Multi-behavior Session Based Recommendations (MBSBRs) have achieved remarkable results due to considering behavioral heterogeneity in sessions. Yet most existing works only consider binary or continuous behavior dependencies and aim to predict the next item under the target behavior, neglecting users' inherent behavior habits, resulting in learning inaccurate intentions. To tackle the above issues, we propose a novelBehaviorHabits Enhanced Intention Learning framework forSessionBasedRecommendation (BHSBR). Specifically, we focus on the next item recommendation and design a global item transition graph to learn the behavior-aware semantic relationships between items, in order to mine the underlying similarity between items beyond the session. In addition, we construct a hypergraph to extract the diverse behavior habits of users and break through the limitations of temporal relationships in the session. Compared to the existing works, our behavior habit learning method learns behavior dependencies at the user level, which could capture the user's more accurate long-term intentions and reduce the impact of noise behaviors. Extensive experiments on three datasets demonstrate that the performance of our proposedBHSBRis superior to SOTA. Further ablation experiments fully illustrate the effectiveness of our various modules.

Session-based recommendation (SBR) aims to predict upcoming user choices based on brief interaction histories. Over the past few years, graph neural networks (GNNs) have become a powerful tool for capturing intricate item relationships and delivering effective recommendations. Existing works use the sequential interactions within all sessions to construct graphs and provide self-supervised signals. Although some progresses have been made, we argue that merely relying on the transitions pattern fail to fully mine the complex information among items and lead to limited item representations. This article introduces a semantic information and intention enhanced SBR paradigm, which is called SISR. Our SISR leverages not only the sequential order of items but also the session intentions and semantic neighbors. Specifically, we begin by constructing a global item transition graph to enhance the GNN-based SBR with insights from items across all sessions. Then, the clustering mechanism is applied to obtain latent semantic prototypes of items and further extract the intention representations of sessions. Finally, we propose two contrastive learning component to distill the self-supervised signals for the item representation learning, so as to alleviate the data-sparsity phenomenon and augment the item recommendation component ratio. Comprehensive testing on three real-world datasets against various leading models highlights the advantages of our SISR paradigm.

Session-based recommendation (SBR) systems have increasingly focused on hypergraph-based approaches due to their potent capability in capturing high-order item relationships. Typically, existing approaches rely on sequential item relations to manually construct fixed hypergraphs. However, this methodology neglects the multiple relations inherent in the original sequences, thereby impeding the hypergraph’s precision in discerning user preferences. Furthermore, the rigidity of fixed hypergraph structures tends to emphasize explicit relationships, ignoring the latent implicit patterns. In light of this, we present a novel Multi-relation enhanced Dynamic HyperGraph (MDHG) learning framework for session-based recommendation, to model intricate and variable item relations. Initially, we establish three distinct relation graphs which capture separate user behavior patterns to extract personalized interest preferences under differentiated intentions. Subsequently, we propose an enhanced dynamic hypergraph paradigm that adaptively generates hypergraph structures based on prior relation graph, thereby reinforcing and unveiling implicit connectivity relations in a layer-aware manner. Finally, to mitigate the noise among diverse relations, we introduce the maximum mutual information auxiliary task and employ the attention mechanism as a cross-relation aggregator. Extensive experiments on various real-world datasets verify the superiority of our MDHG model. Our code is publicly available at https://github.com/Qin-lab-code/MDHG .

The best arm identification problem in multi-armed bandit model has been widely applied into many practical applications, such as spectrum sensing, online advertising, and cloud computing. Although lots of works have been devoted into this area, most of them do not consider the cost of pulling actions, i.e., a player has to pay some cost when she pulls an arm. Motivated by this, we study a ratio-based best arm identification problem, where each arm is associated with a random reward as well as a random cost. For any \(\delta\in(0,1)\) , with probability at least \(1-\delta\) , the player aims to find the arm with the largest ratio of expected reward to expected cost using as few samplings as possible. Specifically, we consider two settings: (1) the precise setting, i.e., identifying the precise optimal one; (2) the Probably Approximate Correct (PAC) setting, which identifies the \(\epsilon\) -optimal one. For the precise setting, we design the elimination-type algorithms and provide a fundamental lower bound which asymptotically matches the upper bound, while in the PAC setting, an UCB-type algorithm which amed \(\epsilon\) -RCB algorithm is proposed. We show that for all algorithms, the sample complexities, i.e., the pulling times for all arms, grow logarithmically as \(\frac{1}{\delta}\) increases. Moreover, compared to existing works, the running of our algorithms is independent of the arm-related parameters, which is more practical. Finally, we validate our theoretical results through numerical experiments.

