H.3.13. Intelligent Web Services and Semantic Web
Atefeh Niroomand; Seyyed Hamid Ghafouri; Amid Khatibi Bardsiri
Abstract
This study addresses the challenges of managing dynamic and heterogeneous Internet of Things (IoT) data by proposing a time-aware recommender system that integrates a dynamic semantic ontology with clustering techniques and a hybrid collaborative filtering framework. The proposed model continuously updates ...
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This study addresses the challenges of managing dynamic and heterogeneous Internet of Things (IoT) data by proposing a time-aware recommender system that integrates a dynamic semantic ontology with clustering techniques and a hybrid collaborative filtering framework. The proposed model continuously updates the ontology based on user interactions and incorporates temporal information into both knowledge representation and clustering processes, enabling adaptive and real-time modeling of evolving user behaviors.The dataset consists of approximately 500 users and 15,000 time-stamped interaction records collected over four months, including demographic attributes (age and gender), IoT device usage patterns, and temporal features such as timestamp and time of day.The recommendation framework combines ontology-enhanced user-based collaborative filtering with dynamic K-means clustering, leveraging both semantic relationships and behavioral similarities to improve recommendation quality. Experimental evaluation is conducted using Precision, Recall, F1-score, Accuracy, MAE, and RMSE metrics. The model achieves improvements ranging from approximately 2% to 52%, with respect to state-of-the-art non-temporal methods and traditional collaborative filtering techniques, respectively.Furthermore, computational complexity analysis indicates that the additional processing cost introduced by dynamic ontology updates and temporal modeling remains manageable, preserving the practical applicability of the proposed framework in resource-constrained IoT environments.
H.3. Artificial Intelligence
Mahdi Ahmadlou; Abolghasem Daeichian; Ali Reihanian
Abstract
The expansion of e-commerce has changed customer purchasing habits, moving them from brick-and-mortar stores to online venues. In this shift, some fundamental customer behaviors had to change because online shoppers cannot physically feel the products and rely heavily on customer reviews for evaluations. ...
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The expansion of e-commerce has changed customer purchasing habits, moving them from brick-and-mortar stores to online venues. In this shift, some fundamental customer behaviors had to change because online shoppers cannot physically feel the products and rely heavily on customer reviews for evaluations. However, the lack of structured textual data poses challenges in sifting through numerous, diverse, and sometimes contradictory comments to make an informed purchasing decision. This study proposes a multi-step system for autonomously and intelligently analyzing customer comments to organize comment sections, utilizing general-purpose large language models. First, the proposed system automatically separates comments that discuss online shop services from those specifically related to products by tagging them. Then, it extracts the various product aspects discussed across all comments. Finally, these comments are categorized based on the extracted aspects. Additionally, a new labeled non-English dataset has been created as a benchmark dataset featuring tagged online-shop-related comments. The experimental results showed that the best performing model was Qwen 2.5, achieving an accuracy of 91.7 %.
Z. Nazari; H.R. Koohi; J. Mousavi
Abstract
Nowadays, with the expansion of the internet and its associated technologies, recommender systems have become increasingly common. In this work, the main purpose is to apply new deep learning-based clustering methods to overcome the data sparsity problem and increment the efficiency of recommender systems ...
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Nowadays, with the expansion of the internet and its associated technologies, recommender systems have become increasingly common. In this work, the main purpose is to apply new deep learning-based clustering methods to overcome the data sparsity problem and increment the efficiency of recommender systems based on precision, accuracy, F-measure, and recall. Within the suggested model of this research, the hidden biases and input weights values of the extreme learning machine algorithm are produced by the Restricted Boltzmann Machine and then clustering is performed. Also, this study employs the ELM for two approaches, clustering of training data and determine the clusters of test data. The results of the proposed method evaluated in two prediction methods by employing average and Pearson Correlation Coefficient in the MovieLens dataset. Considering the outcomes, it can be clearly said that the suggested method can overcome the problem of data sparsity and achieve higher performance in recommender systems. The results of evaluation of the proposed approach indicate a higher rate of all evaluation metrics while using the average method results in rates of precision, accuracy, recall, and F-Measure come to 80.49, 83.20, 67.84 and 73.62 respectively.
J. Hamidzadeh; M. Moradi
Abstract
Recommender systems extract unseen information for predicting the next preferences. Most of these systems use additional information such as demographic data and previous users' ratings to predict users' preferences but rarely have used sequential information. In streaming recommender systems, the emergence ...
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Recommender systems extract unseen information for predicting the next preferences. Most of these systems use additional information such as demographic data and previous users' ratings to predict users' preferences but rarely have used sequential information. In streaming recommender systems, the emergence of new patterns or disappearance a pattern leads to inconsistencies. However, these changes are common issues due to the user's preferences variations on items. Recommender systems without considering inconsistencies will suffer poor performance. Thereby, the present paper is devoted to a new fuzzy rough set-based method for managing in a flexible and adaptable way. Evaluations have been conducted on twelve real-world data sets by the leave-one-out cross-validation method. The results of the experiments have been compared with the other five methods, which show the superiority of the proposed method in terms of accuracy, precision, recall.
S. Javadi; R. Safa; M. Azizi; Seyed A. Mirroshandel
Abstract
Online scientific communities are bases that publish books, journals, and scientific papers, and help promote knowledge. The researchers use search engines to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases due to search ...
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Online scientific communities are bases that publish books, journals, and scientific papers, and help promote knowledge. The researchers use search engines to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases due to search by keywords and lack of attention to the content, they do not achieve the desired results at the early stages. Online scientific communities can increase the system efficiency to respond to their users utilizing a customized search. In this paper, using a dataset including bibliographic information of user’s publication, the publication venues, and other published papers provided as a way to find an expert in a particular context where experts are recommended to a user according to his records and preferences. In this way, a user request to find an expert is presented with keywords that represent a certain expertise and the system output will be a certain number of ranked suggestions for a specific user. Each suggestion is the name of an expert who has been identified appropriate to collaborate with the user. In evaluation using IEEE database, the proposed method reached an accuracy of 71.50 percent that seems to be an acceptable result.
