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.

Cross-modal retrieval plays a key role in the Natural Language Processing area, which aims to retrieve one modality to another efficiently. Despite the notable achievements of existing cross-modal retrieval methodologies, the complexity of the embedding space increases with more complex models, leading to less interpretable and potentially overfitting representations. Most existing methods realize outstanding results based on datasets without any error or noise, but that is extremely ideal and leads to trained models lacking robustness. To solve these problems, in this paper, we propose a novel approach, Soft Contrastive Cross-Modal Retrieval (SCCMR), which integrates the deep cross-modal model with soft contrastive learning and smooth label cross-entropy learning to boost common subspace embedding and improve the generalizability and robustness of the model. To confirm the performance and effectiveness of SCCMR, we conduct extensive experiments comparing 12 state-of-the-art methods on three multi-modal datasets by using image–text retrieval as a showcase. The experimental results show that our proposed method outperforms the baselines.

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.