Despite rapid advances in multimodal large language models, agricultural applications remain constrained by the lack of multilingual speech data, unified multimodal architectures, and comprehensive evaluation benchmarks. To address these challenges, we present AgriGPT-Omni, an agricultural omni-framework that integrates speech, vision, and text in a unified framework.(1) First, we construct a scalable data synthesis and collection pipeline that converts agricultural texts and images into training data, resulting in the largest agricultural speech dataset to date, including 492K synthetic and 1.4K real speech samples across six languages.(2) Second, based on this, we train the first agricultural Omni-model via a three-stage paradigm: textual knowledge injection, progressive multimodal alignment, and GRPO-based reinforcement learning, enabling unified reasoning across languages and modalities.(3) We further propose AgriBench-Omni-2K, the first tri-modal benchmark for agriculture, covering diverse speech–vision–text tasks and multilingual slices, with standardized protocols and reproducible tools. Experiments show that AgriGPT-Omni significantly outperforms general-purpose baselines on multilingual and multimodal reasoning as well as real-world speech understanding.

Deep reinforcement learning (DRL), has shown promise in solving intractable challenges in interactive recommendation systems (IRS). In DRL-based interactive recommendation, state modeling is vital for well-capturing users’ continuous interaction behaviors with recommendation systems. To effectively capture the behavior of users, existing works for state modeling have evolved from sequential-based modeling to session-based modeling. However, existing session-based state modeling works in IRS are still not fully explored with premature session models and insufficient fusion for different session features. As a result, they cannot capture complicated session patterns during interaction, leading to significant information loss. In this article, we propose a Knowledge-enhanced Multi-Level Session Graph (KMSG) model for interactive recommendation to address the above challenge. KMSG models the user’s interactive data into multi-level session graphs and effectively encodes the states via graph neural networks. Specifically, a novel 3-level item transition graph is designed to capture the common session patterns and intra-session item transitions. We further utilize the information from the knowledge graph to enhance the item relations in KMSG. We then design an attention-based graph neural network to propagate the information in KMSG. Extensive experiments on four real-world benchmark datasets demonstrate the superiority of KMSG over state-of-the-art baselines and the rationality of our design in KMSG.

Recent studies have explored the integration of Large Language Models (LLMs) into recommender systems to enhance the semantic understanding of users and items. While traditional collaborative filtering approaches primarily rely on interaction histories, LLM-enhanced methods attempt to construct comprehensive profiles by leveraging descriptive metadata and user-generated reviews. The semantic representations of these profiles are then aligned with recommender embeddings to enhance the performance of recommender systems. However, the effectiveness of such approaches heavily depends on the quality of the generated profiles, which face several critical challenges: inaccurate profiles, insufficient information and information gap between semantic representations and recommender embeddings. To tackle these challenges, we propose a novel framework with reliable multi-profile alignment and collaborative-aware contrastive learning. Specifically, we introduce a profile generation method combining Chain-of-Thought(CoT) prompting and self-reflection to address the issue of inaccurate profiles. To alleviate the problem of insufficient information, we introduce an interactive profile construction mechanism that aggregates and summarizes common characteristics from users' and items' neighbors in the user-item graph. To bridge the information gap between semantic representations and recommender embeddings, we propose interactive information fusion(IIF), which aggregates semantic representations from neighbors and employs supervised contrastive learning to guide representation learning. Furthermore, we propose a multi-profile alignment framework that aligns recommender embeddings with both basic profiles and interactive profiles through deduplicated contrastive objectives, facilitating effective semantic-behavioral alignment. Extensive experiments on three public datasets and six base recommenders demonstrate that our method consistently outperforms strong LLM-based baselines, achieving an average improvement of 2.93% in Recall@20 and 2.64% in NDCG@20.

