Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-behavior Recommendation with Graph Convolutional Networks

Traditional recommendation models that usually utilize only one type of user-item interaction are faced with serious data sparsity or cold start issues. Multi-behavior recommendation taking use of multiple types of user-item interactions, such as clicks and favorites, can serve as an effective solution. Early efforts towards multi-behavior recommendation fail to capture behaviors' different influence strength on target behavior. They also ignore behaviors' semantics which is implied in multi-behavior data. Both of these two limitations make the data not fully exploited for improving the recommendation performance on the target behavior. In this work, we approach this problem by innovatively constructing a unified graph to represent multi-behavior data and proposing a new model named MBGCN (short for Multi-Behavior Graph Convolutional Network ). Learning behavior strength by user-item propagation layer and capturing behavior semantics by item-item propagation layer, MBGCN can well address the limitations of existing works. Empirical results on two real-world datasets verify the effectiveness of our model in exploiting multi-behavior data. Our model outperforms the best baseline by 25.02% and 6.51% averagely on two datasets. Further studies on cold-start users confirm the practicability of our proposed model.
Web Table Retrieval using Multimodal Deep Learning

We address the web table retrieval task, aiming to retrieve and rank web tables as whole answers to a given information need. To this end, we formally define web tables as multimodal objects. We then suggest a neural ranking model, termed MTR, which makes a novel use of Gated Multimodal Units (GMUs) to learn a joint-representation of the query and the different table modalities. We further enhance this model with a co-learning approach which utilizes automatically learned query-independent and query-dependent "helper'' labels. We evaluate the proposed solution using both ad hoc queries (WikiTables) and natural language questions (GNQtables). Overall, we demonstrate that our approach surpasses the performance of previously studied state-of-the-art baselines.
Product Bundle Identification using Semi-Supervised Learning

Many sellers on e-commerce platforms offer buyers product bundles, which package together two or more different items. The identification of such bundles is a necessary step to support a variety of related services, from recommendation to dynamic pricing. In this work, we present a comprehensive study of bundle identification on a large e-commerce website. Our analysis of bundle compared to non-bundle listed items reveals several key differentiating characteristics, spanning the listing's title, image, and attributes. Following, we experiment with a multi-modal classifier, which takes advantage of these characteristics as features. Our analysis also shows that a bundle indicator input by sellers tends to be highly noisy and carries only a weak signal. The bundle identification task therefore faces the challenge of having a small set of manually-labeled clean examples and a larger set of noisy-labeled examples, in conjunction with class imbalance due to the relative scarcity of bundles. Our experiments with basic supervised classifiers, using the manually-labeled and/or the noisy-labeled data for training, demonstrates only moderate performance. We therefore turn to a semisupervised approach and propose GREED, a self-training ensemblebased algorithm with a greedy model selection. Our evaluation over two different meta-categories shows a superior performance of semi-supervised approaches for the bundle identification task, with GREED outperforming several semi-supervised alternatives. The combination of textual, image, and some metadata features is shown to yield the best performance, reaching an AUC of 0.89 and 0.92 for the two meta-categories, respectively
Experimaestro and Datamaestro: Experiment and Dataset Managers (for IR)

Abstract Ensuring reproducibility is key to all scientific domains. As Information Retrieval (IR) experiments are often composed of several steps that can be shared between tested models, and rely on various resources, it is difficult to keep track of all the experimental settings and to ensure experiments can be reproduced easily. In this demo paper, we present two managers, Experimaestro and Datamaestro, and their add-ons for IR, designed to help to define and run experimental plans.
Estratégia para fase de Merge utilizando agrupamento local

The processing of great quantities of data is a subject extensively discussed by the scientific community. Models like PMDE and Mapreduce process those data in phases, which involve execution, data exchanging and grouping. The Merge phase is responsible for grouping values associated with the same key. In this work, we propose to divide this phase in two steps: tree size reduction grouping pairs with local merge and merge of pairs received from other machines. The strategy will be integrated to the PMDE execution environment. We intend to obtain performance for specific application using this strategy, decreasing data traffic and reducing memory use. Resumo. O processamento de aplicações de grandes quantidades de dados é um tema amplamente discutido pela comunidade cientı́fica atualmente. Modelos como PMDE e Mapreduce processam tais dados em fases, que envolvem execução, trocas de dados e agrupamento. A fase de Merge é responsável por agrupar valores associados a uma mesma chave. Neste trabalho, propomos dividir tal fase em duas etapas: redução do tamanho da árvore utilizando agrupamento de pares com merge local e merge de pares recebidos de outras máquinas. A estratégia será aplicada ao ambiente de execução do modelo PMDE. Pretende-se obter um aumento de desempenho para determinadas aplicações utilizando esta estratégia, por meio da redução no tráfego de dados e diminuição do uso de memória.
Global Context Enhanced Graph Neural Networks for Session-based Recommendation

Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Almost all the existing solutions for SBR model user preference only based on the current session without exploiting the other sessions, which may contain both relevant and irrelevant item-transitions to the current session. This paper proposes a novel approach, called Global Context Enhanced Graph Neural Networks (GCE-GNN) to exploit item transitions over all sessions in a more subtle manner for better inferring the user preference of the current session. Specifically, GCE-GNN learns two levels of item embeddings from session graph and global graph, respectively: (i) Session graph, which is to learn the session-level item embedding by modeling pairwise item-transitions within the current session; and (ii) Global graph, which is to learn the global-level item embedding by modeling pairwise item-transitions over all sessions. In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph. We also design a session-level item representation learning layer, which employs a GNN on the session graph to learn session-level item embeddings within the current session. Moreover, GCE-GNN aggregates the learnt item representations in the two levels with a soft attention mechanism. Experiments on three benchmark datasets demonstrate that GCE-GNN outperforms the state-of-the-art methods consistently.
Nonintrusive-Sensing and Reinforcement-Learning Based Adaptive Personalized Music Recommendation

As a particularly prominent application of recommender systems on automated personalized service, the music recommendation has been widely used in various music network platforms, music education and music therapy. Importantly, the individual music preference for a certain moment is closely related to personal experience of the music and music literacy, as well as temporal scenario without any interruption. Therefore, this paper proposes a novel policy for music recommendation NRRS (Nonintrusive-Sensing and Reinforcement-Learning based Recommender Systems) by integrating prior research streams. Specifically, we develop a novel recommendation framework for sensing, learning and adaptation to user's current preference based on wireless sensing and reinforcement learning in real time during a listening session. The established music recommendation prototype monitors individual vital signals for listening music, and captures song characters, individual dynamic preferences, and that it can yield better listening experience for users.
Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

Capturing users' precise preferences is of great importance in various recommender systems (e.g., e-commerce platforms and online advertising sites), which is the basis of how to present personalized interesting product lists to individual users. In spite of significant progress has been made to consider relations between users and items, most of existing recommendation techniques solely focus on singular type of user-item interactions. However, user-item interactive behavior is often exhibited with multi-type (e.g., page view, add-to-favorite and purchase) and inter-dependent in nature. The overlook of multiplex behavior relations can hardly recognize the multi-modal contextual signals across different types of interactions, which limit the feasibility of current recommendation methods. To tackle the above challenge, this work proposes a Memory-Augmented Transformer Networks (MATN), to enable the recommendation with multiplex behavioral relational information, and joint modeling of type-specific behavioral context and type-wise behavior inter-dependencies, in a fully automatic manner. In our MATN framework, we first develop a transformer-based multi-behavior relation encoder, to make the learned interaction representations be reflective of the cross-type behavior relations. Furthermore, a memory attention network is proposed to supercharge MATN capturing the contextual signals of different types of behavior into the category-specific latent embedding space. Finally, a cross-behavior aggregation component is introduced to promote the comprehensive collaboration across type-aware interaction behavior representations, and discriminate their inherent contributions in assisting recommendations. Extensive experiments on two benchmark datasets and a real-world e-commence user behavior data demonstrate significant improvements obtained by MATN over baselines. Codes are available at: https://github.com/akaxlh/MATN.
ASiNE: Adversarial Signed Network Embedding

