Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet. This paper aims develop new hybrid food recommender-system overcome the shortcomings of previous systems, such as ignoring ingredients, time factor, cold start users, items community aspects. The proposed method involves two phases: content-based recommendation user-b...

This article illustrates the vivid research field of hybrid and context-aware recommender systems. Moreover, two own approaches to deal with context-awareness in recommender systems, are described in detail.

Characterization. Adopting the formalisms of [3], we can describe the problem at a more formal, abstract level as follows. Let C be a set of users and I a set of recommendable items. In contrast to matrix-completion problems, we are not interested in predicting a utility value for each i ∈ I and for each c ∈ C , but in computing an ordered list of objects L of length k for each user, where each...

The influence of emotions in decision making is a popular research topic in psychology and cognitive studies. A person facing a choosing problem has to consider different solutions and take a decision. During this process several elements influence the reasoning, some of them are rational, others are irrational, such as emotions. Recommender Systems can be used to support decision making by nar...

Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer’ reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more time-consuming. This s...

cold start is one of the main challenges in recommender systems. solving sparsechallenge of cold start users is hard. more cold start users and items are new. sine many general methods for recommender systems has over fittingon cold start users and items, so recommendation to new users and items is important and hard duty. in this work to overcome sparse problem, we present a new method for rec...