The State of Recommender Systems for E-commerce
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 8979
Special Issue Editors
Interests: e-commerce; e-business; business intelligence; marketing; human-computer interaction; recommender systems; artificial intelligence
Interests: human-computer interaction; machine learning; customer relationship management; electronic commerce
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are inviting submissions to the Special Issue entitled “The State of Recommender Systems for E-commerce”.
The e-commerce industry has been continuously growing since its beginning. It has gained even more popularity during the last two years thanks to restricted access to traditional stores as a result of the pandemic. The choice of goods available online imposes additional effort in finding the right products to suit individual needs. Recommender systems play an important role not only in overcoming the information/product overload problem, but also guiding clients through a store as a personal assistant, helping to build a long-term relationship.
This Special Issue focuses on recommender systems for e-commerce and aims to present advances in machine learning and artificial intelligence in this area. It focuses on a wide range of artificial intelligence applications such as: user profiling and updating; text, image, and review processing; and recommendation engines. Any novel works on the application of artificial intelligence for e-commerce recommender systems are highly welcome.
We invite submissions on all topics of algorithms and theories for e-commerce recommender systems, including:
- Recommender systems for e-commerce;
- User-adaptive profiling;
- User preference change detection and adaptation;
- Context-aware recommender systems;
- Reinforcement learning for recommender systems;
- Fuzzy techniques for recommender systems;
- Transfer learning for recommender systems;
- Deep learning for recommender systems;
- Evaluation of recommender systems;
- Recommendation explainability;
- Adversarial recommender systems;
- Privacy and trust in recommender systems.
Dr. Tomasz Zdziebko
Prof. Dr. Piotr Sulikowski
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 2400 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
- recommender systems
- e-commerce
- machine learning
- user modeling