Motivated by a success of generative adversarial networks (GAN) in various domains including information retrieval, we propose a novel signed network embedding framework, ASiNE, which represents each node of a given signed network as a low-dimensional vector based on the adversarial learning. To do this, we first design a generator G+ and a discriminator D+ that consider positive edges, as well as a generator G - and a discriminator D- that consider negative edges: (1) G+/G- aim to generate the most indistinguishable fake positive/negative edges, respectsupively; (2) D+/D aim to discriminate between real positive/negative edges and fake positive/negative edges, respectively. Furthermore, under ASiNE, we propose two new strategies for effective signed network embedding: (1) an embedding space sharing strategy for learning both positive and negative edges; (2) a fake edge generation strategy based on the balance theory. Through extensive experiments using five real-life signed networks, we verify the effectiveness of each of the strategies employed in ASiNE. We also show that ASiNE consistently and significantly outperforms all the state-of-the-art signed network embedding methods in all datasets and with all metrics in terms of accuracy of sign prediction.
How Deep Learning Works for Information Retrieval

Information retrieval (IR) is the science of search, the search of user query relevant pieces of information from a collection of unstructured resources. Information in this context includes text, imagery, audio, video, xml, program, and metadata. The journey of an IR process begins with a user query sent to the IR system which encodes the query, compares the query with the available resources, and returns the most relevant pieces of information. Thus, the system is equipped with the ability to store, retrieve and maintain information. In the early era of IR, the whole process was completed using handcrafted features and ad-hoc relevance measures. Later, principled frameworks for relevance measure were developed with statistical learning as a basis. Recently, deep learning has proven essential to the introduction of more opportunities to IR. This is because data-driven features combined with data-driven relevance measures can effectively eliminate the human bias in either feature or relevance measure design. Deep learning has shown its significant potential to transform IR evidenced by abundant empirical results. However, we continue to strive to gain a comprehensive understanding of deep learning. This is done by answering questions such as why deep structures are superior to shallow structures, how skip connections affect a model's performance, uncovering the potential relationship between some of the hyper-parameters and a model's performance, and exploring ways to reduce the chance for deep models to be fooled by adversaries. Answering such questions can help design more effective deep models and devise more efficient schemes for model training.
Chromosome evolution and speciation in rodents

Chromosomal differences are frequently associated with taxonomic differences at the species level. It has been assumed that this association in large taxonomic groups, such as the Order Rodentia, means that karyotypic divergence is causally involved both as part of population differentiation (Le. speciation) and as an adaptive component in phyletic evolution (21, 28, 147, 149). There are two proximate effects of chromosomal rearrangements that are generally in· voked to explain these observed associations, which are: (a) the meiotic effects of structural heterozygosity and the establishment of reproductive isolation; or (b) the presumed position·effect alteration of gene function and the creation of cytologically defined, "coadapted" gene complexes (e.g. 41). These two alternative views look at the effects of different components of fitness. The first focuses on fertility and/or fecundity in relation to the meiotic effects of rearranged chromosomes, and the second emphasizes phenotypic adaptation (i.e. temporal survivorship). Both classes of effects are intuitively appealing, and as a result, both are now commonly assumed in discussions of the role of chromosome change in speciation and adaptive evolution. However, these assumptions need to be critically reexamined.
BIRDS - Bridging the Gap between Information Science, Information Retrieval and Data Science

The BIRDS workshop aimed to foster the cross-fertilization of Information Science (IS), Information Retrieval (IR) and Data Science (DS). Recognising the commonalities and differences between these communities, the proposed full-day workshop brought together experts and researchers in IS, IR and DS to discuss how they can learn from each other to provide more user-driven data and infor- mation exploration and retrieval solutions. Therefore, the papers aimed to convey ideas on how to utilise, for instance, IS concepts and theories in DS and IR or DS approaches to support users in data and information exploration.
Identifying Tasks from Mobile App Usage Patterns

Mobile devices have become an increasingly ubiquitous part of our everyday life. We use mobile services to perform a broad range of tasks (e.g. booking travel or office work), leading to often lengthy interactions within distinct apps and services. Existing mobile systems handle mostly simple user needs, where a single app is taken as the unit of interaction. To understand users' expectations and to provide context-aware services, it is important to model users' interactions in the task space. In this work, we first propose and evaluate a method for the automated segmentation of users' app usage logs into task units. We focus on two problems: (i) given a sequential pair of app usage logs, identify if there exists a task boundary, and (ii) given any pair of two app usage logs, identify if they belong to the same task. We model these as classification problems that use features from three aspects of app usage patterns: temporal, similarity, and log sequence. Our classifiers improve on traditional timeout segmentation, achieving over 89% performance for both problems. Secondly, we use our best task classifier on a large-scale data set of commercial mobile app usage logs to identify common tasks. We observe that users' performed common tasks ranging from regular information checking to entertainment and booking dinner. Our proposed task identification approach provides the means to evaluate mobile services and applications with respect to task completion.
Spatial Object Recommendation with Hints: When Spatial Granularity Matters

Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularity and thereby are heterogeneous. For example, one user may prefer to be recommended a region (say Manhattan), while another user might prefer a venue (say a restaurant). Even for the same user, preferences can change at different stages of data exploration. In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI). To solve this problem, we propose the use of a POI tree, which captures spatial containment relationships between POIs. We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level. Each task consists of two subtasks: (i) attribute-based representation learning; (ii) interaction-based representation learning. The first subtask learns the feature representations for both users and POIs, capturing attributes directly from their profiles. The second subtask incorporates user-POI interactions into the model. Additionally, MPR can provide insights into why certain recommendations are being made to a user based on three types of hints: user-aspect, POI-aspect, and interaction-aspect. We empirically validate our approach using two real-life datasets, and show promising performance improvements over several state-of-the-art methods.
AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction

Modelling feature interactions is key in Click-Through Rate (CTR) predictions. State-of-the-art models usually include explicit feature interactions to better model non-linearity in a deep network, but enumerating all feature combinations of high orders is not efficient and brings challenges to network optimization. In this work, we use AutoML to seek useful high-order feature interactions to train on without manual feature selection. For this purpose, an end-to-end model, AutoGroup, is proposed, which casts the selection of feature interactions as a structural optimization problem. In a nutshell, AutoGroup first automatically groups useful features into a number of feature sets. Then, it generates interactions of any order from these feature sets using a novel interaction function. The main contribution of AutoGroup is that it performs both dimensionality reduction and feature selection which are not seen in previous models. Offline experiments on three public large-scale benchmark datasets demonstrate the superior performance and efficiency of AutoGroup over state-of-the-art models. Furthermore, a ten-day online A/B test verifies that AutoGroup can be reliably deployed in production and outperform the commercial baseline by 10% on average in terms of CTR and CVR.
Relationship between Fetuin-A and vascular or valvular calcification in hemodialysis patients

Methods: The study was conducted on 122 MHD patients. Serum levels of calcium, phosphorus, parathormone, and Fetuin-A were tested. Intima-media thickness (IMT) ≥0.8 mm and the presence of stenosis>50% or plaques in carotid doppler sonography were considered as vascular calcifi cation. Calcifi cation of cardiac valves or mitral annular calcifi cation in twodimensional echocardiography, were considered as cardiac valvular calcifi cation. The presence of any or both of the two conditions was defi ned as cardiovascular calcifi cation (CVC).
Risk factors for suicidal ideation among the older people living alone in rural region of China

Abstract In China, suicide accounts for twenty-six percent of all suicides worldwide; however, researches on the suicidal ideation among older people living alone in the rural region are few. We performed a cross-sectional study to explore the prevalence and influence factors of suicidal ideation, and provide a theoretical basis for suicide prevention among older people living alone in rural region. 695 older people living alone in rural region were selected by using stratified cluster sampling. Chi-square for categorical variables, T-test for continuous variables, and path analysis were conducted to statistical analysis. The prevalence of suicidal ideation among the elderly living alone in rural China was 23.6%. Path analyses showed that depression had the most substantial influence on suicidal ideation among the elderly living alone, it was also as a mediator between physical, economic status, social support, anxiety, and suicidal ideation; anxiety and social support had both direct and indirect influence on suicidal ideation; physical status and economic status had indirection impact. The incidence of suicidal ideation among the elderly living alone was high in Dangtu county. Psychological disorders (anxiety and depression) had the strongest impact on suicidal ideation. Strategies and measures targeting these relevant factors (economic status, physical status, and social support) should be taken to reduce the burdens of suicidal ideation among the elderly living alone in China.
Testing electoral theories of US military deployment

Two prominent hypotheses link domestic political conditions, particularly electoral influences, to the use of US military force. The first is the diversionary war hypothesis, which holds that the use of force is more likely when the president faces declining public approval. The second is the hypothesis that unified party control of government, through diminished power of the opposition to constrain the executive, results in increased use of military force. In this paper, we use an (arguably) exogenous series of US monetary shocks from Romer and Romer (2004) and a regression discontinuity design of close elections to test the impact of these hypothesized electoral influences on the use of US military force.