In recent years, the recommendation content on e-commerce platforms has become increasingly rich -- a single user feed may contain multiple entities, such as selling products, short videos, and content posts. To deal with the multi-entity cross-domain recommendation problem, an intuitive solution is to adopt the shared-network-based architecture for joint training. The underlying idea is to transfer the knowledge from one type of entity (source entity) to another (target entity). However, different from the conventional same-entity cross-domain recommendation, multi-entity knowledge transfer encounters several important challenges: (1) data distributions of the source entity and target entity are naturally different, making the shared-network-based joint training susceptible to the negative transfer issue(2) the corresponding feature schema of each entity is not exactly aligned (e.g., price is an essential feature for selling product while missing for content posts), making the existing approaches no longer appropriate. Recent researchers have also experimented with the pre-training and fine-tuning paradigm. Again, they only take into account the scenarios with the same entity type and feature schemas, which is inappropriate in our case. To this end, we design a pre-training & fine-tuning based Multi-entity Knowledge Transfer framework called MKT. MKT utilizes a multi-entity pre-training module to extract transferable knowledge across different entities. In particular, a feature alignment module is first applied to scale and align different feature schemas. Afterward, a couple of knowledge extractors are employed to extract the common and independent knowledge. In the end, the extracted common knowledge is adopted for target entity model training. Through extensive offline and online experiments on public and industrial datasets, we demonstrated the superiority of MKT over multiple State-Of-The-Art methods. MKT has also been deployed for content post recommendations on our production system.

The chemical industry is faced with the urgent challenge of effectively harnessing the vast amounts of time-series data generated by thousands of sensors, which is essential for forecasting chemical states, achieving accurate real-time control of production processes. Traditional forecasting methods suffer from high computational latency and struggle with the complexity of spatiotemporal dependencies. As a result, modeling this data becomes challenging. This paper introduces a novel approach, referred to as ASTNet, designed to address these challenges. ASTNet integrates an asynchronous spatiotemporal modeling framework that combines temporal and spatial encoders, enabling concurrent learning of temporal and spatial dependencies while reducing computational latency. Additionally, it introduces a gated graph fusion mechanism that adaptively combines static (meta) and evolving (dynamic) sensor graphs, enhancing the handling of heterogeneous sensor data and spatial correlations. Extensive experiments on three real-world chemical sensor datasets demonstrate that ASTNet outperforms SOTA methods in terms of both prediction accuracy and computational efficiency, making ASTNet successfully deployed in chemical engineering industrial scenarios.

Most recommendation systems typically follow a product-based paradigm utilizing user-product interactions to identify the most engaging items for users. However, this product-based paradigm has notable drawbacks for Xianyu~. Xianyu is China's largest online C2C e-commerce platform where a large portion of the product are post by individual sellers. Most of the product on Xianyu posted from individual sellers often have limited stock available for distribution, and once the product is sold, it's no longer available for distribution. This result in most items distributed product on Xianyu having relatively few interactions, affecting the effectiveness of traditional recommendation depending on accumulating user-item interactions. To address these issues, we introduce IU4Rec, an Interest Unit-based two-stage Recommendation system framework. We first group products into clusters based on attributes such as category, image, and semantics. These IUs are then integrated into the Recommendation system, delivering both product and technological innovations. IU4Rec begins by grouping products into clusters based on attributes such as category, image, and semantics, forming Interest Units (IUs). Then we redesign the recommendation process into two stages. In the first stage, the focus is on recommend these Interest Units, capturing broad-level interests. In the second stage, it guides users to find the best option among similar products within the selected Interest Unit. User-IU interactions are incorporated into our ranking models, offering the advantage of more persistent IU behaviors compared to item-specific interactions. This interest unit based recommendation can be beneficial from mitigating the side effect of limited-stock problem, since most interaction can be gathered on interest units which can persist and accumulate over time. Experimental results on the production dataset and online A/B testing demonstrate the effectiveness and superiority of our proposed IU-centric recommendation approach. This study not only advances recommendation technologies but also emphasizes the potential for co-evolution between product innovations and the technologies involved in item supply and distribution.

