Typical recommender systems adopt a static view of the recommendation process and treat it as a prediction problem. We argue that it is more appropriate to view the problem of generating recommendations as a sequential optimization problem and, consequently, that Markov decision processes (MDPs) provide a more appropriate model for recommender systems. MDPs introduce two benefits: they take int...

Recommender systems are mostly well known for their applications in e-commerce sites and are mostly static models. Classical personalized recommender algorithm includes item-based collaborative filtering method applied in Amazon, matrix factorization based collaborative filtering algorithm from Netflix, etc. In this article, we hope to combine traditional model with behaviour pattern extraction...

Collaborative filtering Recommender systems apply data mining techniques to produce personalized recommender system during the online interaction of active users. These systems use variety of techniques for achieving high success on business, banking, finance and other domains. The fast increase in users and products in recent years produces some of the key issues and challenges for recommender...

Knowledge-based recommender systems comprise one category of user-modeling system that can draw inferences from user models. This brief paper contains a global description of a multi-faceted, educational, knowledge-based recommender system, including a basic set of descriptors that the model contains, a taxonomy of inferences that might be made over such models, and a listing of literature that...

Sentiment classification and recommender systems were until recently completely disjoint domains. Recommender systems exploit the users/items/rates matrix with omitting the available text information. Sentiment classification exploits text reviews and consumers rates to build models for document analysis. In this article we propose an unified model exploiting both text and user, items and rates...

In view of information overflow on the web, the use of recommender systems seems to be an appropriate means by which to organize information that targets preferences. The purpose of this article is to present a novel model explaining the satisfaction with recommender websites integrating emerging influential factors such as trust, exploratory browsing, and personal factors. Three recommender sy...

Assessing learning outcomes for students in higher education institutes is an interesting task with many potential applications for all involved stakeholders (students, administrators, potential employers, etc.). In this paper, we propose a course recommendation system for students based on the assessment of their “graduate attributes” (i.e. attributes that describe the developing values of stu...

Recommender systems add value to vast content resources by matching users with items of interest. In recent years, immense progress has been made in recommendation techniques. The evaluation of these has however not been matched and is threatening to impede the further development of recommender systems. In this paper we propose an approach that addresses this impasse by formulating a novel eva...