scholarly journals Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them

2018 ◽  
Author(s):  
Antonio Rago ◽  
Oana Cocarascu ◽  
Francesca Toni
Keyword(s):  
Recommender Systems ◽  
Hybrid Method ◽  
State Of The Art ◽  
Significant Problem ◽  
Improve Quality ◽  
Weak Property ◽  
Art Methods

A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations.

scholarly journals Smoothing vs. sharpening of colour images: Together or separated

2017 ◽  
Vol 2 (1) ◽  
pp. 299-316 ◽  
Author(s):  
Cristina Pérez-Benito ◽  
Samuel Morillas ◽  
Cristina Jordán ◽  
J. Alberto Conejero
Keyword(s):  
Image Denoising ◽  
Gaussian Distribution ◽  
State Of The Art ◽  
Visual Quality ◽  
The State ◽  
Art Methods ◽  
Efficiency And Effectiveness

AbstractIt is still a challenge to improve the efficiency and effectiveness of image denoising and enhancement methods. There exists denoising and enhancement methods that are able to improve visual quality of images. This is usually obtained by removing noise while sharpening details and improving edges contrast. Smoothing refers to the case of denoising when noise follows a Gaussian distribution.Both operations, smoothing noise and sharpening, have an opposite nature. Therefore, there are few approaches that simultaneously respond to both goals. We will review these methods and we will also provide a detailed study of the state-of-the-art methods that attack both problems in colour images, separately.

An Effective Recommender System Based on Clustering Technique for TED Talks

2020 ◽  
Vol 15 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Faiz Maazouzi ◽  
Hafed Zarzour ◽  
Yaser Jararweh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Enormous Amount ◽  
Powerful Mechanism ◽  
Ted Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

scholarly journals Preventing delirium in older people

2017 ◽  
Vol 3 (4) ◽  
Author(s):  
Alessandra Anzuini ◽  
Francesca Massariello ◽  
Giuseppe Bellelli
Keyword(s):  
Quality Of Care ◽  
Older People ◽  
State Of The Art ◽  
Altered Mental Status ◽  
Surgical Patients ◽  
Geriatric Syndrome ◽  
Improve Quality ◽  
Significant Burden ◽  
Fluctuating Course

Delirium is a geriatric syndrome, characterized by acutely altered mental status with inattention, fluctuating course and global cognitive dysfunction, which is associated with a significant burden in terms of negative outcomes and costs of care. Delirium is frequently undetected despite its prevalence and incidence are relevant. In this brief report, we report the state of the art in terms of prevention for both medical and surgical patients. A non-pharmacological approach seems to be the more promising method to prevent delirium and improve quality of care for people at risk.

State of the Art Recommendation Approaches

2020 ◽  
pp. 1621-1651
Author(s):  
Bhupesh Rawat ◽  
Sanjay K. Dwivedi
Keyword(s):  
Recommender Systems ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
New Techniques ◽  
Contextual Data ◽  
Additional Information ◽  
E Learning ◽  
Traditional Approaches

Recommender systems have been used successfully in order to deal with information overload problems in a wide variety of domains ranging from e-commerce, e-tourism, to e-learning. They typically predict the ratings of unseen items by a user and recommend the top N items based on user's profile. Moreover, the profile can be enriched further by using additional information such as contextual data, domain knowledge, and tagging information among others for improving the quality of recommendations. Traditional approaches have not been effective in exploiting these additional data sources. Hence, new techniques need to be developed for extracting and integrating them into the recommendation process. In this article, the authors present a survey on state of the art recommendation approaches their algorithms, issues and also provides further research directions for developing smart and intelligent recommender systems.

scholarly journals Simple Shading Correction Method for Brightfield Whole Slide Imaging

