applications are in constant demand and there is need of computing environments with much more intelligent capabilities t...

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has recei...

"En la era del turismo digital, las recomendaciones personalizadas se han vuelto esenciales para mejorar la experiencia del viajero. Esta tesis investiga el desarrollo de un sistema de recomendación turística utilizand...

User populations in modern recommendation systems consist of diverse communities with distinct and often non-overlapping preferences. However, most existing models, such as traditional Variational Autoencoders (VAEs), op...

E-learning systems present to students learning material carefully prepared and organized by teachers to meet some course goals. However, further relevant information that can help students to complete their learning pro...