recommender systems (rs) provide personalized recommendation according to user need by analyzing behavior of users and gathering their information. one of the algorithms used in recommender systems is user-based collaborative filtering (cf) method. the idea is that if users have similar preferences in the past, they will probably have similar preferences in the future. the important part of col...

Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer a given item when making a choice. Over the years, this process has been dependent on robust applications of data mining and machine learning techniques, which are known to have scalability issues when being applied for recommender systems. In this paper, we propose a k-means ...

Contemporary users (players, consumers) of digital games have thousands of products to choose from, which makes finding games that fit their interests challenging. Towards addressing this challenge, in this paper two different formulations of Archetypal Analysis for Top-L recommender tasks using implicit feedback are presented: factorand neighborhood-oriented models. These form the first applic...

Association rules are a simple yet powerful tool for making item-based recommendations. As part of the ECML PKDD 2013 Discovery Challenge, we use association rules to form a name recommender system. We introduce a new measure of association rule confidence that incorporates user similarities, and show that this increases prediction performance. With no special feature engineering and no separat...

Recommender systems apply knowledge discovery techniques to the problem of making personalized product recommendations during a live customer interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success in E-commerce nowadays. The tremendous growth of customers and products in recent years poses some key challenges for recomm...

Recommender systems are an effective solution to the information overload problem, specially in the online world where we are constantly faced with inordinately many choices. These systems try to find the items such as books or movies that match best with users’ preferences. Based on the different approaches to finding the items of interests to users, we can classify the recommender systems int...

The advent of the Semantic Web necessitates paradigm shifts away from centralized client/server architectures towards decentralization and peer-to-peer computation, making the existence of central authorities superfluous and even impossible. At the same time, recommender systems are gaining considerable impact in e-commerce, providing people with recommendations that are personalized and tailor...