Marketplaces with multiple sellers have progressively evolved into viable business models in many web applications. Within this sphere, a marketplace recommendation model provides personalized suggestions regarding items and sellers that correspond with users’ preferences. However, a majority of the popular recommendation models are item-centric, often neglecting the incorporation of user preferences to sellers, thereby undermining the comprehensive utilization of the seller-related information contained in the marketplace datasets.To deal with the aforementioned limitations, this study presents a novel model for marketplace recommendations. It employs multi-task learning to jointly recommend items and sellers to users. Here, we introduce the KAROL, a model comprised of two principal modules. The first module is a Knowledge Attentive Graph Convolutional Network (KAGCN) structure based on the user-item-seller knowledge graph (KG). Specifically, relation-aware graph attention and LightGCN are employed to learn node embeddings of users, items and sellers. Sellers and items function reciprocally as knowledge bases for the bipartite graphs to transfer the knowledge between different tasks. It further employs dual losses to concurrently generate recommendations for both items and sellers. The second module is Adaptive Contrastive Learning (ACL), which involves a contrastive loss that incorporates three schemes for data augmentation: cross-relation sampling, edge-dropping and noise addition, to address knowledge sharing, structural consistency, and robustness challenges. An additional innovative facet is the incorporation of an adaptive temperature that is automatically optimized for contrastive loss without manual hyperparameter tuning. The experiment results on three datasets demonstrate that our model outperforms nine baseline models on both item and seller recommendation tasks.

Online e-commerce platforms have been extending in-store shopping, which allows users to keep the canonical online browsing and checkout experience while exploring in-store shopping. However, the growing transition between online and in-store becomes a challenge to online sequential recommender systems for future online interaction prediction due to the lack of holistic modeling of hybrid user behaviors (online & in-store). The challenges are two-fold. First, combining online & in-store user behavior data into a single data schema and supporting multiple stages in the model life cycle (pre-training, training, inference, etc.) organically needs a new data pipeline design. Second, online recommender systems, which solely relies on online user behavior sequences, must be redesigned to support online and in-store user data as input under the sequential modeling setting. To overcome the first challenge, we propose a hybrid, omnichannel data pipeline to compile online & in-store user behavior data by caching information from diverse data sources. Later, we introduce a model-agnostic encoder module to the sequential recommender system to interpret the user in-store transaction and augment the modeling capacity for better online interaction prediction given the hybrid user behavior.

Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms, and social networks, to provide suggestions that users will most likely consume or connect, thus improving the user experience. This motivates people in industry and research organizations to focus on personalization and recommendation algorithms, resulting in many research papers. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment, including but not limited to business metric definition and evaluation, scalability, recommendation quality control, robustness, fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions to industrial recommendation systems. This workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue for industrial researchers to share practical insights and for academic researchers to become aware of the additional factors of algorithm adoption in real production systems.

Product attribute value extraction is a pivotal component in Natural Language Processing (NLP) and the contemporary e-commerce industry. The provision of precise product attribute values is fundamental in ensuring high-quality recommendations and enhancing customer satisfaction. The recently emerging Large Language Models (LLMs) have demonstrated state of-the-art performance in numerous attribute extraction tasks, without the need for domain-specific training data. Nevertheless, varying strengths and weaknesses are exhibited by different LLMs due to the diversity in data, architectures, and hyperparameters. This variation makes them complementary to each other, with no single LLM dominating all others. Considering the diverse strengths and weaknesses of LLMs, it becomes necessary to develop an ensemble method that leverages their complementary potentials.

Personalized recommender systems aim to predict users’ preferences for items. It has become an indispensable part of online services. Online social platforms enable users to form groups based on their common interests. The users’ group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems. Users join different groups out of different interests. In this paper, we generate group representation from the user’s interests and propose IGRec (Interest-based Group enhanced Recommendation) to utilize the group information accurately. It consists of four modules. (1) Interest disentangler via self-gating that disentangles users’ interests from their initial embedding representation. (2) Interest aggregator that generates the interest-based group representation by Gumbel-Softmax aggregation on the group members’ interests. (3) Interest-based group aggregation that fuses user’s representation with the participated group representation. (4) A dual-trained rating prediction module to utilize both user-item and group-item interactions. We conduct extensive experiments on three publicly available datasets. Results show IGRec can effectively alleviate the data sparsity problem and enhance the recommender system with interest-based group representation. Experiments on the group recommendation task further show the informativeness of interest-based group representation.

