Using Differential Evolution in order to create a personalized list of recommended items

The recommendation systems are used to suggest new, still not discovered items to users. At the moment, in order to achieve the best quality of the generated recommendations, users and their choices in the system must be analyzed to create a certain profile of preferences for a given user in order to adjust the generated recommendation to his personal taste. This article will present a recommendation system, which based on the Differential Evolution (DE) algorithm will learn the ranking function while directly optimizing the average precision (AP) for the selected user in the system. To achieve that, items are represented through a feature vectors generated using user-item matrix factorization. The experiments have been conducted on a popular and widely available public dataset MovieLens, and show that our approach in certain situations can significantly improve the quality of the generated recommendations. Results of experiments are compared with other techniques

Tagging and Tag Recommendation

Tagging has emerged as one of the best ways of associating metadata with objects (e.g., videos, texts) in Web 2.0 applications. Consisting of freely chosen keywords assigned to objects by users, tags represent a simpler, cheaper, and a more natural way of organizing content than a fixed taxonomy with a controlled vocabulary. Moreover, recent studies have demonstrated that among other textual features such as title, description, and user comments, tags are the most effective to support information retrieval (IR) services such as search, automatic classification, and content recommendation. In this context, tag recommendation services aim at assisting users in the tagging process, allowing users to select some of the recommended tags or to come up with new ones. Besides improving user experience, tag recommendation services potentially improve the quality of the generated tags, benefiting IR services that rely on tags as data sources. Besides the obvious benefit of improving the description of the objects, tag recommendation can be directly applied in IR services such as search and query expansion. In this chapter, we will provide the main concepts related to tagging systems, as well as an overview of tag recommendation techniques, dividing them into two stages of the tag recommendation process: (1) the candidate tag extraction and (2) the candidate tag ranking