Recommender systems are means for web personalization and tailoring the browsing experience to the users’ specific needs. There are two categories of recommender systems; memory-based and model-based systems. In this paper we propose a personalized recommender system for the next page prediction that is based on a hybrid model from both categories. The generalized patterns generated by a model ...

The evaluation and selection of recommender systems is a difficult decision making process. This difficulty is partially due to the large diversity of published evaluation criteria in addition to lack of standardized methods of evaluation. As such, a systematic methodology is needed that explicitly considers multiple, possibly conflicting metrics and assists decision makers to evaluate and find...

We outline the history of recommender systems from their roots in information retrieval and filtering to their role in today’s Internet economy. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. Research in recommender systems lies at the intersection of several areas of computer science...

Existing blended learning models for e-courses should be improved in line with changes in the technology development, new pedagogical approaches, and feedback received from its participants. This paper presents improved model of the blended learning course “Multimedia Systems” designed for students of the undergraduate program in Computer Science at the Department of Informatics of the Universi...

In the current era, a rapid increase in data volume produces redundant information on internet. This predicts appropriate items for users great challenge systems. As result, recommender systems have emerged this decade to resolve such problems. Various e-commerce platforms as Amazon and Netflix prefer using some decent recommend their users. literature, multiple methods matrix factorization col...

Empirical evaluation of user model based adaptive systems is challenging and intriguing due to the nature of user model and adaptive decision processes. Their success also depends on the subjective judgment of the user. It can be done by evaluating the constructed user model and the resulting adaptive decision made based upon the user model. Until recently, literature on comprehensive empirical...

In the CLEF NewsREEL 2017 challenge, we build a delegation model based on the contextual bandit algorithm. Our goal is to investigate whether a bandit approach combined with context extracted from the user side, from the item side and from user-item interaction can help choose the appropriate recommender from a recommender algorithm pool for the incoming recommendation requests. We took part in...