Smart, hybrid and context-aware POI mobile recommender system in tourism in Oman

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatemehalsadat Afsahhosseini ◽  
Yaseen Al-Mulla
Keyword(s):  
Social Media ◽  
Recommender System ◽  
Value Chain ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Information And Communications Technology ◽  
Social Implications ◽  
Context Aware ◽  
Content Type ◽  
Additional Information

PurposeThe purpose of this study is to identify the knowledge gap and future opportunities for developing mobile recommender system in tourism sector that lead to comfortable, targeted and attractive tourism. A recommender system improves the traditional classification algorithms and has incorporated many advanced machine learning algorithms.Design/methodology/approachDesign of this application followed a smart, hybrid and context-aware recommender system, which includes various recommender systems. With the recommender system's help, useful management for time and budget is obtained for tourists, since they usually have financial and time constraints for selecting the point of interests (POIs) and so more purposeful trip planned with decreased traffic and air pollution.FindingsThe finding of this research showed that the inclusion of additional information about the item, user, circumstances, objects or conditions and the environment could significantly impact recommendation quality and information and communications technology has become one part of the tourism value chain.Practical implicationsThe application consists of (1) registration: with/without social media accounts, (2) user information: country, gender, age and his/her specific interests, (3) context data: available time, alert, price, spend time, weather, location, transportation.Social implicationsThe study’s social implications include connecting the app and registration through social media to a more social relationship, with its textual reviews, or user review as user-generated content for increasing accuracy.Originality/valueThe originality of this research work lies on introducing a new content- and knowledge-based algorithm for POI recommendations. An “Alert” context emphasizing on safety, supplies and essential infrastructure is considered as a novel context for this application.

A New Contextual Influencer User Measure to Improve the Accuracy of Recommender System

2018 ◽  
Vol 9 (4) ◽  
pp. 38-51
Author(s):  
Maryam Jallouli ◽  
Sonia Lajmi ◽  
Ikram Amous
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
User Model ◽  
Context Aware ◽  
Additional Information ◽  
Context Element ◽  
Cold Start Problem

In the last decade, social-based recommender systems have become the best way to resolve a user's cold start problem. In fact, it enriches the user's model by adding additional information provided from his social network. Most of those approaches are based on a collaborative filtering and compute similarities between the users. The authors' preliminary objective in this work is to propose an innovative context aware metric between users (called contextual influencer user). These new similarities are called C-COS, C-PCC and C-MSD, where C refers to the category. The contextual influencer user model is integrated into a social based recommendation system. The category of the items is considered as the most pertinent context element. The authors' proposal is implemented and tested within the food dataset. The experimentation proved that the contextual influencer user measure achieves 0.873, 0.874, and 0.882 in terms of Mean Absolute Error (MAE) corresponding to C-cos, C-pcc and C-msd, respectively. The experimental results showed that their model outperforms several existing methods.

Managing the risks of social media

2014 ◽  
Vol 22 (5) ◽  
pp. 39-41 ◽  
Author(s):  
John Chelliah ◽  
James Field
Keyword(s):  
Social Media ◽  
Design Methodology ◽  
Social Implications ◽  
Content Type ◽  
Personal Use ◽  
Use Of Social Media ◽  
Practical Implications

Purpose – The purpose of this paper is to highlight the risks employers face when employees use social media. Design/methodology/approach – This paper considers the types of risks and suggests how they could be mitigated. Findings – It is revealed that employers need two policies to manage risks associated with the use of social media: one covering business use of social media and another covering employees’ personal use of social media. Practical implications – This paper guides managers in assessing the exposure of their organizations and clients to the risks identified. Social implications – This paper draws attention to the risks associated with the widespread use of social media for both business and employees’ personal purposes. Originality/value – The issue of organizational awareness and preparedness to tackle the challenges posed by social media has been raised.

Leveraging social media for knowledge management healthcare capability

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ellen Belitzky ◽  
Christian Bach ◽  
Erika Belitzky
Keyword(s):  
Social Support ◽  
Social Media ◽  
Knowledge Management ◽  
Pediatric Oncology ◽  
Healthcare Sector ◽  
Social Implications ◽  
Oncology Patients ◽  
Content Type ◽  
For Profit ◽  
Not For Profit