Abstract When using retrieval‐augmented generation (RAG) to handle multi‐document question answering (MDQA) tasks, it is beneficial to decompose complex queries into multiple simpler ones to enhance retrieval results. However, previous strategies always employ a one‐shot approach of question decomposition, overlooking subquestions dependency problem and failing to ensure that the derived subqueries are single‐hop. To overcome this challenge, we introduce a novel framework called DSRC‐QCS. Decompose‐solve‐renewal‐cycle (DSRC) is an iterative multi‐hop question processing module. The key idea of DSRC involves using a unique symbol to achieve hierarchical dependency management and employing a cyclical process of question decomposition, solving, and renewal to continuously generate and resolve all single‐hop subquestions. Query‐chain selector (QCS) functions as a voting mechanism that effectively utilizes the reasoning process of DSRC to assess and select solutions. We compare DSRC‐QCS against five RAG approaches across three datasets and three LLMs. DSRC‐QCS demonstrates superior performance. Compared to the Direct Retrieval method, DSRC‐QCS improves the average F1 score by 17.36% with Alpaca‐7b, 10.83% with LLaMa2‐Chat‐7b, and 11.88% with GPT‐3.5‐Turbo. We also conduct ablation studies to validate the performance of both DSRC and QCS and explore factors influencing the effectiveness of DSRC. We have included all prompts in the Appendix.

Modern datacenter applications are increasingly being built using a microservices architecture. These microservices communicate with each other using datacenter RPCs. RPC's pass by value semantics incur redundant data movement along the network, especially for data-intensive applications. Naively introducing a shared global address space to datacenter RPC does not work as it would couple microservices and require microservices to handle data consistency, significantly complicating the development and deployment of applications. Fortunately, the modern datacenter is embracing disaggregated memory (DM). In a DM-enabled datacenter, servers running the microservices can be all connected to one global disaggregated memory pool, thus the pass by value semantics can be replaced by pass by reference. However, prior work on DM requires complicated synchronization primitives to share data across physical machines, so naively adopting them to datacenter RPC would harm microservices' agility and modularity. To this end, we present DmRPC, a DM-aware datacenter RPC for data-intensive datacenter applications to our knowledge. First, DmRPC introduces a DM-aware shared global address space to provide the semantics of pass by reference to datacenter RPC, thus alleviating the redundant data movement issue. Second, DmRPC adopts a copy-on-write mechanism to avoid complicating application logic to handle data consistency while guaranteeing high performance. We have applied DmRPC to two different implementations of DM, one is network-based (DmRPC-net) while the other is CXL-based (DmRPC-CXL). Our evaluations on synthetic 7-tier microservices workloads show that DmRPC-net (or DmRPC-CXL) achieves 4.2× (or 8.3×) higher throughput and achieves 1.1 × (or 1.7 ×) lower average latency than that of the baseline, respectively. On a widely used microservice benchmark DeathStarBench, DmRPC-net can achieve 3.1 × higher throughput and 2.5 × lower average latency than the baseline.

In multilingual communities, code-switching is a common phenomenon and code-switched tasks have become a crucial area of research in natural language processing (NLP) applications. Existing approaches mainly focus on supervised learning. However, it is expensive to annotate a sufficient amount of code-switched data. In this paper, we consider zero-shot setting and improve model performance on code-switched tasks via monolingual language datasets, unlabeled code-switched datasets, and semantic dictionaries. Inspired by the mechanism of code-switching itself, we propose multi-label masked language modeling and predict both the masked word and its synonyms in other languages. Experimental results show that compared with baselines, our method can further improve the pretrained multilingual model's performance on code-switched sentiment analysis datasets.

In traditional Chinese medicine (TCM), prescriptions are the daughters of doctors' clinical experiences, which have been the main way to cure diseases in China for several thousand years. In the long Chinese history, a large number of prescriptions have been invented based on TCM theories. Regularities in the prescriptions are important for both clinical practice and novel prescription development. Previous works used many methods to discover regularities in prescriptions, but rarely described how a prescription is generated using TCM theories. In this work, we propose a topic model which characterizes the generative process of prescriptions in TCM theories and further incorporate domain knowledge into the topic model. Using 33,765 prescriptions in TCM prescription books, the model can reflect the prescribing patterns in TCM. Our method can outperform several previous topic models and group recommendation methods on generalization performance, herbs recommendation, symptoms suggestion, and prescribing patterns discovery.

