Tax code prediction is a crucial yet underexplored task in automating invoicing and compliance management for large-scale e-commerce platforms. Each product must be accurately mapped to a node within a multi-level taxonomic hierarchy defined by national standards, where errors lead to financial inconsistencies and regulatory risks. This paper presents Taxon, a semantically aligned and expert-guided framework for hierarchical tax code prediction. Taxon integrates (i) a feature-gating mixture-of-experts architecture that adaptively routes multi-modal features across taxonomy levels, and (ii) a semantic consistency model distilled from large language models acting as domain experts to verify alignment between product titles and official tax definitions. To address noisy supervision in real business records, we design a multi-source training pipeline that combines curated tax databases, invoice validation logs, and merchant registration data to provide both structural and semantic supervision. Extensive experiments on the proprietary TaxCode dataset and public benchmarks demonstrate that Taxon achieves state-of-the-art performance, outperforming strong baselines. Further, an additional full hierarchical paths reconstruction procedure significantly improves structural consistency, yielding the highest overall F1 scores. Taxon has been deployed in production within Alibaba's tax service system, handling an average of over 500,000 tax code queries per day and reaching peak volumes above five million requests during business event with improved accuracy, interpretability, and robustness.

Conversion objectives in large-scale recommender systems are sparse, making them difficult to optimize. Generative recommendation (GR) partially alleviates data sparsity by organizing multi-type behaviors into a unified token sequence with shared representations, but conversion signals remain insufficiently modeled. While recent behavior-aware GR models encode behavior types and employ behavior-aware attention to highlight decision-related intermediate behaviors, they still rely on standard attention over the full history and provide no additional supervision for conversions, leaving conversion sparsity largely unresolved. To address these challenges, we propose RCLRec, a reverse curriculum learning–based GR framework for sparse conversion supervision. For each conversion target, RCLRec constructs a short curriculum by selecting a subsequence of conversion-related items from the history in reverse. Their semantic tokens are fed to the decoder as a prefix, together with the target conversion tokens, under a joint generation objective. This design provides additional instance-specific intermediate supervision, alleviating conversion sparsity and focusing the model on the user's critical decision process. We further introduce a curriculum quality-aware loss to ensure that the selected curricula are informative for conversion prediction. Experiments on offline datasets and an online A/B test show that RCLRec achieves superior performance, with +2.09% advertising revenue and +1.86% orders in online deployment.

Transformer-based foundation models are becoming deeper and larger. For fast training, their billions of parameters (tensors) are split onto parallel tasks running on many modern yet expensive accelerators. To amortize the huge hardware investments, it is cost-effective to share the aggregated resources among multi-tenants. However, resource contention yields the heavy straggling problem. Existing works feature contributions for the traditional data parallelism. They cannot work well for the new tensor parallelism, due to the dependency among split tensors. This paper is the first attempt on accelerating heterogeneous tensor parallelism. We summarize specific challenges, including the very frequent synchronizations and the heavy tensor computation workloads. Our solution is to resize dimensions of parameters on demand, to quickly and dynamically balance workloads. The accuracy loss is reduced through priority resizing. We also migrate workloads between tasks, without any loss of accuracy. The most efficient communication primitives are selected and then scheduled in a non-redundant manner, to reduce the runtime latency. Our final hybrid solution is built on top of resizing and migration. By studying the tradeoff between accuracy and efficiency, it can smartly hit the “sweet spot”. Extensive experiments validate the effectiveness of our proposals.

In cloud computing, static resource configurations create a trade-off: tenants overprovision to avoid resource starvation, causing inefficiency and cost, while providers suffer low utilization despite high allocations. To improve efficiency, providers often use overcommitted environments where multiple workloads share hosts, but this leads to interference and potential Quality-of-Service (QoS) violations. This paper introduces a realtime dynamic control framework that mitigates interference by adaptively pinning workloads to CPU groups. Using deep reinforcement learning (DRL) with the Proximal Policy Optimization (PPO) algorithm, an intelligent agent continuously adjusts CPU pinning based on real-time feedback to maintain Service- Level-Agreement (SLA) compliance. Experiments under two optimization objectives—overall-performance-first and priorityperformance- first—show that the proposed approach improves overall QoS by$\approx$25% compared with static pinning. When prioritization is enabled, high-priority workloads gain significant performance improvements while lower-priority ones remain within SLA limits. These results demonstrate that a DRL-based CPU-pinning strategy effectively manages resource contention in overcommitted clouds, enhancing utilization while upholding tenant SLAs.