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3084
Author(s):  
Yoon-Oh Tak ◽  
Anjin Park ◽  
Janghoon Choi ◽  
Jonghyun Eom ◽  
Hyuk-Sang Kwon ◽  
...  
Keyword(s):  
State Of The Art ◽  
Correction Method ◽  
Input Image ◽  
Image Sequences ◽  
Whole Slide Imaging ◽  
Pattern Noise ◽  
Art Methods ◽  
Previous State ◽  
Fixed Pattern Noise

Whole slide imaging (WSI) refers to the process of creating a high-resolution digital image of a whole slide. Since digital images are typically produced by stitching image sequences acquired from different fields of view, the visual quality of the images can be degraded owing to shading distortion, which produces black plaid patterns on the images. A shading correction method for brightfield WSI is presented, which is simple but robust not only against typical image artifacts caused by specks of dust and bubbles, but also against fixed-pattern noise, or spatial variations in pixel values under uniform illumination. The proposed method comprises primarily of two steps. The first step constructs candidates of a shading distortion model from a stack of input image sequences. The second step selects the optimal model from the candidates. The proposed method was compared experimentally with two previous state-of-the-art methods, regularized energy minimization (CIDRE) and background and shading correction (BaSiC) and showed better correction scores, as smooth operations and constraints were not imposed when estimating the shading distortion. The correction scores, averaged over 40 image collections, were as follows: proposed method, 0.39 ± 0.099; CIDRE method, 0.67 ± 0.047; BaSiC method, 0.55 ± 0.038. Based on the quantitative evaluations, we can confirm that the proposed method can correct not only shading distortion, but also fixed-pattern noise, compared with the two previous state-of-the-art methods.

scholarly journals Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

2020 ◽  
Vol 10 (21) ◽  
pp. 7748
Author(s):  
Zeshan Fayyaz ◽  
Mahsa Ebrahimian ◽  
Dina Nawara ◽  
Ahmed Ibrahim ◽  
Rasha Kashef
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Information Overload ◽  
State Of The Art ◽  
Recommendation Systems ◽  
Qualitative Evaluation ◽  
Potential Influence ◽  
Systems Research ◽  
Current State

Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.

scholarly journals A Closer-to-Reality Model for Comparing Relevant Dimensions of Recommender Systems, with Application to Novelty

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 500
Author(s):  
François Fouss ◽  
Elora Fernandes
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Evaluation Procedure ◽  
Recommendation Algorithm ◽  
Test Set ◽  
Ranking Algorithms ◽  
Traditional Training ◽  
Recommendation Algorithms ◽  
Recent Developments

Providing fair and convenient comparisons between recommendation algorithms—where algorithms could focus on a traditional dimension (accuracy) and/or less traditional ones (e.g., novelty, diversity, serendipity, etc.)—is a key challenge in the recent developments of recommender systems. This paper focuses on novelty and presents a new, closer-to-reality model for evaluating the quality of a recommendation algorithm by reducing the popularity bias inherent in traditional training/test set evaluation frameworks, which are biased by the dominance of popular items and their inherent features. In the suggested model, each interaction has a probability of being included in the test set that randomly depends on a specific feature related to the focused dimension (novelty in this work). The goal of this paper is to reconcile, in terms of evaluation (and therefore comparison), the accuracy and novelty dimensions of recommendation algorithms, leading to a more realistic comparison of their performance. The results obtained from two well-known datasets show the evolution of the behavior of state-of-the-art ranking algorithms when novelty is progressively, and fairly, given more importance in the evaluation procedure, and could lead to potential changes in the decision processes of organizations involving recommender systems.