Purpose This study aims to understand how healthcare social media offer nonmedical psycho-social support for pediatric oncology patients and their care community and how social media can be exploited for healthcare knowledge management. Design/methodology/approach Social media capabilities were identified and categorized based on psycho-social support services for pediatric oncology patients, caregivers and their community of care. Data were collected from 187 service sites representing more than 100 organizations. These broadly defined capabilities in trusted care organizations were analyzed to understand use of social media in providing psycho-social support. Findings Analysis revealed resource guides, stories and in-person support at clinics as the most prevalent forms of technology-guided psycho-social support. Privacy, security and information integrity rose as technical challenges for interactive social media platforms. Medical community trust is inconsistent, leading to immature adoption of critical psycho-social support as a knowledge management source. Findings further indicate the not-for-profit support sector provides robust social media capabilities compared to the healthcare sector. Research limitations/implications Future research may extend to maturing healthcare and not-for-profit sector services and to private sector products such as mobile applications and other technologies. Practical implications Survivor and caregiver quality of life depend on psycho-social support communities and services delivered via social media. Social implications Child protection social implications require significant attention due to sensitivity of security, privacy concerns and longevity of digital footprints for pediatric patients. Originality/value Research demonstrates opportunity for medical provider, healthcare organization, not-for-profit sector, patient and caregiver cooperation using social media. Data indicate healthcare technology systems leveraging social media can extend knowledge management capability beyond organization boundaries.

Wearable IoT intelligent recommender framework for a smarter healthcare approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mona Bokharaei Nia ◽  
Mohammadali Afshar Kazemi ◽  
Changiz Valmohammadi ◽  
Ghanbar Abbaspour
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Iot Devices ◽  
The Right ◽  
Media Sentiment

PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

Transformer network-based word embeddings approach for autonomous cyberbullying detection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Subbaraju Pericherla ◽  
E. Ilavarasan
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Convolutional Neural Networks ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Word Embeddings ◽  
Content Type ◽  
Light Gradient ◽  
Cyberbullying Detection

PurposeNowadays people are connected by social media like Facebook, Instagram, Twitter, YouTube and much more. Bullies take advantage of these social networks to share their comments. Cyberbullying is one typical kind of harassment by making aggressive comments, abuses to hurt the netizens. Social media is one of the areas where bullying happens extensively. Hence, it is necessary to develop an efficient and autonomous cyberbullying detection technique.Design/methodology/approachIn this paper, the authors proposed a transformer network-based word embeddings approach for cyberbullying detection. RoBERTa is used to generate word embeddings and Light Gradient Boosting Machine is used as a classifier.FindingsThe proposed approach outperforms machine learning algorithms such as logistic regression, support vector machine and deep learning models such as word-level convolutional neural networks (word CNN) and character convolutional neural networks with short cuts (char CNNS) in terms of precision, recall, F1-score.Originality/valueOne of the limitations of traditional word embeddings methods is context-independent. In this work, only text data are utilized to identify cyberbullying. This work can be extended to predict cyberbullying activities in multimedia environment like image, audio and video.

Benchmarking project management dimensions at the lapse of a century

2017 ◽  
Vol 24 (6) ◽  
pp. 1675-1689 ◽  
Author(s):  
Maqsood Sandhu ◽  
Asadullah Khan
Keyword(s):  
Risk Management ◽  
Project Management ◽  
Data Collection ◽  
Research Work ◽  
Panama Canal ◽  
Social Implications ◽  
Government Control ◽  
Economic Implications ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper is twofold: first, to investigate project management dimensions while constructing the Panama Canal from the end of ninetieth century to the start of twentieth century and then benchmarking against the Palm Diera Island at the lapse of a century. Second, to highlight issues of project management, specially the risk management with its economic, social and political domains at the construction site and in France and America. Design/methodology/approach The case study research method of qualitative research has been adopted when comparing two mega projects executed in different time and space. For the Panama Canal project documentation investigation was performed. However, a semi-structured interview data collection method was adopted for the Palm Diera Island project. A comparative study of two projects helps in deeper understanding of cross-project management dimensions. Findings The research reveals that the French team failed to complete the Panama Canal construction project due to inadequate planning, inappropriate design, lack of risk management, health and safety of the staff and non-availability of finances. However, the Americans successfully completed construction of the canal within budget and time and this was due to the support of change in the purpose of the canal construction adding to achieve its commercial objectives and at the same time strengthen its naval presence. American took its construction as a national objective than the individual enterprise as executed by the French team. Research limitations/implications Data collection for the Panama Canal was limited to only historical data available from the literature as documentary investigation. The researchers visited the canal to get in-depth understanding of the construction practices and the scale of construction. However, for the Palm Diera project, data collection was limited to three key personnel interviews. Practical implications The Americans were successful in completing the canal due to the US Government control on management and finances of the canal construction and lessons learned during the French construction period. The paper serves as a benchmark for project management dimension in two different regions in different times. The paper bears economic implications for the construction of the mega projects both in South America and the Middle East. Cost overrun construction of the Panama Canal during the French period influenced political spectrum in France resulting into the defeat of the government. During the American period of construction first time out of country visit by the sitting president of the USA reflects its economic and social importance. The valley of death was converted into the valley comfort during the American period resulting into social welfare of the workers. Completion of the canal by the Americans helped them secure operations of the Panama Canal for the next 100 years, contributing to its economic and naval strength. Social implications The paper reveals that safety and social implications for the work place in two different regions and at two different times. The impact of safe and improved working conditions at Palm Diera Island resulted into no injury or loss of life, however, during the Panama Canal construction more than 30,000 workers died affecting not only families of the respective workers but their nations as well. The impacts of both the projects on the society were also significant. The public opinion against the construction of the canal during the French period of construction was so significant that they had to abandon their construction equipment at the site. On the contrary, completion of construction of the Panama Canal during the French period helped secured political mileage for President Roosevelt and his party. Originality/value The paper benchmarks two different mega projects with different scope executed in two different regions at the lapse of a century. No such research work was found to have compared project management dimensions of two mega projects at the lapse of a century and in two different regions.

