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Recommender Systems: Approaches, Challenges and Applications

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 25623

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Prof. Dr. Mehdi Elahi

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Guest Editor

Department of Information Science and Media Studies, University of Bergen, Postboks 7802, 5020 Bergen, Norway
Interests: recommender systems; active learning; human–computer interaction; decision making

Prof. Dr. Amin Beheshti

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Guest Editor

Department of Computing, Data Analytics Lab, Macquarie University, Sydney, NSW 2109, Australia
Interests: artificial intelligence; machine learning; health data analytics
Special Issues, Collections and Topics in MDPI journals

Dr. Mohammad Sina Kiarostami

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Guest Editor

School of Computer Sciences, University of Oulu, Oulu, Finland
Interests: artificial intelligence; computer games; robotics; human–computer interaction

Special Issue Information

Dear Colleagues,

Over the past few years, recommender systems have gained substantial attention, and a variety of approaches have been developed in many application domains. Classical recommender systems have mainly adopted techniques based on content-based filtering and collaborative filtering, while modern recommender systems have gone beyond these techniques by employing more recent machine learning methods. This has enabled them to generate novel forms of recommendation such as sequential, conversational, and attention-aware recommendation capable of taking into account the benefit of multi-stakeholders. From an evaluation point of view, while early research on recommender systems has mainly focused on “accuracy”, the latest research has adopted other novel metrics such as diversity, user experience, coverage, fairness, trust, and transparency.

In this Special Issue on “Recommender Systems: Approaches, Challenges, and Applications”, we aim to form a reference point in this research. Accordingly, we invite researchers to present their latest findings in this area and welcome their original and unpublished submissions related to:

Trust and transparency;
Group recommender systems;
Multi-stakeholder approaches;
Cross-domain recommendations;
Sequential recommender systems;
Visually aware recommender systems;
Recommendation based on deep learning;
Active learning for recommender systems;
Exploiting user cognition for recommendation;
Context-aware recommender systems (CARS);
Explanations methods for recommender systems;
Novelty, diversity, or serendipity in recommender systems;
Further relevant topics

Prof. Dr. Mehdi Elahi
Prof. Dr. Amin Beheshti
Dr. Mohammad Sina Kiarostami
Guest Editors

Manuscript Submission Information

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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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

recommender systems
collaborative filtering
content-based filtering
ranking prediction
deep learning
cold start

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Published Papers (11 papers)

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Research

12 pages, 2053 KiB

Open AccessArticle

Designing Multi-Modal Embedding Fusion-Based Recommender

by Anna Wróblewska, Jacek Dąbrowski, Michał Pastuszak, Andrzej Michałowski, Michał Daniluk, Barbara Rychalska, Mikołaj Wieczorek and Sylwia Sysko-Romańczuk

Electronics 2022, 11(9), 1391; https://doi.org/10.3390/electronics11091391 - 27 Apr 2022

Cited by 6 | Viewed by 2088

Abstract

Recommendation systems have lately been popularised globally. However, often they need to be adapted to particular data and the use case. We have developed a machine learning-based recommendation system, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our system supports multiple types of interaction data with various modalities of metadata through a multi-modal fusion of different data representations. We deployed the system into numerous e-commerce stores, e.g., food and beverages, shoes, fashion items, and telecom operators. We present our system and its main algorithms for data representations and multi-modal fusion. We show benchmark results on open datasets that outperform the state-of-the-art prior work. We also demonstrate use cases for different e-commerce sites. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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34 pages, 3540 KiB

Open AccessArticle

Applying Collective Intelligence in Health Recommender Systems for Smoking Cessation: A Comparison Trial

by Santiago Hors-Fraile, Math J. J. M. Candel, Francine Schneider, Shwetambara Malwade, Francisco J. Nunez-Benjumea, Shabbir Syed-Abdul, Luis Fernandez-Luque and Hein de Vries

Electronics 2022, 11(8), 1219; https://doi.org/10.3390/electronics11081219 - 12 Apr 2022

