DAP$$^2$$CMH: Deep Adversarial Privacy-Preserving Cross-Modal Hashing

Zhu, Lei; Song, Jiayu; Yang, Zhan; Huang, Wei‐Ping; Zhang, Chengyuan; Yu, Weiren

doi:10.1007/s11063-021-10447-4

Cited by 12 publications

(2 citation statements)

References 65 publications

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“…The GraphSAGE model is an inductive learning framework that can efficiently generate unknown vertices embedding by using the attribute information of vertices [6,7]. It is used to derive the user trust relationships from the original social network that hold both local and global information of the social network [8][9][10], and a graph-embedded model-based collaborative filtering recommendation algorithm is proposed. Intuitively, the low-dimensional feature representation of user nodes in social networks can be learned through graph embedding and can be integrated into traditional social network-based recommendation algorithms to address the problems of coarse granularity.…”

Section: Introductionmentioning

confidence: 99%

Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation

Song

Yuan

et al. 2022

Future Internet

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With the rapid development of Internet technology, how to mine and analyze massive amounts of network information to provide users with accurate and fast recommendation information has become a hot and difficult topic of joint research in industry and academia in recent years. One of the most widely used social network recommendation methods is collaborative filtering. However, traditional social network-based collaborative filtering algorithms will encounter problems such as low recommendation performance and cold start due to high data sparsity and uneven distribution. In addition, these collaborative filtering algorithms do not effectively consider the implicit trust relationship between users. To this end, this paper proposes a collaborative filtering recommendation algorithm based on graphsage (GraphSAGE-CF). The algorithm first uses graphsage to learn low-dimensional feature representations of the global and local structures of user nodes in social networks and then calculates the implicit trust relationship between users through the feature representations learned by graphsage. Finally, the comprehensive evaluation shows the scores of users and implicit users on related items and predicts the scores of users on target items. Experimental results on four open standard datasets show that our proposed graphsage-cf algorithm is superior to existing algorithms in RMSE and MAE.

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Section: Introductionmentioning

confidence: 99%

Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation

Song

Yuan

et al. 2022

Future Internet

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“…With the rapid development of mobile internet, social networks, and Location-Based Service (LBS), large numbers of multimedia data [1] with geographical information (a.k.a geo-multimedia) [2], such as text, image [3,4], and video [5][6][7][8], are collected and stored on the internet. As an important data resource, geo-multimedia data is used to support for location-based recommendation, accurate advertising and data search.…”

Section: Introductionmentioning

confidence: 99%

TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data

et al. 2021

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Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data and construct efficient index is crucial for large-scale geo-multimedia retrieval. To combat this challenge, the paper proposes a deep cross-modal hashing framework for geo-multimedia retrieval, termed as Triplet-based Deep Cross-Modal Retrieval (TDCMR), which utilizes deep neural network and an enhanced triplet constraint to capture high-level semantics. Besides, a novel hybrid index, called TH-Quadtree, is developed by combining cross-modal binary hash codes and quadtree to support high-performance search. Extensive experiments are conducted on three common used benchmarks, and the results show the superior performance of the proposed method.

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