Massive multiplayer online role-playing games (MMORPG) have been becoming one of the most popular and exciting online games. In recent years, a cheating phenomenon called real money trading (RMT) has arisen and damaged the fantasy world in many ways. RMT is the sale of in-game items, currency, or even characters to earn real money, breaking the balance of the game economy ecosystem and damaging the game experience. Therefore, some studies have emerged to address the problem of RMT detection. However, they cannot well handle the label uncertainty problem in practice, where there are only labeled RMT samples (positive samples) and unlabeled samples, which could either be RMT samples or normal transactions (negative samples). Meanwhile, the trading relationship between RMTers is modeled in a simple way, leading to some normal transactions being falsely classified as RMT. In this article, we propose PU-Detector, a novel framework based on PU learning (learning from positive and unlabeled data) for RMT detection, considering the fact that there are only labeled RMT samples and other unlabeled transactions. We first automatically estimate the likelihood of one transaction being RMT by developing an improved PU learning method and proposing an assessment rule. Sequentially, we use the estimated likelihood as edge weight to construct a trading graph to learn trader representation. Then, with the trader representations and basic trading features, we detect RMT samples by the improved PU learning method. PU-Detector is evaluated on a large-scale real world dataset consisting of 33,809,956 transaction logs generated by 43,217 unique players. Compared with other approaches, it achieves the state-of-the-art performance and demonstrates its advantages in detecting underlying RMT samples.

Deep reinforcement learning (DRL), has shown promise in solving intractable challenges in interactive recommendation systems. In DRL-based interactive recommendation, state modeling is crucial for well-capturing users’ continuous interaction behaviors with shopping systems. A user’s multiple continuous interactions in a given time period (e.g., the time from login to log out) naturally constitute a session. However, existing studies often overlook such valuable session structure and characteristics and instead simply treat them as sequences. As a result, they are not able to capture the complex transitions over users’ interactions within or between sessions, leading to significant information loss. To bridge this significant gap, in this paper, we propose Session-based Interactive Recommendation with Graph Neural Networks (SIR-GNN). SIR-GNN models interaction data as sessions and employs novel graph neural networks to capture rich transition patterns among interactions. Specifically, a novel 3-level transition module is well designed to effectively capture common patterns from all sessions, intra-session transitions, and adjacent-item transitions respectively, followed by an attention-based gated graph neural network to model the state representation for SIR well. Extensive experiments on 3 real-world benchmark datasets demonstrate the superiority of SIR-GNN over state-of-the-art baselines and the rationality of our design in SIR-GNN.

Game bots are programmed to automatically play games and illegally obtain profit, seriously affecting game experience of honest players and breaking the balance of game ecosystem. Therefore, bot detection needs to be addressed urgently, especially for MMORPGs, one of the most rapidly expanding genres of games. There have been some studies for bot detection, but the features they used are dependent on specific games and the methods cannot be generalized to other games. In this paper, we propose a trajectory based pre-trained model for game bot detection from game character trajectories and mouse trajectories, named T-Detector, which is independent to specific games and can be generalized to others. More specifically, we propose a pretrain method of LocationTime2Vec to learn representations of trajectories from huge unlabeled samples, which deeply embed spatial and temporal information hidden in trajectories. Moreover, we extract universal features based on behavioral differences in movement trajectories between human players and bots. We design an Angle Pretrain to extract features of turning angle, and propose an attention pooling module to extract features of moving speed and distance. Such features are not dependent on any specific game, enabling T-Detector to be generalized to many MMORPGs. Evaluated by two large-scale real-world datasets of 143,938 samples from two MMORPGs, T-Detector achieves the state-of-the-art performance in bot detection, and demonstrates powerful generalization ability.

Enabled by smartphones, social media users are increasingly going mobile. This trend fosters various location based services on social media platforms (e.g., Twitter). Many services like friends notification and community detection benefit from co-location judgement, i.e., to decide whether two Twitter users are co-located in some point-of-interest (POI). This problem is challenging due to the limited information in tweets and the lack of explicit geo-tags in tweets that can be used as labeled data. Our approach to this problem is based on a novel concept of HisRect features extracted from users' historical visits and recent tweets: The former has impacts on where a user visits in general, whereas the latter gives more hints about where a user is currently. In practice, labeled data is scarce. Therefore, we design a semi-supervised learning (SSL) framework that leverages unlabeled data to extract HisRect features. Moreover, we employ an embedding neural network layer to process HisRect features of two users, which decides co-location based on the embedding difference between the two features. Our model is extensively evaluated on two large sets of real Twitter data from more than one million users. The experimental results demonstrate that our HisRect features and SSL framework are highly effective at deciding co-locations. In terms of multiple metrics, our approach clearly outperforms alternative approaches using state-of-the-art techniques.