Cited by 3 | Viewed by 2333

Abstract

Background: Health recommender systems (HRSs) are intelligent systems that can be used to tailor digital health interventions. We compared two HRSs to assess their impact providing smoking cessation support messages. Methods: Smokers who downloaded a mobile app to support smoking abstinence were randomly assigned to two interventions. They received personalized, ratable motivational messages on the app. The first intervention had a knowledge-based HRS (n = 181): it selected random messages from a subset matching the users’ demographics and smoking habits. The second intervention had a hybrid HRS using collective intelligence (n = 190): it selected messages applying the knowledge-based filter first, and then chose the ones with higher ratings provided by other similar users in the system. Both interventions were compared on: (a) message appreciation, (b) engagement with the system, and (c) one’s own self-reported smoking cessation status, as indicated by the last seven-day point prevalence report in different time intervals during a period of six months. Results: Both interventions had similar message appreciation, number of rated messages, and abstinence results. The knowledge-based HRS achieved a significantly higher number of active days, number of abstinence reports, and better abstinence results. The hybrid algorithm led to more quitting attempts in participants who completed their user profiles. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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19 pages, 1274 KiB

Open AccessArticle

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

by María Cora Urdaneta-Ponte, Ibon Oleagordia-Ruíz and Amaia Méndez-Zorrilla

Electronics 2022, 11(8), 1190; https://doi.org/10.3390/electronics11081190 - 08 Apr 2022

Cited by 6 | Viewed by 2302

Abstract

Nowadays, social networks have become highly relevant in the professional field, in terms of the possibility of sharing profiles, skills and jobs. LinkedIn has become the social network par excellence, owing to its content in professional and training information and where there are also endorsements, which are validations of the skills of users that can be taken into account in the recruitment process, as well as in the recommender system. In order to determine how endorsements influence Lifelong Learning course recommendations for professional skills development and enhancement, a new version of our Lifelong Learning course recommendation system is proposed. The recommender system is based on ontology, which allows modelling the data of knowledge areas and job performance sectors to represent professional skills of users obtained from social networks. Machine learning techniques are applied to group entities in the ontology and make predictions of new data. The recommender system has a semantic core, content-based filtering, and heuristics to perform the formative suggestion. In order to validate the data model and test the recommender system, information was obtained from web-based lifelong learning courses and information was collected from LinkedIn professional profiles, incorporating the skills endorsements into the user profile. All possible settings of the system were tested. The best result was obtained in the setting based on the spatial clustering algorithm based on the density of noisy applications. An accuracy of 94% and 80% recall was obtained. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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17 pages, 1655 KiB

Open AccessArticle

A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System

by Longyin Cui and Xiwei Wang

Electronics 2022, 11(7), 1153; https://doi.org/10.3390/electronics11071153 - 06 Apr 2022

Cited by 2 | Viewed by 1720

Abstract

Point-of-interest (POI) recommender systems (RSes) have gained significant popularity in recent years due to the prosperity of location-based social networks (LBSN). However, in the interest of personalization services, various sensitive contextual information is collected, causing potential privacy concerns. This paper proposes a cascaded privacy-preserving POI recommendation (CRS) framework that protects contextual information such as user comments and locations. We demonstrate a minimized trade-off between the privacy-preserving feature and prediction accuracy by applying a semi-decentralized model to real-world datasets. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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14 pages, 1499 KiB

Open AccessArticle

Personalized Tour Recommendation via Analyzing User Tastes for Travel Distance, Diversity and Popularity

by Jongsoo Lee, Jung Ah Shin, Dong-Kyu Chae and Sang-Chul Lee

Electronics 2022, 11(7), 1120; https://doi.org/10.3390/electronics11071120 - 01 Apr 2022

Cited by 2 | Viewed by 1803

Abstract

The goal of a tour recommendation is to recommend the best destinations according to the preferences of each tourist. The task of tour recommendation is challenging in that it not only has to consider the ratings, as do existing traditional recommendation problems, but it must also consider the personalization of the unique characteristics, such as diversity, travel distance, and popularity of the travel destination, which previous studies have failed to take into account. In this paper, we propose, for the first time, aspect personalization: we find out how important each user considers the diversity, distance and popularity of a travel destination when choosing where to visit. Then, we provide recommendations on tourist attractions by combining the personalized score for each factor and the predicted score. For the evaluation, we gathered user ratings and metadata of POIs from TripAdvisor and Naver. Experimental results showed that the proposed method had an 82%, 24% and 20% improvement in precision and a 129%, 35% and 22% improvement in recall in terms of top-1, top-2 and top-3 recommendations. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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15 pages, 372 KiB

Open AccessArticle

Recommending Reforming Trip to a Group of Users

by Rizwan Abbas, Gehad Abdullah Amran, Ahmed Alsanad, Shengjun Ma, Faisal Abdulaziz Almisned, Jianfeng Huang, Ali Ahmed Al Bakhrani, Almesbahi Belal Ahmed and Ahmed Ibrahim Alzahrani

Electronics 2022, 11(7), 1037; https://doi.org/10.3390/electronics11071037 - 25 Mar 2022

Cited by 3 | Viewed by 1747

Abstract

With the quick evolution of mobile apps and trip guidance technologies, a trip recommender that recommends sequential points of interest (POIs) to travelers has emerged and recently received popularity. Compared to other outing recommenders, which suggest the following single POI, our proposed trip proposal research centers around the POI sequence proposal. An advanced sequence of the POI recommendation system named Recommending Reforming Trip (RRT) is presented, recommending a dynamic sequence of POIs to a group of users. It displays the information progression in a verifiable direction, and the output produced is the arrangement of POIs to be expected for a group of users. A successful plan is executed depending upon the deep neural network (DNN) to take care of this sequence-to-sequence problem. From start to finish of the work process, RRT can permit the input to change over time by smoothly recommending a dynamic sequence of POIs. Moreover, two advanced new estimations, adjusted precision (AP) and sequence-mindful precision (SMP), are introduced to analyze the recommended precision of a sequence of POIs. It considers the POIs’ consistency and also meets the sequence of order. We evaluate our algorithm using users’ travel histories extracted from a Weeplaces dataset. We argue that our algorithm outperforms various benchmarks by satisfying user interests in the trips. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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28 pages, 2808 KiB

Open AccessArticle

Trust-Based Recommendation for Shared Mobility Systems Based on a Discrete Self-Adaptive Neighborhood Search Differential Evolution Algorithm

by Fu-Shiung Hsieh

Electronics 2022, 11(5), 776; https://doi.org/10.3390/electronics11050776 - 02 Mar 2022

Cited by 13 | Viewed by 1329

Abstract

Safety is one concern that hinders the acceptance of ridesharing in the general public. Several studies have been conducted on the trust issue in recent years to relieve this concern. The introduction of trust in ridesharing systems provides a pragmatic approach to solving this problem. In this study, we will develop a trust-aware ridesharing recommender system decision model to generate recommendations for drivers and passengers. The requirements of trust for both sides, drivers and passengers, are taken into consideration in the decision model proposed in this paper. The decision model considers the factors in typical ridesharing systems, including vehicle capacities, timing, location and trust requirements, etc. The decision model aims to determine the shared rides that minimize cost while respecting the trust and relevant constraints. As the decision problem is a nonlinear integer programming problem, we combine a self-adaptive neighborhood search with Differential Evolution to develop an algorithm to solve it. To assess the effectiveness of the proposed algorithm, several other evolutionary computation approaches are also applied to solve the same problem. The effectiveness assessment is done based on the performance of applying different algorithms to find solutions for test cases, to provide a guideline for selecting a proper solution approach. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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20 pages, 15051 KiB

Open AccessArticle

Enhancing Knowledge of Propagation-Perception-Based Attention Recommender Systems

by Hanzhong Zhang, Yinglong Wang, Chao Chen, Ruixia Liu, Shuwang Zhou and Tianlei Gao

Electronics 2022, 11(4), 547; https://doi.org/10.3390/electronics11040547 - 11 Feb 2022

Cited by 3 | Viewed by 1587

Abstract

Researchers have introduced side information such as social networks or knowledge graphs to alleviate the problems of data sparsity and cold starts in recommendation systems. However, most of the methods ignore the exploration of feature differentiation aspects in the knowledge propagation process. To solve the above problem, we propose a new attention recommendation method based on an enhanced knowledge propagation perception. Specifically, to capture user preferences in a fine-grained manner in a knowledge graph, an asymmetric semantic attention mechanism is adopted. It identifies the influence of propagation neighbors on user preferences through a more precise representation of the preference semantics for head and tail entities. Furthermore, in consideration of the memory and generalization of different propagation depth features and adaptively adjusting the propagation weights, a new propagation feature exploration framework is designed. The performance of the proposed model is validated by two real-world datasets. The baseline model averagely increases by 9.65% and 9.15% for the Area Under Curve (AUC) and Accuracy (ACC) indicators, which proves the effectiveness of the model. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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22 pages, 766 KiB

Open AccessArticle

An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

by Oumaima Stitini, Soulaimane Kaloun and Omar Bencharef

Electronics 2022, 11(2), 242; https://doi.org/10.3390/electronics11020242 - 13 Jan 2022

Cited by 18 | Viewed by 3607

Abstract

Nowadays, recommendation systems offer a method of facilitating the user’s desire. It is useful for recommending items from a variety of areas such as in the e-commerce, medical, education, tourism, and industry domains. The e-commerce area represents the most active research we found, which assists users in locating the things they want. A recommender system can also provide users with helpful knowledge about things that could be of interest. Sometimes, the user gets bored with recommendations which are similar to their profiles, which leads to the over-specialization problem. Over-specialization is caused by limited content data, under which content-based recommendation algorithms suggest goods directly related to the customer profile rather than new things. In this study, we are particularly interested in recommending surprising, new, and unexpected items that may likely be enjoyed by users and will mitigate this limited content. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved using genetic algorithms that brings diversity to recommendations being made. This paper describes a Revolutionary Recommender System using a Genetic Algorithm called

{R R S}_{G A}

which improves the fitness functions for recommending optimal results. The proposed approach employs a genetic algorithm to address the over-specialization issue of content-based filtering. The proposed method aims to incorporate genetic algorithms that bring variety to recommendations and efficiently adjust and suggest unpredictable and innovative things to the user. Experiments objectively demonstrate that our technology can recommend additional products that every consumer is likely to appreciate. The results of

{R R S}_{G A}

have been compared against recommendation results from the content-based filtering approach. The results indicate the effectiveness of

{R R S}_{G A}

and its capacity to make more accurate predictions than alternative approaches. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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25 pages, 1364 KiB

Open AccessArticle

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

by Shu Wang, Chonghuan Xu, Austin Shijun Ding and Zhongyun Tang

Electronics 2021, 10(15), 1769; https://doi.org/10.3390/electronics10151769 - 24 Jul 2021

Cited by 9 | Viewed by 2744

Abstract

Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a representation of music emotion using low-level audio features and music metadata, model the users’ emotion states using an artificial emotion generation model with endogenous factors exogenous factors capable of expressing the influences posed by events on emotions. The two models were trained using a designed deep neural network architecture (emoDNN) to predict the music emotions for the music and the music emotion preferences for the users in a continuous form. Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations. Experiment results show that emoMR performs better in the metrics of

P r e c i s i o n

R e c a l l

F 1

, and

H i t R a t e

than the other baseline algorithms. We also tested the performance of emoMR on two major events (the death of Yuan Longping and the Coronavirus Disease 2019 (COVID-19) cases in Zhejiang). Results show that emoMR takes advantage of event information and outperforms other baseline algorithms. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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17 pages, 10679 KiB

Open AccessArticle

A Dual-Attention Autoencoder Network for Efficient Recommendation System

by Chao Duan, Jianwen Sun, Kaiqi Li and Qing Li

Electronics 2021, 10(13), 1581; https://doi.org/10.3390/electronics10131581 - 30 Jun 2021

Cited by 4 | Viewed by 1954

Abstract

Accelerated development of mobile networks and applications leads to the exponential expansion of resources, which causes problems such as trek and overload of information. One of the practical approaches to ease these problems is recommendation systems (RSs) that can provide individualized service. Video recommendation is one of the most critical recommendation services. However, achieving satisfactory recommendation service on the sparse data is difficult for video recommendation service. Moreover, the cold start problem further exacerbates the research challenge. Recent state-of-the-art works attempted to solve this problem by utilizing the user and item information from some other perspective. However, the significance of user and item information changes under different applications. This paper proposes an autoencoder model to improve recommendation efficiency by utilizing attribute information and implementing the proposed algorithm for video recommendation. In the proposed model, we first extract the user features and the video features by combining the user attribute and the video category information simultaneously. Then, we integrate the attention mechanism into the extracted features to generate the vital features. Finally, we incorporate the user and item potential factor to generate the probability matrix and defines the user-item rating matrix using the factorized probability matrix. Experimental results on two shared datasets demonstrates that the proposed model can effectively ameliorate video recommendation quality compared with the state-of-the-art methods. Full article

(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications)

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