Recommendation Algorithms and Web Mining

Dear Colleagues,

People access the web daily to stay informed, keep in touch with friends, and use online services. Today, web users can share experiences with friends, and the explosion of available data makes users feel overwhelmed with information. Thus, they need to be guided to find content that interests them. Furthermore, the number of online services has increased, and users can rely on several online platforms for buying products, booking flights, or downloading songs. Given the multitude of offers, users may need support in the decision process for making transactions.

Recommendation systems have been largely used for suggesting products as well as for addressing the problem of information overload. A typical recommendation system suggests new items to users based on their preferences. Items can be products, books, but also textual content (e.g., news, tweets) and multimedia files (e.g., songs, movies). Recommendation algorithms are even employed in social networks for suggesting friends and in location-based social networks for recommending venues.

Designing recommendation systems for web users poses significant challenges. Collaborative filtering and content-based approaches have to face different problems ranging from the heterogeneous and dynamic nature of the web to scalability issues. Moreover, recommendation systems may suffer from data sparsity and lacking user profiles due to privacy concerns.

Web mining can help to improve the accuracy and scalability of recommendation systems. The analysis of users’ previous activities, such as navigational data, posts, purchases, and reviews, are important clues to better understand user interests, predict future actions, and timely satisfy users’ needs.

The MDPI Information journal invites submissions to a Special Issue on “Recommendation Algorithms and Web Mining”. We aim to collect recent advances in web mining addressing the main challenges of recommendation algorithms and leveraging user activities to improve the efficiency and effectiveness of recommendation systems.

Dr. Ida Mele
Guest Editor

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