The fairness issue of clinical data modeling, especially on Electronic Health Records (EHRs), is of utmost importance due to EHR’s complex latent structure and potential selection bias. However, traditional methods often encounter the tradeoff between accuracy and fairness, as they fail to capture the underlying factors beyond observed data. To tackle this challenge, we propose a novel model called Fair Longitudinal Medical Deconfounder (FLMD)1that aims to achieve both fairness and accuracy in longitudinal Electronic Health Records (EHR) modeling. FLMD employs a two-stage training process. In the first stage, FLMD captures unobserved confounders for each encounter, effectively representing underlying medical factors beyond observed EHR, such as patient genotypes and lifestyle habits. This unobserved confounder is crucial for addressing the accuracy/fairness dilemma. In the second stage, FLMD combines the learned latent representation with other relevant features to make predictions. By incorporating appropriate fairness criteria, such as counterfactual fairness, FLMD ensures that it maintains high prediction accuracy while simultaneously minimizing health disparities. We conducted comprehensive experiments on two real-world EHR datasets to demonstrate the effectiveness of FLMD. Apart from comparing baseline methods and FLMD variants in terms of fairness and accuracy, we assessed the performance of all models on disturbed/imbalanced and synthetic datasets to showcase the superiority of FLMD across different settings and provide valuable insights into its capabilities. For more details about this work, please refer to the full version of our paper2.1https://anonymous.4open.science/r/ICDM_FLMD-C2232https://arxiv.org/abs/2308.11819

Session-based Recommendation (SBR) is to predict users’ next interested items based on their previous browsing sessions. Existing methods model sessions as graphs or sequences to estimate user interests based on their interacted items to make recommendations. In recent years, graph-based methods have achieved outstanding performance on SBR. However, none of these methods consider temporal information, which is a crucial feature in SBR as it indicates timeliness or currency. Besides, the session graphs exhibit a hierarchical structure and are demonstrated to be suitable in hyperbolic geometry. But few papers design the models in hyperbolic spaces and this direction is still under exploration.In this paper, we propose Time-aware Hyperbolic Graph Attention Network (TA-HGAT) — a novel hyperbolic graph neural network framework to build a session-based recommendation model considering temporal information. More specifically, there are three components in TA-HGAT. First, a hyperbolic projection module transforms the item features into hyperbolic space. Second, the time-aware graph attention module models time intervals between items and the users’ current interests. Third, an evolutionary loss at the end of the model provides an accurate prediction of the recommended item based on the given timestamp. TA-HGAT is built in a hyperbolic space to learn the hierarchical structure of session graphs. Experimental results show that the proposed TA-HGAT has the best performance compared to ten baseline models on two real-world datasets.

Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequency of the user’s interactions with the item. However, taking the PIF as an explicit feature incurs bias towards frequent items. Items that a user purchases frequently are assigned higher weights in PIF-based recommender system and appear more frequently in the personalized recommendation list. As a result, the system will lose the fairness and balance between items that the user frequently purchases and items that the user never purchases. We refer to this systematic bias on personalized recommendation lists as frequency bias, which narrows users’ browsing scope and reduces the system utility. We adopt causal inference theory to address this issue. Considering the influence of historical purchases on users’ future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram. In this paper, we propose a deconfounder model named FENDER (Frequency-aware Deconfounder for Next-basket Recommendation) to mitigate the frequency bias. With the deconfounder theory and the causal diagram we propose, FENDER decomposes PIF with a neural tensor layer to obtain substitute confounders for users and items. Then, FENDER performs unbiased recommendations considering the effect of these substitute confounders. Experimental results demonstrate that FENDER has derived diverse and fair results compared to ten baseline models on three datasets while achieving competitive performance. Further experiments illustrate how FENDER balances users’ historical purchases and potential interests.

Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. However, in real-world scenarios, the relations in a recommendation graph can be of various kinds. For example, two movies may be associated either by the same genre or by the same director/actor. If we use a single graph to elaborate all these relations, the graph can be too complex to process. To address this issue, we bring the idea of pre-training to process the complex graph step by step. Based on the idea of divide-and-conquer, we separate the large graph into three sub-graphs: user graph, item graph, and user-item interaction graph. Then the user and item embeddings are pre-trained from user and item graphs, respectively. To conduct pre-training, we construct the multi-relational user graph and item graph, respectively, based on their attributes.In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to pre-train user and item embeddings on the user and item graph prior to the recommendation step. Specifically, we design a relation-level attention layer to learn the importance of different relations. Next, a Reinforced Neighbor Sampler (RNS) is applied to search the optimal filtering threshold for sampling top-k similar neighbors in the graph, which avoids the over-smoothing issue. We initialize the recommendation model with the pre-trained user/item embeddings. Finally, an aggregation-based GNN model is utilized to learn from the collaborative relations in the user-item interaction graph and provide recommendations. Our experiments demonstrate that RAM-GNN outperforms other state-of-the-art graph-based recommendation models and multi-relational graph neural networks.