Graph partitioning as a classic NP-complete problem, is the most fundamental procedure that needs to be performed before parallel computations. Partitioners can be divided into vertex- and edge-based approaches. Recently, both approaches are employing a streaming heuristic to find approximate solutions. It is lightweight in space and time complexities, but suffers from suboptimal partitioning quality, especially for directed graphs where the explicit knowledge provided for heuristic is limited. This paper thereby proposes new heuristics for not only vertex-based but also edge-based partitioning. They improve quality by additionally utilizing implicit knowledge, which is embedded in the local streaming view and the global graph view. Memory reduction techniques are presented to extract this knowledge with negligible space costs. That preserves the lightweight advantages of streaming partitioning. Besides, we study parallel acceleration and restreaming, to further boost the partitioning efficiency and quality. Extensive experiments validate that our proposals outperform the state-of-the-art competitors.

Origin-Destination recommendation that recom-mends personalized origin city (O) and destination city (D) of flight itinerary is of great value for both Online Travel Platforms (OTPs) and users. Existing studies on next location recommendation propose to model the sequential regularity of users' check-in location sequences, but cannot well solve two new challenges facing OTPs, namely the necessity of exploring O&D and learning O&D as a whole. To this end, we propose a novel personalized Origin-Destination ranking NETwork (ODNET) for flight recommendation. In particular, a heterogeneous spatial graph (HSG) which models historical interactions between users and cities is designed at first. HSG is then deployed in ODNET to identify user preference Os and Ds by exploring the neighbor-hood information in HSG. To cope with the second challenge, the idea of multi-task learning is employed by ODNET to learn$O$and$D$jointly so as to capture their correlations. Moreover, temporal information of Os and Ds are also considered to further improve the accuracy of origin-destination recommendation. An offline experiment on multiple real-world datasets and an online A/B test both show the superiority of ODNET towards the state-of-the-art methods. Further, the implementation and deployment details of the proposed ODNET at Fliggy, one of the most popular OTPs in China, are also described. ODNET has now been successfully applied to provide high-quality flight recommendation service at Fliggy, serving tens of millions of users.

Nowadays, artificial neural networks are widely used for users’ online travel planning. Personalized travel planning has many real applications and is affected by various factors, such as transportation type, intention destination estimation, budget limit and crowdness prediction. Among those factors, users’ intention destination prediction is an essential task in online travel platforms. The reason is that, the user may be interested in the travel plan only when the plan matches his real intention destination. Therefore, in this paper, we focus on predicting users’ intention destinations in online travel platforms. In detail, we act as online travel platforms (such as Fliggy and Airbnb) to recommend travel plans for users, and the plan consists of various vacation items including hotel package, scenic packages and so on. Predicting the actual intention destination in travel planning is challenging. Firstly, users’ intention destination is highly related to their travel status (e.g., planning for a trip or finishing a trip). Secondly, users’ actions (e.g. clicking, searching) over different product types (e.g. train tickets, visa application) have different indications in destination prediction. Thirdly, users may mostly visit the travel platforms just before public holidays, and thus user behaviors in online travel platforms are more sparse, low-frequency and long-period. Therefore, we propose a Deep Multi-Sequences fused neural Networks (DMSN) to predict intention destinations from fused multi-behavior sequences. Real datasets are used to evaluate the performance of our proposed DMSN models. Experimental results indicate that the proposed DMSN models can achieve high intention destination prediction accuracy.