Roman to Gurmukhi Social Media Text Normalization

2020 ◽  
Vol 13 (4) ◽  
pp. 407-435
Author(s):  
Jagroop Kaur ◽  
Jaswinder Singh
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Research Work ◽  
Target Language ◽  
Translation System ◽  
Second Phase ◽  
Content Type ◽  
Social Media Text ◽  
Practical Implications ◽  
Text Normalization

PurposeNormalization is an important step in all the natural language processing applications that are handling social media text. The text from social media poses a different kind of problems that are not present in regular text. Recently, a considerable amount of work has been done in this direction, but mostly in the English language. People who do not speak English code mixed the text with their native language and posted text on social media using the Roman script. This kind of text further aggravates the problem of normalizing. This paper aims to discuss the concept of normalization with respect to code-mixed social media text, and a model has been proposed to normalize such text.Design/methodology/approachThe system is divided into two phases – candidate generation and most probable sentence selection. Candidate generation task is treated as machine translation task where the Roman text is treated as source language and Gurmukhi text is treated as the target language. Character-based translation system has been proposed to generate candidate tokens. Once candidates are generated, the second phase uses the beam search method for selecting the most probable sentence based on hidden Markov model.FindingsCharacter error rate (CER) and bilingual evaluation understudy (BLEU) score are reported. The proposed system has been compared with Akhar software and RB\_R2G system, which are also capable of transliterating Roman text to Gurmukhi. The performance of the system outperforms Akhar software. The CER and BLEU scores are 0.268121 and 0.6807939, respectively, for ill-formed text.Research limitations/implicationsIt was observed that the system produces dialectical variations of a word or the word with minor errors like diacritic missing. Spell checker can improve the output of the system by correcting these minor errors. Extensive experimentation is needed for optimizing language identifier, which will further help in improving the output. The language model also seeks further exploration. Inclusion of wider context, particularly from social media text, is an important area that deserves further investigation.Practical implicationsThe practical implications of this study are: (1) development of parallel dataset containing Roman and Gurmukhi text; (2) development of dataset annotated with language tag; (3) development of the normalizing system, which is first of its kind and proposes translation based solution for normalizing noisy social media text from Roman to Gurmukhi. It can be extended for any pair of scripts. (4) The proposed system can be used for better analysis of social media text. Theoretically, our study helps in better understanding of text normalization in social media context and opens the doors for further research in multilingual social media text normalization.Originality/valueExisting research work focus on normalizing monolingual text. This study contributes towards the development of a normalization system for multilingual text.

scholarly journals Aggregating social media data with temporal and environmental context for recommendation in a mobile tour guide system

2016 ◽  
Vol 7 (3) ◽  
pp. 281-299 ◽  
Author(s):  
Kevin Meehan ◽  
Tom Lunney ◽  
Kevin Curran ◽  
Aiden McCaughey
Keyword(s):  
Social Media ◽  
Recommender System ◽  
Real World ◽  
Environmental Context ◽  
User Preferences ◽  
Temporal Context ◽  
Tour Guide ◽  
Social Media Data ◽  
Content Type ◽  
Media Data

