With the boosting of neural networks, recommendation methods become significantly improved by their powerful ability of prediction and inference. Existing neural-network based recommender systems (NN-RSs) usually first employ matrix embedding (ME) as a pre-process to learn users’ and items’ representations (latent vectors), then input these representations to a specific modified neural network framework to make accurate Top-k recommendations. Obviously, the performance of ME has a significant effect on RS models. However, most NN-RSs focus on accuracy by building representations from the direct user-item interactions (e.g., user-item rating matrix), while ignoring the underlying relatedness between users and items (e.g., users who rate the same ratings for the same items should be embedded into similar representations), which is an ideological disadvantage. On the other hand, ME models directly employ inner products as a default loss function metric that cannot project users and items into a proper latent space, which is a methodological disadvantage. In this paper, we propose a supervised collaborative representation learning model - Magnetic Metric Learning (MML) - to map users and items into a unified latent vector space, enhancing the representation learning for NN-RSs. First, MML utilizes dual triplets to model not only the observed relationships between users and items, but also the underlying relationships between users as well as items to overcome the ideological disadvantage. Specifically, a modified metric-based dual loss function is proposed in MML to gather similar entities and disperse the dissimilar ones. With MML, we can easily compare all the relationships (user to user, item to item, user to item) according to the weighted metric, which overcomes the methodological disadvantage. We conduct extensive experiments on four real-world datasets with large item space. The results demonstrate that MML can learn a proper unified latent space for representations from the user-item matrix with high accuracy and effectiveness, and lead to a performance gain over the state-of-the-art RS models by an average of 17 percent.

Mobile Crowdsensing (MCS) is a promising paradigm that recruits users to cooperatively perform a sensing task. When recruiting users, existing works mainly focus on selecting a group of users with the best objective ability, e.g., the user's probability or frequency of covering the task locations. However, we argue that the task completion effect depends not only on the user's objective ability, but also on their subjective collaboration likelihood with each other. Furthermore, even though we can find a well-behaved group of users in the single-round scenario, while in the multi-round scenario without enough prior knowledge, we still face the problem of recruiting previously well-behaved user groups (exploitation) or recruiting uncertain user groups (exploration). Additionally, we consider that each user has a different cost, and the platform recruits users under a cost budget; thus, the problem becomes more challenging: users may report fake costs to gain more profits. To address these problems, assuming that the user's information is known, we first convert the single-round user recruitment problem into the min-cut problem and propose a graph theory based algorithm to find the approximate solution. Then, in the multi-round scenario where the user's information is estimated from the previous rounds, to balance the trade-off between exploration and exploitation, we propose the multi-round User Recruitment strategy under the budget constraint based on the combinatorial Multi-armed Bandit model (URMB), which is proven to achieve a tight regret bound. Next, we propose a graph-based payment strategy to achieve truthfulness and individual rationality of users. Finally, extensive experiments on three real-world datasets show that URMB always outperforms the state-of-th-art strategies.

The past flourishing years of sequential point-of-interest (POI) recommendation began with the introduction of Self-Attention Network (SAN), which quickly superseded CNN or RNN as the state-of-the-art backbone. To realize the fine-grained users' behavior patterns modeling, recent works utilize modified attention mechanisms or neural network layers to process spatial-temporal factors. However, due to the significant increase on either model's parameter scale or computational burden, we argue that these methods can be further improved. In this paper, we exploit two lightweight approaches, Time Aware Position Encoder (TAPE) and Interval Aware Attention Block (IAAB), to impel SAN by considering the spatial-temporal intervals among POIs separately, where requiring neither extra parameters nor high computational cost. On the one hand, TAPE, adjusting the positions in sequences based on the timestamps dynamically and generating positional representations with sinusoidal transformation, can enhance sequence representations to reflect both the absolute order and relative temporal proximity among all POIs. On the other hand, IAAB, point-wise adding the scaled spatial-temporal intervals to the attention map, can promote the attention mechanism attaching importance to the spatial relation among all POIs under the constraints of time conditions and providing more explainable recommendation. We integrate these two modules into SAN and propose a Spatial-Temporal Interval-Aware sequential POI recommender, namely STiSAN, as an end-to-end deployment. Experimental results based on three public LBSN datasets and one real-world city transportation dataset demonstrate STiSAN's superior performance (average 13.01% improvement against the strongest baseline). Moreover, we validate the extensibility and interpretability of TAPE and IAAB through metric evaluation and visualization separately.

