When a tourist wants to visit a site, he/she first asks about the category of the site. The availability of detailed information about the site allows him/her to tailor his/her visit to his/her preferences. This infor...
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ISBN:
(纸本)9798350309188
When a tourist wants to visit a site, he/she first asks about the category of the site. The availability of detailed information about the site allows him/her to tailor his/her visit to his/her preferences. This information is therefore essential for Point of Interest (POI) recommendation. However, it is rarely or never available for lesser-known POIs. Lesser-known POIs can be considered as places or events that local people know well, and that may be important to them, but that others do not know about. We propose an approach to estimate the categories of lesser-known POIs based on information from social media. The originality of this approach lies in the extraction of information and links between them, the encoding of the POIs, the representation of the data, and the combination of machine learning techniques such as Few Shot learning, LightGCN, and Clustering for the estimation of POI categories. The results of the experiments would allow us to confirm that our approach can estimate POI categories and thus discover information about POIs that may be relevant. This approach would be useful for our future work on POI recommendations.
With the continuing advancement of ubiquitous computing and various sensor technologies, we are observing a massive population of multimodal sensors at the edge which posts significant challenges in fusing the data. I...
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E-learning has emerged as the most effective way of getting information in a range of sectors in the contemporary age. The utilisation of electronic content to provide education and development is referred to as e-lea...
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Social recommender systems based on graph neural networks can successfully model the social influence among users and effectively mitigate the issue of data sparsity. However, existing methods mostly focus on individu...
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ISBN:
(纸本)9798350309188
Social recommender systems based on graph neural networks can successfully model the social influence among users and effectively mitigate the issue of data sparsity. However, existing methods mostly focus on individual influence while ignoring group influence. The social influence exerted by different groups of users can vary, and the intensity can also vary. A user is often more susceptible to the influence of groups with preferences similar to his/her own. This paper proposes a social recommendation model called Multi-channel Graph neural network with Contrastive learning (MGCL). MGCL classifies users into different groups according to their preferences, establishes separate social propagation channels for each group, and extracts the influence of different user groups to better represent a user. In addition, contrastive learning is utilized to improve the modeling of user preferences and social influence during model training. The superiority of the proposed MGCL over state-of-the-art baselines has been demonstrated through extensive experiments conducted on three benchmark datasets.
Artificial Intelligence (AI) has the potential to revolutionize various aspects of education due to its rapid expansion in educational technology by introducing personalized and efficient learning experiences. Several...
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ISBN:
(纸本)9798350328202
Artificial Intelligence (AI) has the potential to revolutionize various aspects of education due to its rapid expansion in educational technology by introducing personalized and efficient learning experiences. Several artificial intelligence (AI) tools are being integrated into this research project such as 'Coursera Coach powered by Generative AI' which incorporates adaptive learning, 'AI- assisted course building powered by Generative AI' which incorporates intelligent tutoring elements and 'Turnitin' offers tools for grading and providing feedback to revolutionize teaching, learning, and assessment practices in higher education. Researchers evaluate the effectiveness of AI tools in enhancing student engagement, personalizing learning experiences, and optimizing assessment processes based on a variety of quantitative metrics. A comparison is made between experimental groups that follow AI-integrated teaching methods and control groups that follow a traditional approach to instruction. As part of the study, scores of pre- and post-assessment questions were collected, learning progression patterns were analyzed, and instructors and students provided qualitative feedback. As a result of the experimental data, it has been possible to discern which AI tools have strengths and limitations within the context of higher education. A variety of adaptive learning platforms are evaluated for their ability to adjust content to individual learning styles, while intelligent tutoring systems are assessed for their impact on comprehension, retention, and problem-solving skills. automated grading tools are tested for their efficiency gains in assessment processes and their ability to improve feedback promptness and quality. A substantial amount of empirical evidence is provided by the findings of this study to the ongoing discourse on AI integration in higher education. This study provides a wealth of actionable insights for academic staff, technicians, and administrators seeking e
Nowadays, many platforms on the Web offer organized events, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem ...
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ISBN:
(纸本)9798350309188
Nowadays, many platforms on the Web offer organized events, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem tends to borrow recommendation techniques. However, compared to e-commerce items and purchases, events and participation are usually of a much smaller frequency, and the data may be insufficient to learn an accurate model. In this paper, we propose to utilize social media retweeting activity data to enhance the learning of event participant prediction models. We create a joint knowledge graph to bridge the social media and the target domain, assuming that event descriptions and tweets are written in the same language. Furthermore, we propose a learning model that utilizes retweeting information for the target domain prediction more effectively. We conduct comprehensive experiments in two scenarios with real-world data. In each scenario, we set up training data of different sizes, as well as warm and cold test cases. The evaluation results show that our approach consistently outperforms several baseline models, especially with the warm test cases, and when target domain data is limited.
One of the difficult task for many users on SQL is to write the SQL Query due to its syntax and structure. If a person needs to query a database, they should know everything about how data is distributed and what the ...
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ISBN:
(纸本)9798331540661;9798331540678
One of the difficult task for many users on SQL is to write the SQL Query due to its syntax and structure. If a person needs to query a database, they should know everything about how data is distributed and what the internal dependencies are. For this reason, it is not easy for everyone to access data in database without proper knowledge. This paper presents a novel application that leverages generative AI and natural language processing (NLP) to enable users to interact with databases using natural language queries. Built on the Gemini API, the application translates user queries into SQL queries, simplifying database interactions for non-technical users. The Python-based backend, SQLite database management, and Streamlit frontend provide a comprehensive solution for database querying and analysis. This approach democratizes data retrieval and analysis, offering automated insights and visualizations to users of all skill levels. The app also features automateddata analysis, which boosts insight generation for users of all skill levels. Further, the traditional ways of querying SQL generally require specialized knowledge and are only accessible to those with technical backgrounds. When the application takes charge of the query construction process and offers data as UI elements, it can invigorate users to have a higher degree of insight into what their data actually is and explore it even more efficiently. The Python software stack combines Python for backend processing, SQLite for database management and a web-based frontend for user interaction to provide an all-encompassing database querying and analysis solution.
data augmentation plays a vital role in tackling model overfitting in deep learning. Traditional methods of data augmentation typically utilize basic image manipulation or deep learning techniques, which face challeng...
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The advent of ubiquitous computing has resulted in the widespread use of Internet of Things (IoT) devices, which are expected to control every aspect of our everyday live with a prevalence in the billions. These devic...
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With the increasing demand for edge computing in cyber-physical system (CPS) applications, ensuring the safety and reliability of machine learning models running on edge devices during online model training and infere...
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