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Article
Computer Science, Artificial Intelligence
Ali Akbar Noorian Avval et al.
Summary: This research proposes a novel personalized method for POI route recommendation that employs contextual data. The proposed approach enhances the existing methods by considering user preferences and multifaceted tourism contexts. Due to the sparsity of the data, the proposed method employs two-level clustering (DBSCAN based on the Manhattan distance) that reduces the time to discover POI.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yimeng Ren et al.
Summary: This study proposes a fully adaptive recommendation paradigm named TERD based on the Knowledge Distillation technique, aiming to improve the recommendation effect of the top positions. The experimental evaluation demonstrates that the TERD scheme effectively resolves the Top-enhanced recommendation issue.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Thi N. N. Dinh et al.
Summary: The process of finding suitable papers related to an interesting topic in research can be time-consuming and labor-intensive. Citation recommendation, using deep learning techniques, has significantly improved the performance of recommending relevant papers. However, existing models still face limitations in integrating auxiliary information for contextual learning. In this paper, we propose a novel context-aware Neural Citation Network (NCN) model with additional textual data integration and BERT model to enhance the performance of citation recommendation. Extensive experiments on the arXiv dataset demonstrate the effectiveness of our proposed model.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Syed Raza Bashir et al.
Summary: In this study, we propose a sophisticated location-aware recommendation system that uses BERT model to provide personalized location-based suggestions based on users' historical behavior and preferences.
Article
Computer Science, Artificial Intelligence
Mohammad Yahya H. Al-Shamri
Summary: This paper explores the importance of similarity matching in recommender systems and how modifiers can be used to enhance matching performance, introducing new modifiers and demonstrating their effectiveness on popular datasets.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Tajuddeen Rabiu Gwadabe et al.
Summary: Session-based recommendation aims to recommend the next item for anonymous user sessions. Existing models only consider the current session and overlook the co-occurrence patterns of items in different sessions. In this study, the IC-GAR model is proposed to augment the representations of the current session with session co-occurrence patterns, achieving significant improvement in performance across multiple datasets.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Abderrahim Lakehal et al.
Summary: This paper proposes a recommendation method based on semantic and probabilistic situations, using the ontology approach and Bayesian classifier to predict user context for improved service recommendation accuracy and relevance. By utilizing four probability-based context rule situation items, the method calculates similarity through weighted linear combination to identify relevant user situations. Additionally, context parameters of current devices are used to ensure adaptive service recommendation. Experimental results demonstrate enhanced accuracy and efficiency compared to existing recommendation approaches.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Agyemang Paul et al.
Summary: Information retrieval is useful in all aspects of life. Many systems use personalized recommendation to assist users, but traditional methods only model user-product interaction data, yielding limited results. This paper proposes a personalized recommendation method that combines visual, temporal, and sequential information, and demonstrates its effectiveness through experiments.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Dongjin Yu et al.
Summary: This paper proposes a method for POI recommendation using user-generated content. By constructing a heterogeneous graph and employing Node2Vec technique, the method establishes latent vectors and integrates multiple factors for recommendation. Experimental results demonstrate that this method outperforms other approaches in terms of accuracy on two real datasets.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Omar Abdel Wahab et al.
Summary: This paper proposes a federated learning-based approach to address the problem of cold-start items in recommendation systems. The uniqueness of this approach lies in its specific application to the cold-start problem and the introduction of a trust mechanism and a double deep Q learning scheduling method based on trust and energy levels. Simulation experiments show that this method improves the accuracy of recommending cold-start items.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Chuanyi Li et al.
Summary: This research proposes a method of recommending statutes by modeling the relationship between case content and law content and using interpretable hand-crafted features. Experimental results show that the pairwise ranking model with the proposed novel features achieves the best performance.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jin Fan et al.
Summary: This study proposes a sequential recommendation model called AUBRec, which uses controlled bidirectional self-attention to model user behavior sequences in an augmented manner. By constructing item interaction patterns and locally augmenting attention, the accuracy and robustness of the model are improved. Compared to the current state-of-the-art methods, our approach achieves better performance in sequential recommendation.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Junhang Wu et al.
Summary: The prevalence of Location-based Social Networks (LBSNs) services has made personalized Point-of-Interest (POI) predictions an important research topic. However, missing geolocation information hinders existing POI-oriented research for user preference analysis. To address this issue, we propose a novel model called Bi-STAN, which combines spatiotemporal transition patterns and personalized dynamic preferences to identify missing check-in POIs. Experimental results demonstrate the effectiveness of our proposed method, and Bi-STAN also performs well in next POI prediction tasks.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Guangli Li et al.
Summary: The study introduces a new multi-modal visual adversarial Bayesian personalized ranking (MVABPR) model to address the challenge of data sparseness in recommendation systems. By leveraging cross-modal semantic features and employing an adversarial learning strategy, the MVABPR model proves to be effective and robust, outperforming competitive baselines. Additionally, the model is capable of jointly learning visual information and user ratings, while accurately capturing the implicit feeling tone of recommended items on large-scale sparser datasets.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Minghao Yin et al.
Summary: The proposed tensor decomposition based collaborative filtering (TDCF) algorithm aims to solve the POI recommendation problem in LBSN by improving precision and recall value through addressing data sparsity and personalized preference.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Chunhong Zhang et al.
APPLIED INTELLIGENCE
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Fei Sun et al.
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19)
(2019)
Article
Computer Science, Artificial Intelligence
Kwan Hui Lim et al.
KNOWLEDGE AND INFORMATION SYSTEMS
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Yanpeng Sun et al.
PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT I
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Wang-Cheng Kang et al.
2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)
(2018)
Article
Computer Science, Artificial Intelligence
Evaggelos Spyrou et al.
