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Technologies
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Next Generation of Recommender Systems
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Next Generation of Recommender Systems

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: closed (15 April 2019) | Viewed by 29722

Special Issue Editor

Prof. Dr. Fernando Ortega

E-Mail Website
Guest Editor
Dpto. Sistemas Informáticos, ETSI de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
Interests: artificial intelligence; machine learning; recommender systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recommender Systems (RS) have become an essential tool for society. The advent of social networks and the Internet of things have sharpened the information overload problem. RS are the main solution to this problem. RS are intelligent systems that learn the preferences of users and provide them a set of items that fit with those preferences. In other words, RS acts as a filter that allows the passage of relevant items to users and block irrelevant ones. RS have been applied to a wide variety of conexts, such as movies, books, e-commerce, social networks, e-learning, and so on.

RS can be classified according to the type of information that they use to compute recommendations: Content Based Filtering (CBF) provides recommendations based on the features that describes the users and/or items belonging to the RS; Collaborative Filtering (CF) uses the ratings of users regarding items to provide a set of recommendations based on the assumtion that, if users shared the same interest in the past, they will also share the same interest in the future; and Hybrid Filtering (HF) combining CBF and CF to provide better recommendations.

We solicit original submission that improve RS on any of the following topics:

  • collaborative filtering: Similarity metrics, quality measures, matrix factorization, cold start
  • content based filtering: Topic modeling, folksonomies, geo-based recommendations
  • social media data: Recommendations to group of users, followers, time-based recommendations

Dr. Fernando Ortega
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Collaborative Filtering
  • Content Based Filtering
  • Recommender Systems
  • Matrix Factorization

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Published Papers (3 papers)

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Research

16 pages, 302 KiB  
Article
Recommendations with a Nudge
by Randi Karlsen and Anders Andersen
Technologies 2019, 7(2), 45; https://doi.org/10.3390/technologies7020045 - 13 Jun 2019
Cited by 43 | Viewed by 14783
Abstract
In areas such as health, environment, and energy consumption, there is a need to do better. A common goal in society is to get people to behave in ways that are sustainable for the environment or support a healthier lifestyle. Nudging is a [...] Read more.
In areas such as health, environment, and energy consumption, there is a need to do better. A common goal in society is to get people to behave in ways that are sustainable for the environment or support a healthier lifestyle. Nudging is a term known from economics and political theory, for influencing decisions and behavior using suggestions, positive reinforcement, and other non-coercive means. With the extensive use of digital devices, nudging within a digital environment (known as digital nudging) has great potential. We introduce smart nudging, where the guidance of user behavior is presented through digital nudges tailored to be relevant to the current situation of each individual user. The ethics of smart nudging and the transparency of nudging is also discussed. We see a smart nudge as a recommendation to the user, followed by information that both motivates and helps the user choose the suggested behavior. This paper describes such nudgy recommendations, the design of a smart nudge, and an architecture for a smart nudging system. We compare smart nudging to traditional models for recommender systems, and we describe and discuss tools (or approaches) for nudge design. We discuss the challenges of designing personalized smart nudges that evolve and adapt according to the user’s reactions to the previous nudging and possible behavioral change of the user. Full article
(This article belongs to the Special Issue Next Generation of Recommender Systems)
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19 pages, 2019 KiB  
Article
Factors Affecting the Performance of Recommender Systems in a Smart TV Environment
by Iftikhar Alam, Shah Khusro and Mumtaz Khan
Technologies 2019, 7(2), 41; https://doi.org/10.3390/technologies7020041 - 27 May 2019
Cited by 17 | Viewed by 7823
Abstract
The recommender systems are deployed on the Web for reducing cognitive overload. It uses different parameters, such as profile information, feedbacks, history, etc., as input and recommends items to a user or group of users. Such parameters are easy to predict and calculate [...] Read more.
The recommender systems are deployed on the Web for reducing cognitive overload. It uses different parameters, such as profile information, feedbacks, history, etc., as input and recommends items to a user or group of users. Such parameters are easy to predict and calculate for a single user on a personalized device, such as a personal computer or smartphone. However, watching the Web contents on a smart TV is significantly different from other connected devices. For example, the smart TV is a multi-user, lean-back supported device, and normally enjoyed in groups. Moreover, the performance of a recommender system is questionable due to the dynamic interests of groups in front of a smart TV. This paper discussed in detail the existing recommender system approaches in the context of smart TV environment. Moreover, it highlights the issues and challenges in existing recommendations for smart TV viewer(s) and presents some research opportunities to cope with these issues. The paper further reports some overlooked factors that affect the recommendation process on a smart TV. A subjective study of viewers’ watching behavior on a smart TV is also presented for validating these factors. Results show that apart from all technological advancement, the viewers are enjoying smart TV as a passive, lean-back device, and mostly used for watching live channels and videos on the big screen. Furthermore, in most households, smart TV is enjoyed in groups as a shared device which creates hurdles in personalized recommendations. This is because predicting the group members and satisfying each member is still an issue. The findings of this study suggest that for precise and relevant recommendations on smart TVs, the recommender systems need to adapt to the varying watching behavior of viewer(s). Full article
(This article belongs to the Special Issue Next Generation of Recommender Systems)
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15 pages, 2089 KiB  
Article
User Similarity Determination in Social Networks
by Sadia Tariq, Muhammad Saleem and Muhammad Shahbaz
Technologies 2019, 7(2), 36; https://doi.org/10.3390/technologies7020036 - 15 Apr 2019
Cited by 3 | Viewed by 6591
Abstract
Online social networks have provided a promising communication platform for an activity inherently dear to the human heart, to find friends. People are recommended to each other as potential future friends by comparing their profiles which require numerical quantifiers to determine the extent [...] Read more.
Online social networks have provided a promising communication platform for an activity inherently dear to the human heart, to find friends. People are recommended to each other as potential future friends by comparing their profiles which require numerical quantifiers to determine the extent of user similarity. From similarity-based methods to artificial intelligent machine learning methods, several metrics enable us to characterize social networks from different perspectives. This research focuses on the collaborative employment of neighbor based and graphical distance-based similarity measurement methods with text classification tools such as the feature matrix and feature vector. Likeminded nodes are predicted accurately and effectively as compared to other methods. Full article
(This article belongs to the Special Issue Next Generation of Recommender Systems)
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