web usage mining (wum) is the automatic discovering of hidden informationof user access pattern from the web log data. frequent pattern discovery is one ofthe main techniques in wum that can be used to implement recommender systems,forecast user`s navigational behaviour, and personalize web sites. many algorithmshave been suggested on obtaining frequent user navigation patterns. this paperprese...

Recommender systems support users in €nding relevant items in overloaded information spaces. Researchers and practitioners have proposed many di‚erent collaborative €ltering algorithms for different information scenarios, domains and contexts. One of the laŠer, are time-aware recommender methods that consider temporal dynamics in the users’ interests in certain items, topics, etc. While there i...

Recent advances in electronic media and computer networks have allowed the creation of large and distributed repositories of information. However, the immediate availability of extensive resources for use by broad classes of computer users gives rise to new challenges in everyday life. These challenges arise from the fact that users cannot exploit available resources effectively when the amount...

Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based...

User modelling and personalisation are the key aspects of recommender systems in terms of recommendation quality. While being very efficient and designed to work with huge amounts of data, present recommender systems often lack the facility of user integration when it comes to feedback and direct user modelling. We propose the introduction of dynamic logic programming – an extension of answer s...

Several kinds of features of user navigation paths (e.g., subsets of the resources as nodes of the paths covered, subsequences of the sequences used for path description, path fragments constructed via combination of subsequences and wildcards) can be employed to build recommender systems designed for tasks as di erent as site personalization, cross-/up-selling, and navigation assistance. A voc...

To date, recommender systems are a popular instrument to make personalized suggestions and provide information about items for users. There are many techniques that can be applied for personalization in recommender systems. All these techniques have complementary strengths and weaknesses. A hybrid recommender system combines two or more recommendation techniques to gain better system performanc...

The recommender system research community has become a well-identified and active research community built around the goal of understanding, building, and improving recommender systems. A recurring theme since the first workshop in Berkeley (March 1996) has been the development of substantial research infrastructure—typically publicly-accessible recommendation services—as a tool to conduct rese...

Along with the growth of the Internet, automatic recommender systems have become popular. Due to being intuitive and useful, factorization based models, including the Nonnegative Matrix Factorization (NMF) model, are one of the most common approachs for building recommender systems. In this study, we focus on how a recommender system can be built for online services and how the parameters of an...

Recommender systems are becoming a salient part of many e-commerce websites. Much research has focused on advancing recommendation technologies to improve accuracy of predictions, while behavioral aspects of using recommender systems are often overlooked. In this study, we explore how consumer preferences at the time of consumption are impacted by predictions generated by recommender systems. W...