A Hybrid Recommender System Using KNN and Clustering

2021 ◽  
Vol 20 (02) ◽  
pp. 553-596
Author(s):  
Hao Fan ◽  
Kaijun Wu ◽  
Hamid Parvin ◽  
Akram Beigi ◽  
Kim-Hung Pho
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Cold Start ◽  
The State ◽  
Text Segment ◽  
Art Methods ◽  
Additional Process ◽  
High Scalability ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Recommender Systems ([Formula: see text]) are known in the E-Commerce ([Formula: see text]) field. They are expected to suggest the accurate goods/musics/films/items to the consumers/clients/people/users. Recent Hybrid [Formula: see text]s ([Formula: see text] have made us able to deal with the most important shortages of traditional Content-based F iltering ([Formula: see text]) and Collaborative Filtering ([Formula: see text]). Cold start, scalability and sparsity are the most important challenges to [Formula: see text] recommender systems ([Formula: see text]). [Formula: see text]s combine [Formula: see text] and [Formula: see text]. While the [Formula: see text]s that are based on memory have high accuracy, they are not scalable. Contrarily, the RSs on the basis of models have low accuracy but high scalability. Thus, aiming at dealing with cold start, scalability and sparsity challenges, [Formula: see text] is proposed to use both methods and also it has been evaluated on a real benchmark. An ontology, which is automatically created by an intelligently collected wordnet, has been employed in [Formula: see text] segment of the proposed [Formula: see text]. It has been automatically created and enhanced by an additional process. The functionality of the recommended framework has been superior to the performance of the state-of-the-art methods and the traditional [Formula: see text] and [Formula: see text] embedded in our method. Using a real dataset as a benchmark, the experimentations indicate that the proposed method not only has better performance but also has more efficacy rather than the state-of-the-art methods.

scholarly journals Adversarial Oracular Seq2seq Learning for Sequential Recommendation

2020 ◽  
Author(s):  
Pengyu Zhao ◽  
Tianxiao Shui ◽  
Yuanxing Zhang ◽  
Kecheng Xiao ◽  
Kaigui Bian
Keyword(s):  
Recommender Systems ◽  
Real World ◽  
State Of The Art ◽  
Large Datasets ◽  
Time Varying ◽  
Learning Problem ◽  
Adversarial Learning ◽  
Real World Applications ◽  
Art Methods ◽  
And Behavior

Recently, sequential recommendation has become a significant demand for many real-world applications, where the recommended items would be displayed to users one after another and the order of the displays influences the satisfaction of users. An extensive number of models have been developed for sequential recommendation by recommending the next items with the highest scores based on the user histories while few efforts have been made on identifying the transition dependency and behavior continuity in the recommended sequences. In this paper, we introduce the Adversarial Oracular Seq2seq learning for sequential Recommendation (AOS4Rec), which formulates the sequential recommendation as a seq2seq learning problem to portray time-varying interactions in the recommendation, and exploits the oracular learning and adversarial learning to enhance the recommendation quality. We examine the performance of AOS4Rec over RNN-based and Transformer-based recommender systems on two large datasets from real-world applications and make comparisons with state-of-the-art methods. Results indicate the accuracy and efficiency of AOS4Rec, and further analysis verifies that AOS4Rec has both robustness and practicability for real-world scenarios.

State of the Art Recommendation Approaches

2018 ◽  
Vol 10 (1) ◽  
pp. 51-76
Author(s):  
Bhupesh Rawat ◽  
Sanjay K. Dwivedi
Keyword(s):  
Recommender Systems ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
New Techniques ◽  
Contextual Data ◽  
Additional Information ◽  
E Learning ◽  
Traditional Approaches

Recommender systems have been used successfully in order to deal with information overload problems in a wide variety of domains ranging from e-commerce, e-tourism, to e-learning. They typically predict the ratings of unseen items by a user and recommend the top N items based on user's profile. Moreover, the profile can be enriched further by using additional information such as contextual data, domain knowledge, and tagging information among others for improving the quality of recommendations. Traditional approaches have not been effective in exploiting these additional data sources. Hence, new techniques need to be developed for extracting and integrating them into the recommendation process. In this article, the authors present a survey on state of the art recommendation approaches their algorithms, issues and also provides further research directions for developing smart and intelligent recommender systems.

Sign in / Sign up

Export Citation Format

Share Document