In this work, we present STiSAN $^+$ as an end-to-end mobility trajectory prediction framework. It is a Self-Attention Network (SAN)-based model augmented with two lightweight approaches: Rotary Time Aware Position Encoder (RoTAPE) and multi-head Interval Aware Attention Block (IAAB). These methods allow for the explicit, efficient and effective processing of spatial-temporal intervals among the user's historical trajectory, without the need for additional parameters or a substantial computational burden. On the one hand, RoTAPE simultaneously encodes day- and hour-level timestamps into the sequence representation via a sinusoidal encoding matrix. Notably, the multi-level temporal differences operate in a mutually independent manner to reflect the periodical pattern, and collectively measure the absolute time interval. On the other hand, IAAB, point-wise injecting the historical spatial-temporal intervals into the attention map, can promote SAN attaching importance to the spatial relations under the constraints of time conditions. Moreover, we equip STiSAN $^+$ with a novel MLP-based module, namely Spatial-Temporal Relation Memory (STR Memory). It endows the interactions inside historical intervals along different directions, and converts them into spatial-temporal relations in future trajectories for accurate predictions. The empirical study on six public LBSN shows that from Next Location Recommendation to Multi-location Future Trajectory Prediction, our STiSAN $^+$ gains average 15.05% and 18.35% improvements against several state-of-the-art sequential models, respectively. We demonstrate the effectiveness of proposed module with an ablation study, and validate the extensibility and interpretability of RoTAPE and IAAB through non-sampled metric evaluation and visualization.

Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them bored and unsatisfied. The key to solving these issues is learning users' fine-grained preferences and recommending appealing and unexplored items deviating from users' historical items. However, existing models consider cold-start or filter bubble problems separately and ignore that they can reinforce mutually and damage the models' performance accuracy. To this end, we devise a novel serendipity-oriented recommender system ( G enerative S elf-constrained S erendipitous R ecommender S ystem, GS$^{2}$-RS ) that generates users' fine-grained preferences to enhance the recommendation performance. Specifically, GS $^{2}$ -RS extracts users' interest and satisfaction preferences and generates virtual but convincible neighbors' preferences from themselves with a twin Conditional Generative Adversarial Nets (not from real neighbors). Then we introduce the serendipity item, which is low-interest but high-satisfaction among candidate items. We use the serendipity item to improve the diversity of recommended items, which relieves the filter-bubble problem. Along with this line, a gated mechanism is applied to their fine-grained preferences (interests, satisfactions) to obtain their serendipity items. Finally, these serendipity items are inversely injected into the original user-item rating matrix and build a relatively dense matrix as the input for backbone RS models. Note that GS $^{2}$ -RS tackles cold-start and filter-bubble problems in a unified framework without any additional side information and enriches the interpretability of recommendation models. We comprehensively validate GS $^{2}$ -RS for solving cold-start and filter bubble problems on four real-world benchmark datasets. Extensive experiments illustrate GS $^{2}$ -RS's superiority in accuracy, serendipity, and interpretability over state-of-the-art models. Also, we can plug our model into existing recommender systems as a preprocessing procedure to enhance their performance.

A high-performance network deployment strategy supports fundamental network services, such as topology controls, protocol designs, and boundary detections in underwater wireless sensor networks (UWSNs). Existing deployment methods treat nodes within the communication range as connected. However, in addition to internode distance, packet errors and collisions are also significant factors for point-to-point connectivity. Furthermore, when allocating node locations, deployment strategies focus on maximizing coverage, ignoring the tradeoff between coverage and network performance (reliability, latency, and energy efficiency). To this end, an efficient deployment scheme with network performance modeling (EDNPM) is proposed, to provide reliable data transmission in a time-aware and energy-efficient way for UWSNs. Specifically, we first explore sensor locations’ impact on communication and network factors, to improve the point-to-point connectivity and network performance. A network performance evaluation model (NPEM) is established to quantify performance metrics for guiding network deployment. Based on NPEM, network deployment is formulated as a multiobjective optimization problem, and we propose a novel network connection-constraint particle swarm optimization (NCPSO) algorithm to solve this problem. Notably, EDNPM is a unified network deployment framework for various underwater applications. Extensive experiments demonstrate that EDNPM outperforms other deployment algorithms in terms of network performance, and robustness with different network settings.