Recommender systems are software applications that help users to find items of interest in situations information overload. Current research often assumes a one-shot interaction paradigm, where the users’ preferences estimated based on past observed behavior and presentation ranked list suggestions is main, one-directional form user interaction. Conversational recommender (CRS) take different a...

Recommender systems are a subclass of information filtering systems that predict the 'rating' or 'preference' that a user would give to an item. Most traditional Recommender Systems (RSs) focus on recommending the most relevant items to individual users and do not take into consideration the circumstances and other contextual information such as time, place and company of other people when reco...

The increasing diversity of consumers’ demand, as documented by the debate on the long tail of the distribution of sales volume across products, represents a challenge for retail stores. Recommender systems offer a tool to cope with this challenge. The recent developments in information technology and ubiquitous computing makes it feasible to move recommender systems from the on-line commerce, ...

Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decisionmaking processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support S...

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommende...

We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, coverin...

Recommender systems have become an important part users’ everyday interactions with Web based applications, particularly those driving e-commerce. Businesses have come to realize the potential of these personalized and adaptive systems in order to increase sales and to retain customers. Likewise, Web users have come to rely on such systems to help them in more efficiently finding items of inter...

The goal of a Recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Suggestions for books on Amazon, or movies on Netflix, are real world examples of the operation of industry-strength recommender systems. The design of such recommendation engines depends on the domain and the particular characteristics of the data ...

Recommender systems are needed to find food items of one’s interest. We review recommender systems and recommendation methods. We propose a food personalization framework based on adaptive hypermedia. We extend Hermes framework with food recommendation functionality. We combine TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorp...

Recommender systems are needed to find subject items of one’s interest. We review recommender systems and recommendation methods. We propose a subject personalization framework based on adaptive hypermedia for Computer Science ACM Curricula. We extend Hermes framework with subject recommendation functionality. We combine TF-IDF term extraction method with cosine similarity measure. Specializati...