During the last decade, community-based question answering (CQA) sites have accumulated a vast amount of questions and their crowdsourced answers over time. How to efficiently identify the quality of answers that are relevant to a given question has become an active line of research in CQA. The major challenge of CQA is the accurate selection of high-quality answers w.r.t given questions. Previous approaches tend to model the semantic matching between individual pair of one question and its corresponding answer (how fitting an answer is to a posted question). However, these works ignore the temporal interactions between answers (how previous answers influence the late posted answers). For example, a rational user likely adapts others' opinions, revises his inclinations, and posts a more appropriate answer after understanding the given question and previously posted answers. As a result, this paper devises an architecture named Temporal Interaction and Causal Influence LSTM (TC-LSTM) to effectively leverage not only the causal influence between question-answer (how appropriate an answer is for a given question) but also the temporal interactions between answers-answer (how a high-quality answer gradually forms). In particular, long short-term memory (LSTM) is used to capture the explicit question-answer influence and the implicit answers-answer interactions. Experiments are conducted on SemEval 2015 CQA dataset for answer classification task and Baidu Zhidao Dataset for answer ranking task. The experimental results show the advantage of our model comparing with other state-of-the-art methods.

We propose selective supervised Latent Dirichlet Allocation (ssLDA) to boost the prediction performance of the widely studied supervised probabilistic topic models. We introduce a Bernoulli distribution for each word in one given document to selectthis word as a strongly or weakly discriminative one with respect to its assigned topic. The Bernoulli distribution is parameterized by the discrimination power of the word for its assigned topic. As a result, the document is represented as a “bag-of-selective-words” instead of the probabilistic “bag-of-topics” in the topic modeling domain or the flat “bag-of-words” in the traditional natural language processing domain to form a new perspective. Inheriting the general framework of supervised LDA (sLDA), ssLDA can also predict many types of response specified by a Gaussian Linear Model (GLM). Focusing on the utilization of this word selection mechanism for singe-label document classification in this paper, we conduct the variational inference for approximating the intractable posterior and derive a maximum-likelihood estimation of parameters in ssLDA. The experiments reported on textual documents show that ssLDA not only performs competitively over “state-of-the-art” classification approaches based on both the flat “bag-of-words” and probabilistic “bag-of-topics” representation in terms of classification performance, but also has the ability to discover the discrimination power of the words specified in the topics (compatible with our rational knowledge).

Hashing techniques have been extensively investigated to boost similarity search for large-scale high-dimensional data. Most of the existing approaches formulate the their objective as a pair-wise similarity-preserving problem. In this paper, we consider the hashing problem from the perspective of optimizing a list-wise learning to rank problem and propose an approach called List-Wise supervised Hashing (LWH). In LWH, the hash functions are optimized by employing structural SVM in order to explicitly minimize the ranking loss of the whole list-wise permutations instead of merely the point-wise or pair-wise supervision. We evaluate the performance of LWH on two real-world data sets. Experimental results demonstrate that our method obtains a significant improvement over the state-of-the-art hashing approaches due to both structural large margin and list-wise ranking pursuing in a supervised manner.

Recently, there has been extensive research towards the user-provided tags on photo sharing websites which can greatly facilitate image retrieval and management. However, due to the arbitrariness of the tagging activities, these tags are often imprecise and incomplete. As a result, quite a few technologies has been proposed to improve the user experience on these photo sharing systems, including tag clustering and refinement, etc. In this work, we propose a novel framework to model the relationships among tags and images which can be applied to many tag based applications. Different from previous approaches which model images and tags as heterogeneous objects, images and their tags are uniformly viewed as compositions of Semantic Unities in our framework. Then Semantic Unity Graph (SUG) is introduced to represent the complex and high-order relationships among these Semantic Unities. Based on the representation of Semantic Unity Graph, the relevance of images and tags can be naturally measured in terms of the similarity of their Semantic Unities. Then Tag clustering and refinement can then be performed on SUG and the polysemy of images and tags is explicitly considered in this framework. The experiment results conducted on NUS-WIDE and MIR-Flickr datasets demonstrate the effectiveness and efficiency of the proposed approach.