In the era of digitalization and e-commerce, people use online platforms to find their desired products and services. Such platforms often accommodate enormous collections of entities; nevertheless, typically, each user is interested in only a tiny fraction of them. To this end, the role of personalized AI-driven recommender systems is paramount. Recommender systems (RSs) are based on intelligent models that leverage data mining and machine learning methodologies, learning users' preferences and recommending relevant items to each user. Typically, they manage to infer users' preferences by using historical user-item data as well as other types of available information, such as item and user side-information (i.e., features that describe the users/items in the system). RSs are omni-present as they are currently employed by movie and music platforms, online sellers, booking agencies, marketing agencies, and social media platforms.

This Special Issue is dedicated to new challenges and innovative approaches related to AI-driven recommender systems. We are pleased to invite submissions of original research on all aspects of recommendation, including the following topics.

Dr. Konstantinos Pliakos
Dr. Alireza Gharahighehi
Guest Editors

Manuscript Submission Information:

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords:

- bias and fairness in recommender systems
- filter bubble problem
- cold-start problem
- multi-stakeholder recommendation
- performance metrics and new aspects of evaluating recommendations
- real-world implementations and scalability of recommendation algorithms
- ethics around recommender systems
- privacy and security
- cross-domain and multi-modal recommendation
- multimedia recommender systems (images, videos, music)
- benchmarking and comparative studies