Recommender systems have become essential in many web sites, especially in the e-commerce area; however, they are not extended enough in some domains. In this work, a recommender system for TV series is presented due to the increasing interest for this kind of products. The system implements a methodology that deals with the most important problems of recommender systems.

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...

Recommender system is a topic which falls under the domain of information retrieval, data mining and machine learning. Recommender systems are widely used by famous websites like Amazon, Flipcart, Netflix, Facebook, twitter and many others. There are various types of recommender systems like collaborative filtering, content based filtering and hybrid recommender system. Recommender systems can ...

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...

Recommender System is a process or model that can be applied to identify the user choice based on some statistical observations. In this work, A movie recommender system is defined using cluster cluster adaptive genetic approach. In first phase of this model, the clustering is applied on user dataset to identify the most similar users. Later on, the statistical analysis is applied to generate t...

Recommender systems are acknowledged as an essential instrument to support users in finding relevant information. However, the adaptation of recommender systems to multiple domain-specific requirements and data models still remains an open challenge. In the present paper, we contribute to this sparse line of research with guidance on how to design a customizable recommender system that accounts...

Recommender System is a special type of information filtering system that provides a prediction which helps the user to evaluate items from a huge collection that the user is likely to find interesting or useful. Recommender System is used to produce meaningful suggestions about new items for particular consumers. These recommendations facilitate the users to make decisions in multiple contexts...

This paper addresses conversational interaction in useradaptive recommender systems. By collecting and analyzing a movie recommendation dialogue corpus, two initiative types that need to be accommodated in a conversational recommender dialogue system are identified. The initiative types are modeled in a dialogue strategy suitable for implementation. The approach is exemplified by the MADFILM mo...

Recommender systems apply data mining techniques and prediction algorithms to predict users’ interest on information, products and services among the tremendous amount of available items. The vast growth of information on the Internet as well as number of visitors to websites add some key challenges to recommender systems. These are: producing accurate recommendation, handling many recommendati...

We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed ...