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4 pages, 179 KiB

Open AccessEditorial

New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering

by Diego Pérez-López, Jorge Dueñas-Lerín, Fernando Ortega and Ángel González-Prieto

Appl. Sci. 2023, 13(15), 8845; https://doi.org/10.3390/app13158845 - 31 Jul 2023

Viewed by 1017

Abstract

In recent times, recommender systems (RSs) have been attracting a lot of attention from the research community because of their groundbreaking applications [...] Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

Research

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21 pages, 910 KiB

Open AccessArticle

A Generative Adversarial Network for Financial Advisor Recruitment in Smart Crowdsourcing Platforms

by Raby Hamadi, Hakim Ghazzai and Yehia Massoud

Appl. Sci. 2022, 12(19), 9830; https://doi.org/10.3390/app12199830 - 29 Sep 2022

Cited by 3 | Viewed by 1481

Abstract

Financial portfolio management is a very time-consuming task as it requires the continuous surveying of the market volatility. Investors need to hire potential financial advisors to manage portfolios on their behalf. Efficient hiring of financial advisors not only facilitates their cooperation with investors [...] Read more.

Financial portfolio management is a very time-consuming task as it requires the continuous surveying of the market volatility. Investors need to hire potential financial advisors to manage portfolios on their behalf. Efficient hiring of financial advisors not only facilitates their cooperation with investors but also guarantees optimized portfolio returns and hence, optimized benefits for the two entities. In this paper, we propose to tackle the portfolio optimization problem by efficiently matching financial advisors to investors. To this end, we model the problem as an automated crowdsourcing platform to organize the cooperation between the different actors based on their features. The recruitment of financial advisors is performed using a Generative Adversarial Network (GAN) that extrapolates the problem to an image processing task where financial advisors’ features are encapsulated in gray-scale images. Hence, the GAN is trained to generate, based on an investor profile given as an input, the ’ideal’ financial advisor profile. Afterwards, we measure the level of similarity between the generated ideal profiles and the existing profiles in the crowdsourcing database to perform a low complexity, many-to-many investor-to-financial advisor matching. In the simulations, intensive tests were performed to show the convergence and effectiveness of the proposed GAN-based solution. We have shown that the proposed method achieves more than 17% of the average expected return compared to baseline approaches. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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19 pages, 2040 KiB

Open AccessArticle

Resolving Data Sparsity via Aggregating Graph-Based User–App–Location Association for Location Recommendations

by Xiang Chen, Junxin Chen, Xiaoqin Lian and Weimin Mai

Appl. Sci. 2022, 12(14), 6882; https://doi.org/10.3390/app12146882 - 7 Jul 2022

Cited by 2 | Viewed by 1233

Abstract

Personalized location recommendations aim to recommend places that users want to visit, which can save their decision-making time in daily life. However, the recommending task faces a serious data sparsity problem because users have only visited a small part of total places in [...] Read more.

Personalized location recommendations aim to recommend places that users want to visit, which can save their decision-making time in daily life. However, the recommending task faces a serious data sparsity problem because users have only visited a small part of total places in a city. This problem directly leads to the difficulty in learning latent representations of users and locations. In order to tackle the data sparsity problem and make better recommendations, users’ app usage records in different locations are introduced to compensated for both users’ interests and locations’ characteristics in this paper. An attributed graph-based representation model is proposed to dig out user–app–location associations with high-order features aggregated. Extensive experiments prove that better representations of users and locations are obtained by our proposed model, thus it greatly improves location recommendation performances compared with the state-of-art methods. For example, our model achieves 13.20%, 10.1%, and 9.44% higher performance than the state-of-art (SOTA) models in

T o p 3 H i t r a t e

,

T o p 3 A c c u r a c y

, and

n D C G_{3}

, respectively, in the Telecom dataset. In the TalkingData dataset, our model achieves 9.34%, 13.35%, and 8.56% better performance than the SOTA models in

T o p 2 H i t r a t e

,

T o p 2 A c c u r a c y

, and

n D C G_{2}

, respectively. Furthermore, numerical results demonstrate that our model can effectively alleviate the data sparsity problem in recommendation systems. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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15 pages, 405 KiB

Open AccessArticle

Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System

by Raúl Lara-Cabrera, Álvaro González, Fernando Ortega and Ángel González-Prieto

Appl. Sci. 2022, 12(3), 1223; https://doi.org/10.3390/app12031223 - 24 Jan 2022

Cited by 3 | Viewed by 2325

Abstract

Traditionally, recommender systems have been approached as regression models aiming to predict the score that a user would give to a particular item. In this work, we propose a recommender system that tackles the problem as a classification task instead of as a [...] Read more.

Traditionally, recommender systems have been approached as regression models aiming to predict the score that a user would give to a particular item. In this work, we propose a recommender system that tackles the problem as a classification task instead of as a regression. The new model, Dirichlet Matrix Factorization (DirMF), provides not only a prediction but also its reliability, hence achieving a better balance between the quality and quantity of the predictions (i.e., reducing the prediction error by limiting the model’s coverage). The experimental results conducted show that the proposed model outperforms other models due to its ability to discard unreliable predictions. Compared to our previous model, which uses the same classification approach, DirMF shows a similar efficiency, outperforming the former on some of the datasets included in the experimental setup. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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16 pages, 3607 KiB

Open AccessArticle

Enhanced Collaborative Filtering for Personalized E-Government Recommendation

by Ninghua Sun, Tao Chen, Wenshan Guo and Longya Ran

Appl. Sci. 2021, 11(24), 12119; https://doi.org/10.3390/app112412119 - 20 Dec 2021

Cited by 8 | Viewed by 2504

Abstract

The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority [...] Read more.

The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and users by the latent features inferred from the user–item interaction matrix. A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback. This problem negatively affected the performance of the recommendation system in e-government. In this paper, we firstly propose to learn positive items’ latent features by leveraging both the negative item information and the original embedding features. We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system. Such mixing information is beneficial for extending the expressiveness of the latent features. Comprehensive experimentation on a real-world e-government dataset showed that our approach improved the performance significantly compared with the state-of-the-art baseline algorithms. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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14 pages, 1088 KiB

Open AccessArticle

Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

by Silvana Vanesa Aciar, Ramon Fabregat, Teodor Jové and Gabriela Aciar

Appl. Sci. 2021, 11(24), 11890; https://doi.org/10.3390/app112411890 - 14 Dec 2021

Cited by 1 | Viewed by 2433

Abstract

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary [...] Read more.

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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12 pages, 2857 KiB

Open AccessArticle

A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations

by Dehai Zhang, Xiaobo Yang, Linan Liu and Qing Liu

Appl. Sci. 2021, 11(21), 10432; https://doi.org/10.3390/app112110432 - 5 Nov 2021

Cited by 4 | Viewed by 2490

Abstract

In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we [...] Read more.

In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KANR, a knowledge graph-enhanced attention aggregation network for making recommendations. This is an end-to-end deep learning model using knowledge graph embedding to enhance the attention aggregation network for making recommendations. It consists of three main parts. The first is the attention aggregation network, which collect the user’s interaction history and captures the user’s preference for each item. The second is the knowledge graph-embedded model, which aims to integrate the knowledge. The semantic information of the nodes and edges in the graph is mapped to the low-dimensional vector space. The final part is the information interaction unit, which is used for fusing the features of two vectors. Experiments showed that our model achieved a stable improvement compared to the baseline model in making recommendations for movies, books, and music. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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18 pages, 7620 KiB

Open AccessArticle

A Deep Learning-Based Course Recommender System for Sustainable Development in Education

by Qinglong Li and Jaekyeong Kim

Appl. Sci. 2021, 11(19), 8993; https://doi.org/10.3390/app11198993 - 27 Sep 2021

Cited by 19 | Viewed by 5305

Abstract

Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system [...] Read more.

Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system has the essential role of improving the learning efficiency of users. At present, many online education platforms have built diverse recommender systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite the development and contributions of many recommender systems based on CF, diverse deep learning models for personalized recommendation are being studied because of problems such as sparsity and scalability. Therefore, to solve traditional recommendation problems, this study proposes a novel deep learning-based course recommender system (DECOR), which elaborately captures high-level user behaviors and course attribute features. The DECOR model can reduce information overload, solve high-dimensional data sparsity problems, and achieve high feature information extraction performance. We perform several experiments utilizing real-world datasets to evaluate the DECOR model’s performance compared with that of traditional recommendation approaches. The experimental results indicate that the DECOR model offers better and more robust recommendation performance than the traditional methods. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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15 pages, 1332 KiB

Open AccessArticle

Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering

by Dionisis Margaris, Dimitris Spiliotopoulos and Costas Vassilakis

Appl. Sci. 2021, 11(18), 8369; https://doi.org/10.3390/app11188369 - 9 Sep 2021

Cited by 3 | Viewed by 1527

Abstract

In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users’ ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of [...] Read more.

In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users’ ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of ‘black sheep near neighbours’, which are pairs of near neighbours with opinion agreement on items that deviates from the dominant community opinion on the same item. The presented work substantiates that the weights of near neighbours can be adjusted, based on the degree to which the target user and the near neighbour deviate from the dominant ratings for each item. This concept can be utilized in various other CF algorithms. The experimental evaluation was conducted on six datasets broadly used in CF research, using two user similarity metrics and two rating prediction error metrics. The results show that the proposed technique increases rating prediction accuracy both when used independently and when combined with other CF algorithms. The proposed algorithm is designed to work without the requirements to utilise any supplementary sources of information, such as user relations in social networks and detailed item descriptions. The aforesaid point out both the efficacy and the applicability of the proposed work. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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29 pages, 2288 KiB

Open AccessArticle

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

by Jonathan Ayebakuro Orama, Joan Borràs and Antonio Moreno

Appl. Sci. 2021, 11(14), 6512; https://doi.org/10.3390/app11146512 - 15 Jul 2021

Cited by 8 | Viewed by 2913

Abstract

Tourists who visit a city for the first time may find it difficult to decide on places to visit, as the amount of information in the Web about cultural and leisure activities may be large. Recommender systems address this problem by suggesting the [...] Read more.

Tourists who visit a city for the first time may find it difficult to decide on places to visit, as the amount of information in the Web about cultural and leisure activities may be large. Recommender systems address this problem by suggesting the points of interest that fit better with the user’s preferences. This paper presents a novel recommender system that leverages tweets to build user profiles, taking into account not only their personal preferences but also their travel habits. Association rules, which are mined from the previous visits of users documented on Twitter, are used to make the final recommendations of places to visit. The system has been applied to data of the city of Barcelona, and the results show that the use of the social media-based clustering procedure increases its performance according to several relevant metrics. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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18 pages, 318 KiB

Open AccessArticle

New Vector-Space Embeddings for Recommender Systems

by Sandra Rizkallah, Amir F. Atiya and Samir Shaheen

Appl. Sci. 2021, 11(14), 6477; https://doi.org/10.3390/app11146477 - 13 Jul 2021

Cited by 4 | Viewed by 2747

Abstract

In this work, we propose a novel recommender system model based on a technology commonly used in natural language processing called word vector embedding. In this technology, a word is represented by a vector that is embedded in an n-dimensional space. The [...] Read more.

In this work, we propose a novel recommender system model based on a technology commonly used in natural language processing called word vector embedding. In this technology, a word is represented by a vector that is embedded in an n-dimensional space. The distance between two vectors expresses the level of similarity/dissimilarity of their underlying words. Since item similarities and user similarities are the basis of designing a successful collaborative filtering, vector embedding seems to be a good candidate. As opposed to words, we propose a vector embedding approach for learning vectors for items and users. There have been very few recent applications of vector embeddings in recommender systems, but they have limitations in the type of formulations that are applicable. We propose a novel vector embedding that is versatile, in the sense that it is applicable for the prediction of ratings and for the recommendation of top items that are likely to appeal to users. It could also possibly take into account content-based features and demographic information. The approach is a simple relaxation algorithm that optimizes an objective function, defined based on target users’, items’ or joint user–item’s similarities in their respective vector spaces. The proposed approach is evaluated using real life datasets such as “MovieLens”, “ModCloth”, “Amazon: Magazine_Subscriptions” and “Online Retail”. The obtained results are compared with some of the leading benchmark methods, and they show a competitive performance. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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14 pages, 1195 KiB

Open AccessArticle

Sequential Recommendations on GitHub Repository

by JaeWon Kim, JeongA Wi and YoungBin Kim

Appl. Sci. 2021, 11(4), 1585; https://doi.org/10.3390/app11041585 - 10 Feb 2021

Cited by 5 | Viewed by 3104

Abstract

The software development platform is an increasingly expanding industry. It is growing steadily due to the active research and sharing of artificial intelligence and deep learning. Further, predicting users’ propensity in this huge community and recommending a new repository is beneficial for researchers [...] Read more.

