In recommender systems, it is frequently presumed that missing ratings adhere to a missing at random (MAR) mechanism, implying the absence of ratings is independent of their potential values. However, this assumption fails to hold in real-world scenarios, where users are inclined to rate items they either strongly favor or disfavor, introducing a missing not at random (MNAR) scenario. To tackle this issue, prior researchers have utilized explicit MAR feedbacks to infer the propensities of unobserved, implicit MNAR feedbacks. Nonetheless, acquiring explicit MAR feedbacks is resource-intensive and time-consuming and may not reflect users’ true preferences. Furthermore, most methods have only been tested on synthetic or small-scale datasets, thus their applicability and effectiveness in real-world settings without MAR feedbacks remain unclear. Along these lines, we aim to predict MNAR ratings without MAR prior propensities by exploring the consistency between MAR and MNAR feedbacks and narrowing the gap between them. From the empirical study and preliminary experiment, we hypothesize thatuser preferencescan be treated as the common prior propensity for both MAR and MNAR generative processes. In this way, we extend this hypothesis to a more general MNAR scenario: user preferences learned from MNAR can partially substitute for the prior propensities derived from MAR feedbacks for MNAR recommendation tasks. To validate our hypothesis and approach, we develop a lightweight iterative probabilistic matrix factorization framework (lightIPMF) as a practical method of our methodology, utilizing user preferences extracted from MNAR, not MAR, to estimate MNAR feedbacks. Finally, the experimental results show that modeling user preferences can effectively improve MNAR feedback estimation without MAR feedback, and our proposed lightIPMF outperforms the state-of-the-art MNAR methods in predicting MNAR feedbacks.

The past flourishing years of sequential location-based services began with the introduction of the Self-Attention Network (SAN), which quickly superseded CNN or RNN as the state-of-the-art backbone. Recent works utilize modified attention mechanisms or neural network layers to process spatial-temporal factors to realize fine-grained individual behavior pattern modeling. However, we argue these methods can be further improved due to the significant increase in the model's parameter scale or computational burden. In this paper, we first exploit two lightweight approaches, Rotary Time Aware Position Encoder (RoTAPE) and multi-head Interval Aware Attention Block (IAAB), to impel SAN by efficiently and effectively capturing spatial-temporal intervals among the user's visited locations, which require neither extra parameters nor a high computational cost. On the one hand, RoTAPE encodes the day- and hour-level timestamps into sequence representation simultaneously via a sinusoidal encoding matrix, and the corresponding time intervals can be explicitly captured by SAN. Specifically, the multi-level temporal differences are mutually independent to reflect the periodical pattern and jointly complete to 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. Then, we design a novel MLP-based module, Spatial-Temporal Relation Memory (STR Memory), implemented with fully connected linear layers and matrix transpose operations. STR Memory, endowing the interactions inside historical intervals along different directions, can convert the historical intervals into spatial-temporal relations in future trajectories for accurate predictions. To this end, we propose an end-to-end mobility trajectory prediction framework, namely STiSAN$^+$, employing RoTAPE, stacking multiple layers of IAAB-based encoder-decoder architecture, and coupling with STR Memory. We conducted numerous experiments on six public LBSN datasets to evaluate our proposed algorithm. 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. Ablation studies demonstrate the effectiveness of RoTAPE, IAAB, and STR Memory under our framework. Moreover, we separately validate the extensibility and interpretability of RoTAPE and IAAB through non-sampled metric evaluation and visualization.

We study defending strategies against adversarial attacks onCombinatorial Multi-Armed Bandits(CMAB) algorithms. CMAB is an effective sequence decision making tool that has been broadly applied in online real-world applications. We consider a realistic CMAB setting, budgeted CMAB, in which multiple arms associated with pulling costs and unknown rewards are pulled per round, aiming to maximize the cumulative reward under a budget constraint. However, the adversarial attack against budgeted CMAB is rarely studied, posing a very important security issue. Specifically, a suboptimal arm that is not pulled (i.e., attacker) can hijack the budgeted CMAB algorithm's behavior, forcing itself to be pulled frequently by manipulating other arms' rewards. Existing strategies cannot prevent such attacks. Motivated by this, we closely study the adversarial attack against a popular budgeted CMAB algorithm, exposing a significant security threat to real-world applications. The attack extends to other algorithms with certain customization. To address this, we incorporate a truthful pricing-based defending strategy that prevents such attacks effectively and ensures arms share pulling costs truthfully. Extensive simulations have illustrated the proposed attack strategy can hijack the algorithm efficiently, while the defending strategy provides attack prevention, individual rationality, and asymptotic truthfulness guarantees.

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 (GenerativeSelf-constrainedSerendipitousRecommenderSystem,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.