PM2.5is hazardous to human health, and high-quality data are thus needed on a routine basis. An attempt is made here to improve the accuracy of near-surface PM2.5estimates using the newly released aerosol product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite with the Deep Blue retrieval algorithm. A high-quality PM2.5data set is generated at a spatial resolution of 6 km from 2013 to 2018 by applying the space-time extremely randomized trees (STET) model, which also aims to extend the Earth Observing System (EOS) long-term PM2.5data records in China. The PM2.5estimates are highly consistent with ground-based measurements, with an out-of-sample cross-validation coefficient of determination (CV-R2) of 0.88, a root-mean-square error (RMSE) of$16.52~\mu \text{g}/\text{m}^{3}$, and a mean absolute error of$10~\mu \text{g}/\text{m}^{3}$at the national scale. Spatiotemporal PM2.5variations at monthly scales are also well captured (e.g.,$R^{2} =0.91$–0.94, RMSE = 5.8–$11.6~\mu \text{g}/\text{m}^{3})$. PM2.5varied greatly at regional and seasonal scales across China. Benefiting from emission reduction and air pollution controls, PM2.5pollution has reduced dramatically in China with an average of$- 5.6~\mu \text{g}/\text{m}^{3}$/yr−1during 2013–2018. Significant regional reductions are also seen, in particular, in the Beijing–Tianjin–Hebei region ($- 6.6~\mu \text{g}/\text{m}^{3}$/yr−1,$p < 0.001$), and the Deltas of Yangtze River ($- 6.3~\mu \text{g}/\text{m}^{3}$/yr−1,$p < 0.001$) and Pearl River Delta ($- 4.5~\mu \text{g}/\text{m}^{3}$/yr−1,$p < 0.001$). Our study improved the accuracy of near-surface PM2.5estimates in terms of their spatiotemporal variations at a relatively long-term record, which is important for future air pollution and health studies in China.

SpaceX plans ambitiously to launch approximately 12,000 satellites from 2019 to 2024, expected to be a complement or even competitor to ground networks. However, the mega-scale satellite network is topology-varying and the frequency of inter-satellite link (ISL) handovers increases rapidly as the topology expands, which will further arouse a massive number of route updates with considerable packet travel delay or even packet loss during the route convergence. The classic Dijkstra's algorithm is adopted for space route calculation, however, it always selects the default shortest path from multiple equal-cost shortest paths between two satellite nodes. To reduce the route change as much as possible during the periodical topology change, in this work, we refined the original Dijkstra and propose StableRoute to select the most appropriate route from the equal-cost candidates with the least route updates compared with the routing table last round. In this way, the end-to-end paths can be maintained as far as possible without time-to-time oscillation. Evaluation shows that it reduces 41% of the route updates in a 36 × 36 topology compared with Dijkstra, and the reduction rate will rise persistently with the growth of the satellite constellation.

With global coverage as well as ultra-low latency, the Low-Earth-Orbit (LEO) satellite constellation is regarded as an ideal complement to the terrestrial network infrastructure. One technical issue in LEO satellite networks is efficient and resilient routing. Considering the periodic topology changes, straightforwardly leveraging terrestrial routing protocols, such as OSPF, will incur endless route convergence, consuming expensive inter-satellite link bandwidth. Prior work proposes several snapshot-based routing approaches, which either require to store a sequence of routing table snapshots in limited satellite memory, or have to maintain frequent interaction with the ground stations. In this work, we propose OPSPF, a novel routing protocol dedicated to LEO satellite networks. OPSPF takes advantage of the regularity of the constellation and conducts periodic route calculation for instantaneous routing table generation, which well handles the regular topology changes. Moreover, OPSPF proposes an on-demand dynamic routing mechanism, dedicated to the irregular topology changes caused by link failure/recovery. Evaluation shows, compared with OSPF, OPSPF has zero route convergence overhead during regular topology changes and 57% reduction of the communication overhead and 82% reduction of the route convergence time during irregular topology changes.