This study explores the problem of co-location judgement, i.e., to decide whether two Twitter users are co-located at some point-of-interest (POI). We extract novel features, named HisRect, from users' historical visits and recent tweets: The former has impact on where a user visits in general, whereas the latter gives more hints about where a user is currently. To alleviate the issue of data scarcity, a semi-supervised learning (SSL) framework is designed to extract HisRect features. Moreover, we use an embedding neural network layer to decide co-location based on the difference between two users' His-Rect features. Extensive experiments on real Twitter data suggest that our HisRect features and SSL framework are highly effective at deciding co-locations.

Forecasting price trend of bulk commodities is important in international trade, not only for markets participants to schedule production and marketing plans but also for government administrators to adjust policies. Previous studies cannot support accurate fine-grained short-term prediction, since they mainly focus on coarse-grained long-term prediction using historical data. Recently, cross-domain open data provides possibilities to conduct fine-grained price forecasting, since they can be leveraged to extract various direct and indirect factors of the price. In this article, we predict the price trend over upcoming days, by leveraging cross-domain open data fusion. More specifically, we formulate the price trend into three classes (rise, slight-change, and fall), and then we predict the specific class in which the price trend of the future day lies. We take three factors into consideration: (1) supply factor considering sources providing bulk commodities,<?brk?> (2) demand factor focusing on vessel transportation with reflection of short time needs, and (3) expectation factor encompassing indirect features (e.g., air quality) with latent influences. A hybrid classification framework is proposed for the price trend forecasting. Evaluation conducted on nine real-world cross-domain open datasets shows that our framework can forecast the price trend accurately, outperforming multiple state-of-the-art baselines.

App usage prediction, i.e. which apps will be used next, is very useful for smartphone system optimization, such as operating system resource management, battery energy consumption optimization, and user experience improvement as well. However, it is still challenging to achieve usage prediction of high accuracy. In this paper, we propose a novel framework for app usage prediction, called AppUsage2Vec, inspired by Doc2Vec. It models app usage records by considering the contribution of different apps, user personalized characteristics, and temporal context. We measure the contribution of each app to the target app by introducing an app-attention mechanism. The user personalized characteristics in app usage are learned by a module of dual-DNN. Furthermore, we encode the top-k supervised information in loss function for training the model to predict the app most likely to be used next. The AppUsage2Vec was evaluated on a dataset of 10,360 users and 46,434,380 records in three months. The results demonstrate the state-of-the-art performance.

The fast pace of global urbanization is drastically changing the population distributions over the world, which leads to significant changes in geographical population densities. Such changes in turn alter the underlying geographical power demand over time, and drive power substations to become over-supplied (demand << capacity) or under-supplied (demand ≈ capacity). In this paper, we make the first attempt to investigate the problem of power substation-user assignment by analyzing large-scale power grid data. We develop a Scalable Power User Assignment (SPUA) framework, that takes large-scale spatial power user/substation distribution data and temporal user power consumption data as input, and assigns users to substations, in a manner that minimizes the maximum substation utilization among all substations. To evaluate the performance of our SPUA framework, we conduct evaluations on real power consumption data and user/substation location data collected from a province in China for 35 days in 2015. The evaluation results demonstrate that our SPUA framework can achieve a 20%--65% reduction on the maximum substation utilization, and 2 to 3.7 times reduction on total transmission loss over other baseline methods.

A social event is an occurrence that involves lots of people and is accompanied by an obvious rise in human flow. Analysis of social events has real-world importance because events bring about impacts on many aspects of city life. Traditionally, detection and impact measurement of social events rely on social investigation, which involves considerable human effort. Recently, by analyzing messages in social networks, researchers can also detect and evaluate country-scale events. Nevertheless, the analysis of city-scale events has not been explored. In this article, we use human flow dynamics, which reflect the social activeness of a region, to detect social events and measure their impacts. We first extract human flow dynamics from taxi traces. Second, we propose a method that can not only discover the happening time and venue of events from abnormal social activeness, but also measure the scale of events through changes in such activeness. Third, we extract traffic congestion information from traces and use its change during social events to measure their impact. The results of experiments validate the effectiveness of both the event detection and impact measurement methods.