scholarly journals Exploring the effects of Clustering Algorithms on Free Text Recommendation

2018 ◽  
Author(s):  
Israel Mendonca dos Santos ◽  
Antoine Trouve ◽  
Akira Fukuda ◽  
Kazuaki Murakami
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Negative Impact ◽  
Clustering Algorithms ◽  
Search Space ◽  
Free Text ◽  
Word Embeddings ◽  
Performance Measurements ◽  
Number Of Clusters

In this paper, we provide a study on the effects of applying classical clustering algorithms, such as k-Means to free text recommender systems. A typical recommender system may face problems when the number of items from a database goes from a few items to hundreds of items. Currently, one of the most prominent techniques to scale the database is applying clustering, however clustering may have a negative impact on the accuracy of the system when applied without taking into consideration the underlying items. In this work, we build a conceptual text recommender system and use k-Means to partition its search space into different groups. We study how the variation of the number of clusters affects its performance in the light of two performance measurements: recommendation time and precision. We also analyze if this clustering is affected by the representation of text we use. All the techniques used in this study uses word-embeddings to represent the document. One of the main findings of this work is that using clustering we can improve the recommendation time in up to almost 30 times without affecting much off its initial accuracy. Another interesting finding is that the increment of the number of clusters is not directly translated into linear performance.

Recommender Systems Using Collaborative Tagging

2020 ◽  
Vol 16 (3) ◽  
pp. 183-200
Author(s):  
Latha Banda ◽  
Karan Singh ◽  
Le Hoang Son ◽  
Mohamed Abdel-Basset ◽  
Pham Huy Thong ◽  
...  
Keyword(s):  
Social Networking ◽  
Recommender Systems ◽  
Recommender System ◽  
Prediction Accuracy ◽  
Clustering Algorithms ◽  
Incremental Clustering ◽  
Collaborative Tagging ◽  
Accuracy Ratio ◽  
Hybrid Recommender ◽  
Gradual Decay

Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.

scholarly journals Critique Generation to Increase Diversity in Conversational Recipe Recommender System

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Fakhri G Abbas ◽  
Nadia Najjar ◽  
David Wilson
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Search Space ◽  
Initial Evaluation ◽  
Exploration Process ◽  
Food Items ◽  
Novel Approach ◽  
Nutritional Health ◽  
Conversational Interactions

Conversational recommender systems help to guide users in exploring the search space in order to discover items of interest. During the exploration process, the user provides feedback on recommended items to refine subsequent recommendations. Critiquing as a way of feedback has proven effective for conversational interactions. In addition, diversifying the recommended items during exploration can help to increase user understanding of the search space, which critiquing alone will not achieve. Both aspects are important elements for recommender applications in the food domain. Diversity in diet has been shown to predict nutritional health, and conversational exploration can help to introduce new food items. In this paper, we introduce a novel approach that brings together critique and diversity to support conversational recommendation in the recipe domain. Initial evaluation in comparison to a baseline similarity-based recommender shows that the proposed approach increases diversity during the exploration process.

scholarly journals 764 Human Factors in Operative Theatres During the Covid-19 Pandemic: A Cross-sectional Analysis of Operating Theatre Staff Experiences in a Regional Centre

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
L E Murchison ◽  
R Anbarasan ◽  
A Mathur ◽  
M Kulkarni
Keyword(s):  
Human Factors ◽  
Online Survey ◽  
Situational Awareness ◽  
Negative Impact ◽  
High Stress ◽  
Operating Theatre ◽  
Free Text ◽  
Cross Sectional ◽  
Chi Squared ◽  
Theatre Staff

Abstract Introduction In the already high-risk, high-stress environment of the operating theatre, operating during Covid-19 has brought its own unique challenges. Communication, teamwork and anxiety related new operating practices secondary to Covid-19 are hypothesised to have a negative impact on patient care. Method We conducted a single-centre online survey of operating theatre staff from 22nd June–6th July 2020. Respondents completed 18 human factors questions related to COVID-19 precautions including communication, teamwork, situational awareness, decision making, stress, fatigue, work environment and organisational culture. Questions consisted of yes/no responses, multiple choice and Likert items. Kruskall-Wallis tests, Chi-Squared, Mann Whitney U tests, Spearman’s correlation coefficient, lambda and Cramer’s V tests were used. Free-text responses were also reviewed. Results 116 theatre staff responded. Visual (90.5%), hearing/ understanding (96.6%) difficulties, feeling faint/lightheaded (66.4%) and stress (47.8%) were reported. Decreased situational awareness was reported by 71.5% and correlated with visors (r = 0.27 and p = 0.03) and FFP2/3 mask usage (r = 0.29 and p = 0.01). Reduced efficiency of theatre teams was reported by 75% of respondents and 21.5% felt patient safety was at greater risk due to Covid-19 precautions in theatre. Conclusions Organisational adjustments are required, and research focused on development of fit-for-purpose personal protective equipment (PPE).

scholarly journals Mental Health, Information and Being Connected: Qualitative Experiences of Social Media Use during the COVID-19 Pandemic from a Trans-National Sample

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 735
Author(s):  
Schoultz Mariyana ◽  
Leung Janni ◽  
Bonsaksen Tore ◽  
Ruffolo Mary ◽  
Thygesen Hilde ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Health Information ◽  
Online Survey ◽  
Negative Impact ◽  
National Study ◽  
Free Text ◽  
Cross Sectional ◽  
Mental Health Information ◽  
The Uk

