Multi-party urban flow analysis is a crucial task in smart cities. However, existing analysis methods has difficulty in trade-off between data privacy security and spatio-temporal feature capture. The solution to the problem of how to capture the complete spatio-temporal features of multi-party urban flow data while protecting data privacy is of great importance in multi-party urban flow analysis. Therefore, to address data privacy and spatio-temporal feature capture in multi-party urban flow analysis, this paper proposes a spatio-temporal federated analysis model, for multi-party urban flow mining, which is able to effectively protect data privacy and capture spatio-temporal features completely at the same time. Firstly, a multi-party urban flow mining framework based on federated learning is proposed to realize complete capture of spatio-temporal feature information of multi-party urban flow data and mining urban flow pattern knowledge under the premise of protecting data privacy. Secondly, to address the communication cost of the multi-party urban flow analysis, we propose a lazy aggregation method based on similarity clustering, which improves the communication efficiency between clients and the server. Further, we propose a similarity evaluation criteria for urban flow data based on step function, which can effectively calculate the similarity between urban flow data. Finally, we compare the proposed model with some benchmark methods on Chengdu Didi order data and point of interest data to prove the effectiveness of the proposed model and visualize and analyze the spatio-temporal features.

Accurate user profiles are crucial for personalized recommendation systems to mitigate information overload on large-scale online platforms. While recent advances in large language models have enhanced semantic understanding for profile construction through textual artifacts, existing methods often neglect the higher-order structural patterns inherent in user-item interaction graphs-a key limitation for achieving accurate and diverse recommendations. In this paper, we propose SSPRec, a Higher-order Structure and Semantics-enhanced User Profiling for Recommendation. Specifically, we first introduce a multi-hop proximity matrix over item-item transitions, followed by low-rank approximation and clustering to group users based on behavioral similarity. Group-level user profiles are then distilled via representative keywords extracted from co-interacted items, and collaborative embeddings are concurrently learned from the interaction graph. To integrate collaborative signals with language-based profiles, we introduce a cross-view contrastive objective that encourages coherence between structural and semantic representations. Final recommendations are made using a fused user-item similarity score. Extensive experiments on four real-world datasets show that SSPRec not only outperforms baselines in accuracy (with 46.35% improvements), but also remains diverse and robust, even under incomplete interactions.

Representation learning is an important topic in machine learning, pattern recognition, and data mining research. Among many representation learning approaches, semi-nonnegative matrix factorization (SNMF) is a frequently-used one. However, a typical problem of SNMF is that usually there is no learning rate guidance during the optimization process, which often leads to a poor representation ability. To overcome this limitation, we propose a very general representation learning framework (DNSRF) that is based on a deep neural net. Essentially, the parameters of the deep net used to construct the DNSRF algorithms are obtained by matrix element update. In combination with different activation functions, DNSRF can be implemented in various ways. In our experiments, we tested nine instances of our DNSRF framework on six benchmark datasets. In comparison with other state-of-the-art methods, the results demonstrate the superior performance of our framework, which is thus shown to have a great representation ability.

As the spatio-temporal data of a city is not always available, insufficient data would lead to poor performance in some urban prediction tasks. Existing works utilize transfer learning to solve the data scarcity problem, but they ignore the differences in data distributions across cities, which leads to the ineffectiveness of knowledge transfer. In this paper, we propose a domain adversarial model with knowledge transfer for spatio-temporal prediction across cities, entitledCityTrans. Specifically, 1) the self-adaptive spatio-temporal knowledge (namely ST-Knowledge) is mined, to learn the latent spatial and temporal patterns among cities; 2) the domain-adversarial training strategy is introduced to enhance domain invariance; 3) a knowledge attention mechanism is proposed to extract the transferable information from the ST-Knowledge. Note that our CityTrans is an end-to-end domain adversarial spatio-temporal network without two-stage training (i.e., pre-training and fine-tuning). Finally, we conduct extensive experiments on two spatio-temporal prediction tasks: traffic (flow and speed) prediction, and air quality prediction. Experimental results demonstrate that CityTrans outperforms state-of-the-art models on all tasks by a significant margin.

Clustering is a popular research topic in the field of data mining, in which the clustering method based on non-negative matrix factorization (NMF) has been widely employed. However, in the update process of NMF, there is no learning rate to guide the update as well as the update depends on the data itself, which leads to slow convergence and low clustering accuracy. To solve these problems, a generalized deep learning clustering (GDLC) algorithm based on NMF is proposed in this article. Firstly, a nonlinear constrained NMF (NNMF) algorithm is constructed to achieve sequential updates of the elements in the matrix guided by the learning rate. Then, the gradient values corresponding to the element update are transformed into generalized weights and generalized biases, by inputting the elements as well as their corresponding generalized weights and generalized biases into the nonlinear activation function to construct the GDLC algorithm. In addition, for improving the understanding of the GDLC algorithm, its detailed inference procedure and algorithm design are provided. Finally, the experimental results on eight datasets show that the GDLC algorithm has efficient performance.

Daily schedule recommendation is an intelligent approach to recommend multiple suitable activity locations and activity sequences for users based on their needs in a day. In such a scenario, training the model using traditional methods requires centralized data collection from individual users, which may be prohibited by data protection acts, such as GDPR and CCPA. In this paper, we address the problem of daily schedule recommendation utilizing the deep reinforcement learning model in a federated learning framework (FedDSR). And curriculum learning is applied to guide the training process towards better local optimization and better generalization. For the uploaded local parameters, a similarity aggregation algorithm is proposed to improve the quality of the model. The experimental results show that the proposed FedDSR model is superior and effective to multiple baselines on two real datasetsGeolifeandChengdu. Comparing with baselines, our method not only ensures that the parties do not need to share data and thus achieve joint modeling, but also can exceed$\sim\!\! 18\%$under evaluation metricperimeterand improve$\sim\! 0.72\%$under evaluation metricADTS.

Recommender system can provide users with the required information accurately and efficiently, playing a very important role in improving users' life experience. Although knowledge graph-based recommender system can solve the sparsity and cold start problems faced by traditional recommender system, it cannot handle the cross-domain cold start problem and cannot provide multi-domain recommendations. Therefore, this paper focuses on multi-domain item-item (I2I) recommendation based on cross-domain knowledge graph embedding by analyzing the association between items of the same domain and the interaction between items of diverse domains with the aid of knowledge graph that contains rich information. Firstly, a cross-domain knowledge graph chiasmal embedding approach is proposed to efficiently interact all items in multiple domains. To help achieve both homo-domain embedding and hetero-domain embedding of items, a binding rule is put forward. Secondly, a multi-domain I2I recommendation method is presented to efficiently recommend items in multiple domains, which is a recommendation method based on link prediction of knowledge graph. Finally, the proposed methods are compared and analyzed with some benchmark methods using two datasets. The experimental results show that the proposed methods achieve better link prediction results and multi-domain recommendation results.