Matching items for a user from a travel item pool of large cardinality have been the most important technology for increasing the business at Fliggy, one of the most popular online travel platforms (OTPs) in China. There are three major challenges facing OTPs: sparsity, diversity, and implicitness. In this paper, we present a novel Fliggy ITinerary-aware deep matching NETwork (FitNET) to address these three challenges. FitNET is designed based on the popular deep matching network, which has been successfully employed in many industrial recommendation systems, due to its effectiveness. The concept itinerary is firstly proposed under the context of recommendation systems for OTPs, which is defined as the list of unconsumed orders of a user. All orders in a user itinerary are learned as a whole, based on which the implicit travel intention of each user can be more accurately inferred. To alleviate the sparsity problem, users’ profiles are incorporated into FitNET. Meanwhile, a series of itinerary-aware attention mechanisms that capture the vital interactions between user’s itinerary and other input categories are carefully designed. These mechanisms are very helpful in inferring a user’s travel intention or preference, and handling the diversity in a user’s need. Further, two training objectives, i.e., prediction accuracy of user’s travel intention and prediction accuracy of user’s click behavior, are utilized by FitNET, so that these two objectives can be optimized simultaneously. An offline experiment on Fliggy production dataset with over 0.27 million users and 1.55 million travel items, and an online A/B test both show that FitNET effectively learns users’ travel intentions, preferences, and diverse needs, based on their itineraries and gains superior performance compared with state-of-the-art methods. FitNET now has been successfully deployed at Fliggy, serving major online traffic.

Trajectory streams consist of large volumes of timestamped spatial data that are constantly generated from diverse and geographically distributed sources. Discovery of traveling patterns on trajectory streams such as gathering and companies needs to process each record when it arrives and correlates across multiple records near real-time. Thus techniques for handling high-speed trajectory streams should scale on distributed cluster computing. The main issues encapsulate three aspects, namely a data model to represent the continuous trajectory data, the parallelism of a discovery algorithm, and end-to-end performance improvement. In this paper, we propose a parallel discovery method that consists of 1) a model of partitioning trajectories sampled on different time intervals; 2) definition on distance measurements of trajectories; and 3) a parallel discovery algorithm. We develop this method in a stream processing workflow. From parallelization point of view, we investigate system performance, scalability, stability. Our method discovers trajectory gathering patterns with low latency and scales as the size of trajectory data grows.

Ad-hoc entity search, which is to retrieve a ranked list of relevant entities in response to a query of natural language question, has been widely studied. It has been shown that category matching of entities, especially when matching to fine-grained entity types/categories, is critical to the performance of entity search. However, the potentials of the fine-grained Wikipedia entity categories, has not been well exploited by existing studies. Based on the observation of how people describe entities of a specific type, we propose a headword-and-modifier model to deeply interpret both queries and fine-grained entity types/categories. Probabilistic generative models are designed to effectively estimate the relevance of headwords and modifiers as a pattern-based matching problem, taking the Wikipedia type taxonomy as an important input to address the ad-hoc representations of concepts/entities in queries. Extensive experimental results on three widely-used test sets: INEX-XER 2009, SemSearch-LS and TREC-Entity, show that our method achieves a significant improvement of the entity search performance over the state-of-the-art methods.

In this work, we propose efficient query workload partition techniques to reduce processing times of queries in parallel search engines. Existing methods cannot offer both high cache hit ratios and caching-aware load balance of the system. Aiming to solve this problem, we propose effective solutions to capture tradeoff between the cache hit ratio and load balance to reduce the total query processing time. The performance of the proposed algorithms are demonstrated by extensive experiments on real datasets, and the experimental results demonstrate that our algorithms have an efficiency improvement of up to at least 30% compared to extending current methods such as the roundrobin based algorithm and so on.

The advances in location-acquisition technologies have generated massive spatio-temporal trajectory data, which represent the mobility of a diversity of moving objects over time, such as people, vehicles, and animals. Discovery of traveling companions on trajectory data has many real-world applications. Most of existing discovery approaches are limited to centralized computing, while these techniques for handling large-scale trajectory data require considerable performance improvement. Parallel computing essentially provides an alternative method for handling this problem. In this work, we first present the design and implementation of both batch and streaming gathering patterns discovery algorithm in a distributed parallel computing fashion. Afterwards, we further propose several optimization techniques for efficient computation. Finally we conduct extensive experiments based on a public dataset to evaluate the efficiency of our approaches and effectiveness of optimizations using Amazon EC2 clusters.

Implementing trajectory data stream analysis in parallel has technical issues of data partition and improvements of the analysis operations. In this paper, we define the trajectory analysis problem as discovering trajectory companies of moving objects. We develop a discovery workflow in parallel batch processing. We solve technical issues of data partition and data locality in the steps of analysis operations. Our techniques focus on different partition methods, and observe the effects on the execution time and data locality by varying the operators of the workflow. We demonstrate our parallel implementation techniques using Apache Spark to process real GPS trajectory data on an Amazon Web Service cluster.