Medical triage chatbot is widely used in pre-diagnosis by asking symptom and medical history-related questions. Information collected from patients through an online chatbot system is often incomplete and imprecise, and thus it's essentially hard to achieve precise triaging. In this paper, we propose Multi-relational Hyperbolic Diagnosis Predictor (MHDP) --- a novel multi-relational hyperbolic graph neural network-based approach, to build a disease predictive model. More specifically, in MHDP, we generate a heterogeneous graph consisting of symptoms, patients, and diagnoses nodes, and then derive node representations by aggregating neighborhood information recursively in the hyperbolic space. Experiments conducted on two real-world datasets demonstrate that the proposed MHDP approach surpasses state-of-the-art baselines.

The problem of basket recommendation (BR) is to recommend a ranking list of items to the current basket. Existing methods solve this problem by assuming the items within the same basket are correlated by one semantic relation, thus optimizing the item embeddings. However, this assumption breaks when there exist multiple intents within a basket. For example, assuming a basket contains {bread, cereal, yogurt, soap, detergent} where {bread, cereal, yogurt} are correlated through the "breakfast" intent, while {soap, detergent} are of "cleaning" intent, ignoring multiple relations among the items spoils the ability of the model to learn the embeddings. To resolve this issue, it is required to discover the intents within the basket. However, retrieving a multi-intent pattern is rather challenging, as intents are latent within the basket. Additionally, intents within the basket may also be correlated. Moreover, discovering a multi-intent pattern requires modeling high-order interactions, as the intents across different baskets are also correlated. To this end, we propose a new framework named as Multi-Intent Translation Graph Neural Network (MITGNN). MITGNN models T intents as tail entities translated from one corresponding basket embedding via T relation vectors. The relation vectors are learned through multi-head aggregators to handle user and item information. Additionally, MITGNN propagates multiple intents across our defined basket graph to learn the embeddings of users and items by aggregating neighbors. Extensive experiments on two real-world datasets prove the effectiveness of our proposed model on both transductive and inductive BR. The code1is available online.

Mining Electronic Health Records (EHRs) becomes a promising topic because of the rich information they contain. By learning from EHRs, machine learning models can be built to help human expert to make medical decisions and thus improve healthcare quality. Recently, many models based on sequential or graph model are proposed to achieve this goal. EHRs contain multiple entities and relations, and can be viewed as a heterogeneous graph. However, previous studies ignore the heterogeneity in EHRs. On the other hand, current heterogeneous graph neural networks cannot be simply used on EHR graph because of the existence of hub nodes in it. To address this issue, we propose Heterogeneous Similarity Graph Neural Network (HSGNN) to analyze EHRs with a novel heterogeneous GNN. Our framework consists of two parts: one is a preprocessing method and the other is an end-to-end GNN. The preprocessing method normalizes edges and splits the EHR graph into multiple homogeneous graphs while each homogeneous graph contains partial information of the original EHR graph. The GNN takes all homogeneous graphs as input and fuses all of them into one graph to make prediction. Experimental results show that HSGNN outperforms other baselines in the diagnosis prediction task.

Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and interests of users change over time. Based on this assumption, many previous works focus on interaction sequences and learn evolutionary embeddings of users and items. However, we argue that sequence-based models are not able to capture collaborative information among users and items directly. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time. We propose three update mechanisms: zero-order `inheritance', first-order `propagation', and second-order `aggregation', to represent the impact on a user or item when a new interaction occurs. Based on them, we update related user and item embeddings simultaneously when interactions occur in turn, and then use the latest embeddings to make recommendations. Extensive experiments conducted on three public datasets show that DGCF significantly outperforms the state-of-the-art dynamic recommendation methods up to 30%. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.

Recently, the percentage of people with hypertension is increasing, and this phenomenon is widely concerned. At the same time, wireless home Blood Pressure (BP) monitors become accessible in people's life. Since machine learning methods have made important contributions in different fields, many researchers have tried to employ them in dealing with medical problems. However, the existing studies for BP prediction are all based on clinical data with short time ranges. Besides, there do not exist works which can jointly make use of historical measurement data (e.g. BP and heart rate) and contextual data (e.g. age, gender, BMI and altitude). Recurrent Neural Networks (RNNs), especially those using Long Short-Term Memory (LSTM) units, can capture long range dependencies, so they are effective in modeling variable-length sequences. In this paper, we propose a novel model named recurrent models with contextual layer, which can model the sequential measurement data and contextual data simultaneously to predict the trend of users' BP. We conduct our experiments on the BP data set collected from a type of wireless home BP monitors, and experimental results show that the proposed models outperform several competitive compared methods.