Points-of-interest (POIs) recommendation plays a vital role in location-based social networks (LBSNs) by introducing unexplored POIs to consumers and has drawn extensive attention from academia and industry. Existing POI recommender systems usually learn fixed latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatio-temporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers’ complicated check-in actions. To this end, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe the dynamic nature of POIs. Besides, check-in actions to a POI are also affected by the zone where it is located. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance POI embeddings. Along this line, we propose aTime-zone-spacePOI embedding model (ToP), which integrates multi-knowledge graphs and topic model to introduce not only spatio-temporal effects but also sentiment constraints into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in a different period with spatial constraints via knowledge graph learning. To add sentiment constraints, ToP jointly combines these vectors with the zone's representations learned by topic models to make explainable recommendations. ToP considers the time, space, and sentiment of POI in a unified embedding framework, which benefits the POI recommendations. Extensive experiments on real-world Changchun city datasets demonstrate that ToP achieves state-of-the-art performance in terms of common metrics and provides more insights for consumers’ POI check-in actions.

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.

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.

In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose to threaten the retailers for illegal profits. PMUs are difficult to be detected because they utilize masking strategies to disguise themselves as normal users. Specifically, there are three challenges for PMU detection: 1) professional malicious users do not conduct any abnormal or illegal interactions (they never concurrently leave too many negative reviews and low ratings at the same time), and they conduct masking strategies to disguise themselves. Therefore, conventional outlier detection methods are confused by their masking strategies. 2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem. 3) there are no datasets with labels for professional malicious users in public, which makes PMU detection an unsupervised learning problem. To this end, we propose an unsupervised multi-modal learning model: MMD, which employs Metric learning for professional Malicious users Detection with both ratings and reviews. MMD first utilizes a modified RNN to project the informational review into a sentiment score, which jointly considers the ratings and reviews. Then professional malicious user profiling (MUP) is proposed to catch the sentiment gap between sentiment scores and ratings. MUP filters the users and builds a candidate PMU set. We apply a metric learning-based clustering to learn a proper metric matrix for PMU detection. Finally, we can utilize this metric and labeled users to detect PMUs. Specifically, we apply the attention mechanism in metric learning to improve the model’s performance. The extensive experiments in four datasets demonstrate that our proposed method can solve this unsupervised detection problem. Moreover, the performance of the state-of-the-art recommender models is enhanced by taking MMD as a preprocessing stage.

Most existing research about task allocation in mobile crowdsensing mainly focus on requester-centric mobile crowdsensing (RCMCS), where the requester assigns tasks to workers to maximize his/her benefits. A worker in RCMCS might suffer benefit damage because the tasks assigned to him/her may not maximize his/her benefit. Contrarily, worker-centric mobile crowdsensing (WCMCS), where workers autonomously select tasks to accomplish to maximize their benefits, does not receive enough attention. The workers in WCMCS can maximize their benefits, but the requester in WCMCS will suffer benefit damage (cannot maximize the number of expected completed tasks). It is hard to maximize the number of expected completed tasks in WCMCS, because some tasks may be selected by no workers, while others may be selected by many workers. In this paper, we apply task bundling to address this issue, and we formulate a novel task bundling problem in WCMCS with the objective of maximizing the number of expected completed tasks. To solve this problem, we design an algorithm named LocTrajBundling which bundles tasks based on the location of tasks and the trajectories of workers. Experimental results show that, compared with other algorithms, our algorithm can achieve a better performance in maximizing the number of expected completed tasks.

Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity , which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accuracy compared with state-of-the-art methods.

In real-world recommendation scenarios, there are two common phenomena: 1) users only provide ratings but there is no review comment. As a result, the historical transaction data available for recommender system are usually unbalanced and sparse; 2) Users' opinions can be better grasped in their reviews than ratings. This indicates that there is always a bias between ratings and reviews. Therefore, it is important that users' ratings and reviews should be mutually reinforced to grasp the users' true opinions. To this end, in this paper, we develop an opinion mining model based on convolutional neural networks for enhancing recommendation (NeuO). Specifically, we exploit a two-step training neural networks, which utilize both reviews and ratings to grasp users' true opinions in unbalanced data. Moreover, we propose a Sentiment Classification scoring method (SC), which employs dual attention vectors to predict the users' sentiment scores of their reviews. A combination function is designed to use the results of SC and user-item rating matrix to catch the opinion bias. Finally, a Multilayer perceptron based Matrix Factorization (MMF) method is proposed to make recommendations with the enhanced user-item matrix. Extensive experiments on real-world data demonstrate that our approach can achieve a superior performance over state-of-the-art baselines on real-world datasets.