The software development platform is an increasingly expanding industry. It is growing steadily due to the active research and sharing of artificial intelligence and deep learning. Further, predicting users’ propensity in this huge community and recommending a new repository is beneficial for researchers and users. Despite this, only a few researches have been done on the recommendation system of such platforms. In this study, we propose a method to model extensive user data of an online community with a deep learning-based recommendation system. This study shows that a new repository can be effectively recommended based on the accumulated big data from the user. Moreover, this study is the first study of the sequential recommendation system that provides a new dataset of a software development platform, which is as large as the prevailing datasets. The experiments show that the proposed dataset can be practiced in various recommendation tasks. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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22 pages, 3689 KiB

Open AccessArticle

Incorporating Similarity Measures to Optimize Graph Convolutional Neural Networks for Product Recommendation

by Wafa Shafqat and Yung-Cheol Byun

Appl. Sci. 2021, 11(4), 1366; https://doi.org/10.3390/app11041366 - 3 Feb 2021

Cited by 13 | Viewed by 2743

Abstract

With the ever-growing amount of online data and information, recommender systems are becoming overwhelmingly popular as an adequate approach for overcoming the challenge of information overload. Artificial Intelligence (AI) and Deep Learning (DL) have accumulated significant interest in many research areas, and recommender [...] Read more.

With the ever-growing amount of online data and information, recommender systems are becoming overwhelmingly popular as an adequate approach for overcoming the challenge of information overload. Artificial Intelligence (AI) and Deep Learning (DL) have accumulated significant interest in many research areas, and recommender systems are one of them. In this paper, a Graph Convolutional Neural Network (GCNN)-based approach was used for online product recommendation. Graph-based methods have undergone substantial consideration for several recommendation tasks, with effective results. However, handling the computational complexities and training large datasets remain a challenge for such a model. Even though they are useful, the excessive measure of the model’s boundaries obstructs their applications in real-world recommender frameworks to a great extent. The recursive way of generating neighbor node embeddings for each node in the graph makes it more challenging to train a deep and large GCNN model. Therefore, we propose a model that incorporates measures of similarity between two different nodes, and these similarity measures help us to sample the neighbors beforehand. We estimate the similarity based on their interaction probability distribution with other nodes. We use KL divergence on different probability distributions to find the distance between them. This way, we set a threshold criterion for neighbor selection and generate other clusters. These clusters are then converted to subgraphs and are used as input for the proposed GCNN model. This approach simplifies the task of neighbor sampling for GCNN, and hence, we can observe a significant improvement in the computational complexity of the GCNN model. Finally, we compared the results with those for the previously proposed OpGCN model, basic GCNN model, and other traditional approaches such as collaborative filtering and probabilistic matrix factorization. The experiments showed that the complexity and computational time were decreased by estimating the similarity among nodes and sampling the nodes before training. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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Review

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19 pages, 366 KiB

Open AccessReview

Recommender Systems in the Real Estate Market—A Survey

by Alireza Gharahighehi, Konstantinos Pliakos and Celine Vens

Appl. Sci. 2021, 11(16), 7502; https://doi.org/10.3390/app11167502 - 16 Aug 2021

Cited by 7 | Viewed by 6732

Abstract

The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms in finding the best possible properties [...] Read more.

The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms in finding the best possible properties that fulfill their needs. However, the recommendation task is substantially more challenging in the real estate domain due to the many domain-specific limitations that impair typical recommender systems. For instance, real estate recommender systems usually face the clod-start problem where there are no historical logs for new users or new items, and the recommender system should provide recommendations for these new entities. Therefore, the recommender systems in the real estate market are different and substantially less studied than in other domains. In this article, we aim at providing a comprehensive and systematic literature review on applications of recommender systems in the real estate market. We evaluate a set of research articles (13 journal and 13 conference papers) which represent the majority of research and commercial solutions proposed in the field of real estate recommender systems. These papers have been reviewed and categorized based on their methodological approaches, the main challenges that they addressed, and their evaluation procedures. Based on these categorizations, we outlined some possible directions for future research. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering)

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