Background: Due to the COVID-19 pandemic and the strict national policies regarding social distancing behavior in Europe, America and Australia, people became reliant on social media as a means for gathering information and as a tool for staying connected to family, friends and work. This is the first trans-national study exploring the qualitative experiences and challenges of using social media while in lockdown or shelter-in-place during the current pandemic. Methods: This study was part of a wider cross-sectional online survey conducted in Norway, the UK, USA and Australia during April/May 2020. The manuscript reports on the qualitative free-text component of the study asking about the challenges of social media users during the COVID-19 pandemic in the UK, USA and Australia. A total of 1991 responses were included in the analysis. Thematic analysis was conducted independently by two researchers. Results: Three overarching themes identified were: Emotional/Mental Health, Information and Being Connected. Participants experienced that using social media during the pandemic amplified anxiety, depression, fear, panic, anger, frustration and loneliness. They felt that there was information overload and social media was full of misleading or polarized opinions which were difficult to switch off. Nonetheless, participants also thought that there was an urge for connection and learning, which was positive and stressful at the same time. Conclusion: Using social media while in a shelter-in-place or lockdown could have a negative impact on the emotional and mental health of some of the population. To support policy and practice in strengthening mental health care in the community, social media could be used to deliver practical advice on coping and stress management. Communication with the public should be strengthened by unambiguous and clear messages and clear communication pathways. We should be looking at alternative ways of staying connected.

scholarly journals A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5248
Author(s):  
Aleksandra Pawlicka ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Ryszard S. Choraś
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
The State ◽  
Advantages And Disadvantages ◽  
Comprehensive Survey ◽  
Security Concerns ◽  
Valuable Role

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

scholarly journals A novel bidirectional clustering algorithm based on local density

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Baicheng Lyu ◽  
Wenhua Wu ◽  
Zhiqiang Hu
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Clustering Algorithms ◽  
Cluster Number ◽  
Denoising Method ◽  
Number Of Clusters ◽  
Data Points ◽  
Cutoff Distance ◽  
Large Clusters ◽  
Small Clusters

AbstractWith the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images.

scholarly journals Natural language processing methods for knowledge management—Applying document clustering for fast search and grouping of engineering documents

2021 ◽  
pp. 1063293X2098297
Author(s):  
Ivar Örn Arnarsson ◽  
Otto Frost ◽  
Emil Gustavsson ◽  
Mats Jirstrand ◽  
Johan Malmqvist
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Unstructured Data ◽  
Free Text ◽  
Engineering Change ◽  
Engineering Documents

Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested.

scholarly journals The Perceived Impact of The First UK Covid-19 Lockdown on Companion Animal Welfare and Behaviour: A Mixed-Method Study of Associations with Owner Mental Health

2021 ◽  
Vol 18 (11) ◽  
pp. 6171
Author(s):  
Emily Shoesmith ◽  
Luciana Santos de Assis ◽  
Lion Shahab ◽  
Elena Ratschen ◽  
Paul Toner ◽  
...  
Keyword(s):  
Mental Health ◽  
Animal Welfare ◽  
Online Survey ◽  
Negative Impact ◽  
Companion Animals ◽  
Principal Component ◽  
Companion Animal ◽  
Free Text ◽  
Cross Sectional ◽  
Human Animal

Background: Companion animals may be a positive presence for their owners during the Covid-19 pandemic. However, the welfare of a companion animal is strongly influenced by the behaviour of their owners, as well as their physical and social environment. We aimed to investigate the reported changes in companion animal welfare and behaviour and to examine the association between these changes and companion animal owners’ mental health. Methods: A cross-sectional online survey of UK residents over 18 years of age was conducted between April and June 2020 (n = 5926). The questionnaire included validated, bespoke items measuring outcomes related to mental health, human-animal bonds and reported changes in animal welfare and behaviour. The final item of the survey invited open-ended free-text responses, allowing participants to describe experiences associated with human-animal relationships during the first UK lockdown phase. Results: Animal owners made up 89.8% of the sample (n = 5323), of whom 67.3% reported changes in their animal’s welfare and behaviour during the first lockdown phase (n = 3583). These reported changes were reduced to a positive (0–7) and negative (0–5) welfare scale, following principal component analysis (PCA) of 17 items. Participants reported more positive changes for cats, whereas more negative changes were reported for dogs. Thematic analysis identified three main themes relating to the positive and negative impact on companion animals of the Covid-19 pandemic. Generalised linear models indicated that companion animal owners with poorer mental health scores pre-lockdown reported fewer negative changes in animal welfare and behaviour. However, companion animal owners with poorer mental health scores since lockdown reported more changes, both positive and negative, in animal welfare and behaviour. Conclusion: Our findings extend previous insights into perceived welfare and behaviour changes on a very limited range of species to a wider a range of companion animals. Owner mental health status has a clear, albeit small, effect on companion animal welfare and behaviour.

Legal document recommendation system: A cluster based pairwise similarity computation

2021 ◽  
pp. 1-13
Author(s):  
Jenish Dhanani ◽  
Rupa Mehta ◽  
Dipti Rana
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Real Life ◽  
Citation Network ◽  
Search Space ◽  
Pairwise Similarity ◽  
Large Numbers ◽  
Legal Document ◽  
Legal Domain ◽  
Similarity Scores

Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O (n2). However, there is a limited number of pairs consisting of strong relevance among the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

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