Purpose Manufacturers of smartphone devices are increasingly utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. One area that has been significantly enhanced by the increased use of context in mobile applications is tourism. Traditionally, tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. This research proposes an intelligent context-aware recommender system that aims to minimise the highlighted problems. Design/methodology/approach Intelligent reasoning was performed to determine the weight or importance of different types of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist attractions. Temporal context can be the time of day or season; this is particularly important in tourism as it is largely a seasonal activity. Social context such as social media can potentially provide an indication of the “mood” of an attraction. These types of contexts are combined with location data and the context of the user to provide a more effective recommendation to tourists. The evaluation of the system is a user study that utilised both qualitative and quantitative methods, involving 40 participants of differing gender, age group, number of children and marital status. Findings This study revealed that the participants selected the context-based recommendation at a significantly higher level than either location-based recommendation or random recommendation. It was clear from analysing the questionnaire results that location is not the only influencing factor when deciding on a tourist attraction to visit. Research limitations/implications To effectively determine the success of the recommender system, various combinations of contextual conditions were simulated. Simulating contexts provided the ability to randomly assign different contextual conditions to ensure an effective recommendation under all circumstances. This is not a reflection of the “real world”, because in a “real world” field study the majority of the contextual conditions will be similar. For example, if a tourist visited numerous attractions in one day, then it is likely that the weather conditions would be the same for the majority of the day, especially in the summer season. Practical implications Utilising this type of recommender system would allow the tourists to “go their own way” rather than following a prescribed route. By using this system, tourists can co-create their own experience using both social media and mobile technology. This increases the need to retain user preferences and have it available for multiple destinations. The application will be able to learn further through multiple trips, and as a result, the personalisation aspect will be incrementally refined over time. This extensible aspect is increasingly important as personalisation is gradually more effective as more data is collated. Originality/value This paper contributes to the body of knowledge that currently exists regarding the study of utilising contextual conditions in mobile recommender systems. The novelty of the system proposed by this research is the combination of various types of temporal, environmental and personal context data to inform a recommendation in an extensible tourism application. Also, performing sentiment analysis on social media data has not previously been integrated into a tourist recommender system. The evaluation concludes that this research provides clear evidence for the benefits of combining social media data with environmental and temporal context to provide an effective recommendation.

South-east Asia's digital divide is widening

2018 ◽  
Keyword(s):  
Digital Divide ◽  
Value Chain ◽  
Smart Cities ◽  
Information And Communications Technology ◽  
Foreign Assistance ◽  
Social Progress ◽  
Communications Technology ◽  
Economic Prosperity ◽  
Regional Economic ◽  
Content Type

Subject Digital access. Significance Advances in information and communications technology (ICT) are critical to South-east Asia’s future economic prosperity and social progress, and to boosting the region’s economies up the value chain. Impacts Singapore will use the ‘smart cities’ initiative to attract foreign assistance to ease the regional digital divide. Poorer countries will struggle the most in increasing digital access, due to lack of infrastructure and policy misalignment. This will, in turn, increase inter-regional economic inequality.

A comparative study of the effectiveness of sentiment tools and human coding in sarcasm detection

2018 ◽  
Vol 20 (3) ◽  
pp. 358-374
Author(s):  
Phoey Lee Teh ◽  
Pei Boon Ooi ◽  
Nee Nee Chan ◽  
Yee Kang Chuah
Keyword(s):  
Social Media ◽  
Comparative Study ◽  
Design Methodology ◽  
Social Implications ◽  
Product Reviews ◽  
Verbal Irony ◽  
Content Type ◽  
Media Politics ◽  
Human Coder ◽  
Practical Implications

Purpose Sarcasm is often used in everyday speech and writing and is prevalent in online contexts. The purpose of this paper is to investigate the analogy between sarcasm comments from sentiment tools and the human coder. Design/methodology/approach Using the Verbal Irony Procedure, eight human coders were engaged to analyse comments collected from an online commercial page, and a dissimilarity analysis was conducted with sentiment tools. Three constants were tested, namely, polarity from sentiment tools, polarity rating by human coders; and sarcasm-level ratings by human coders. Findings Results found an inconsistent ratio between these three constants. Sentiment tools used did not have the capability or reliability to detect the subtle, contextualized meanings of sarcasm statements that human coders could detect. Further research is required to refine the sentiment tools to enhance their sensitivity and capability. Practical implications With these findings, it is recommended that further research and commercialization efforts be directed at improving current sentiment tools – for example, to incorporate sophisticated human sarcasm texts in their analytical systems. Sarcasm exists frequently in media, politics and human forms of communications in society. Therefore, more highly sophisticated sentiment tools with the abilities to detect human sarcasm would be vital in research and industry. Social implications The findings suggest that presently, of the sentiment tools investigated, most are still unable to pick up subtle contexts within the text which can reverse or change the message that the writer intends to send to his/her receiver. Hence, the use of the relevant hashtags (e.g. #sarcasm; #irony) are of fundamental importance in detection tools. This would aid the evaluation of product reviews online for commercial usage. Originality/value The value of this study lies in its original, empirical findings on the inconsistencies between sentiment tools and human coders in sarcasm detection. The current study proves these inconsistencies are detected between human and sentiment tools in social media texts and points to the inadequacies of current sentiment tools. With these findings, it is recommended that further research and commercialization efforts be directed at improving current sentiment tools – to incorporate sophisticated human sarcasm texts in their analytical systems. The system can then be used as a reference for psychologists, media analysts, researchers and speech writers to detect cues in the inconsistencies in behaviour and language.

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