Recommendation by Users’ Multimodal Preferences for Smart City Applications

Xu, Cai; Guan, Ziyu; Zhao, Wei; Quan-zhou, WU; Yan, Meng; Chen, Long; Miao, Qiguang

doi:10.1109/tii.2020.3008923

Cited by 45 publications

(7 citation statements)

References 20 publications

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“…The implementation of smart cities is not without its challenges. One of the main challenges is social inclusion and equity in access to technology [11]. It is important to ensure that all citizens can benefit from the improvements offered by technology, thus avoiding the digital divide.…”

Section: Background Of Smart Citiesmentioning

confidence: 99%

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Andrade-Ruiz,

Carrasco,

Porcel

et al. 2024

Electronics

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Smart cities represent the convergence of information and communication technologies (ICT) with urban management to improve the quality of life of city dwellers. In this context, recommender systems, tools that offer personalised suggestions to city dwellers, have emerged as key contributors to this convergence. Their successful application in various areas of city life and their ability to process massive amounts of data generated in urban environments has expedited their status as a crucial technology in the evolution of city planning. Our methodology included reviewing the Web of Science database, resulting in 130 articles that, filtered for relevancy, were reduced to 86. The first stage consisted of carrying out a bibliometric analysis with the objective of analysing structural aspects with the SciMAT tool. Secondly, a systematic literature review was undertaken using the PRISMA 2020 statement. The results illustrated the different processes by which recommendations are filtered in areas such as tourism, health, mobility, and transport. This research is seen as a significant breakthrough that can drive the evolution and efficiency of smart cities, establishing a solid framework for future research in this dynamic field.

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Section: Background Of Smart Citiesmentioning

confidence: 99%

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Andrade-Ruiz,

Carrasco,

Porcel

et al. 2024

Electronics

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“…The first category involves learning representations from different modal features individually and then directly concatenating or averaging these representations to generate multi-modal representations of users or items. For example, users' multimodal preferences-based recommendation (UMPR) [8] is a recommendation model that captures users' multi-modal preferences by mining information from user comments to obtain user-level visual preferences. To further enhance item cold start performance, the Pre-trained Multimodal Graph Transformer (PMGT) [9] model integrates multi-modal information into item representations and achieves pre-training on a homogeneous item graph.…”

Section: B Multi-modal Recommendation Algorithmsmentioning

confidence: 99%

Multi-modal similarity fusion for user behavior sequence modeling

2024

Fourth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2023)

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With the pervasive use of the Internet, recommendation systems have gained increasing importance in people's daily lives. Among the crucial tasks in recommendation systems, click-through rate prediction stands out as it directly influences their effectiveness. Recent studies have revealed that incorporating user behavior sequences can substantially enhance the accuracy of click-through rate prediction models. However, existing models overlook user preferences for textual and visual information, which hampers the acquisition of a comprehensive representation of user interests. Consequently, this limitation results in suboptimal model accuracy. In this paper, we propose a unified framework for modeling multi-modal user behavior sequences. Our framework leverages a unified cross-modal pre-trained model for feature extraction and employs a multi-modal similarity-enhanced attention mechanism to capture users' preferences across various modalities. We conduct extensive experiments on large-scale real-world datasets to validate the effectiveness of our approach. Compared to other state-of-the-art click-through rate estimation algorithms, our model achieves an approximate 1.22% improvement in AUC, thereby significantly enhancing the accuracy of the recommendation model.

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“…[60], [53] MLP / Concat None 1 Model Optimization (Section 4): all of the two-step training models are bolded in reference, while others are optimized in End-to-End manner.…”

Section: Restaurantmentioning

confidence: 99%

“…Besides, though UMPR [53] does not have an explicit CL loss function, it also constructs a loss function that describes the difference between visual positive and negative samples.…”

Section: Contrastive Learningmentioning

confidence: 99%

Multimodal Recommender Systems: A Survey

Liu¹,

Hu²,

Xiao³

et al. 2023

Preprint

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The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, such as short video, news and etc., understanding these contents while recommending becomes critical. Besides, multimodal features are also helpful in alleviating the problem of data sparsity in RS. Thus, Multimodal Recommender System (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.First, we conclude the general procedures and major challenges for MRS. Then, we introduce the existing MRS models according to three categories, i.e., Feature Interaction, Feature Enhancement and Model Optimization. To make it convenient for those who want to research this field, we also summarize the dataset and code resources. Finally, we discuss some promising future directions of MRS and conclude this paper.

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Recommendation by Users’ Multimodal Preferences for Smart City Applications

Cited by 45 publications

References 20 publications

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Multi-modal similarity fusion for user behavior sequence modeling

Multimodal Recommender Systems: A Survey

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