Commercial web search engines adopt parallel and replicated architecture in order to support high query throughput. In this paper, we investigate the effect of caching on the throughput in such a setting. A simple scheme, called uniform caching, would replicate the cache content to all servers. Unfortunately, it does not exploit the variations among queries, thus wasting memory space on caching the same cache content redundantly on multiple servers. To tackle this limitation, we propose a diversified caching problem, which aims to diversify the types of queries served by different servers, and maximize the sharing of terms among queries assigned to the same server. We show that it is NP-hard to find the optimal diversified caching scheme, and identify intuitive properties to seek good solutions. Then we present a framework with a suite of techniques and heuristics for diversified caching. Finally, we evaluate the proposed solution with competitors by using a real dataset and a real query log.

Nowadays, many applications return to the user a set of results that take the query as their nearest neighbor, which are commonly expressed through reverse nearest neighbor (RNN) queries. When considering moving objects, users would like to find objects that appear in the RNN result set for a period of time in some real-world applications such as collaboration recommendation and anti-tracking. In this work, we formally define the problem of interval reverse nearest neighbor (IRNN) queries over moving objects, which return the objects that maintain nearest neighboring relations to the moving query objects for the longest time in the given interval. Location uncertainty of moving data objects and moving query objects is inherent in various domains, and we investigate objects that exhibit Markov correlations, that is, each object's location is only correlated with its own location at previous timestamp while being independent of other objects. There exists the efficiency challenge for answering IRNN queries on uncertain moving objects with Markov correlations since we have to retrieve not only all the possible locations of each object at current time but also its historically possible locations. To speed up the query processing, we present a general framework for answering IRNN queries on uncertain moving objects with Markov correlations in two phases. In the first phase, we apply space pruning and probability pruning techniques, which reduce the search space significantly. In the second phase, we verify whether each unpruned object is an IRNN of the query object. During this phase, we propose an approach termed Probability Decomposition Verification (PDV) algorithm which avoid computing the probability of any object being an RNN of the query object exactly and thus improve the efficiency of verification. The performance of the proposed algorithm is demonstrated by extensive experiments on synthetic and real datasets, and the experimental results show that our algorithm is more efficient than the Monte-Carlo based approximate algorithm.

Given a set of spatial objects, facilities can influence the objects located within their influence regions that are represented by circular disks with the same radius $(r)$. Our task is to select the minimum number of locations such that establishing a temporary facility at each selected location would ensure that all the objects are influenced. Aiming to solve this location selection problem, we propose a novel kind of location selection query, called group location selection (GLS) queries. In many real-world applications, every object is usually located within an uncertainty region instead of at an exact point. Due to the uncertainty of the data, GLS processing needs to ensure that the probability of each uncertain object being influenced by one facility is not less than a given threshold $(\tau)$. An analysis of the time cost reveals that it is infeasible to exactly answer GLS queries over uncertain objects in polynomial time. Hence, this paper proposes an approximate query framework for answering queries efficiently while guaranteeing that the results of GLS queries are correct with a bounded probability. The performance of the proposed methods of the framework is demonstrated by theoretical analysis and extensive experiments with both real and synthetic data sets.

Complex event detection in stream is an important problem in event stream processing field. In this paper, we propose a new complex event detection algorithm in probabilistic stream, Instance Pruning and Filter-Detection Algorithm (IPF-DA). This algorithm is based on a kind of data structure called Chain Instance Queues (CIQ), to detect complex events satisfying query requirements with single-scanning probabilistic stream. In the process of complex event detection, IPF-DA prunes unnecessary event instances with query requirements and achieves filter for complex events with the given threshold. And it further improves the efficiency by setting proper tolerance, while insuring high recall. In addition, we construct Bayesian network to express and infer the probability distribution of uncertain events. Conditional Probability Indexing-Tree (CPI-Tree) is defined to store conditional probabilities of Bayesian network, saving query time compared with traditional Conditional Probability Table (CPT). Experimental results show that a series of strategies proposed by this paper are effective for complex event detection in probabilistic stream.