OpenRaiser/Intern-Atlas huggingface dataset:
An Enhanced Gated Graph Neural Network for E-commerce Recommendation
Authors: Jihai Zhang, Fangquan Lin, Cheng Yang, Ziqiang Cui
Venue: CIKM
Year: 2022

The recommender system for e-commerce aims to recommend appropriate items to online customers in order to drive more views, clicks or purchases on those items. Most existing models incorporate the users' historical behaviors, their profiles, and the item metadata to achieve good performances. However, since more and more people are surfing the Internet without logging in, it is no longer capable to provide accurate recommendations based on the historical data or profiles. To tackle this issue, we propose MentalNet-a mental model for e-commerce recommendation by enhancing the gated Graph Neural Network (GNN) and capturing user intent in a short session. More precisely, MentalNet is composed of two stages: in the first stage, we enhance the gated GNN to take into account the complex graph-level transitions among items, for an improved item representation; In the second stage, we propose a mental model to simulate user intent using item embedding, and then compute item preferences based on each intent. Finally, we empirically demonstrate the effectiveness of the proposed method on three datasets, including the CIKM CUP data, the RecSys Challenge data and a real-world e-commerce dataset in Alibaba Group.
Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies
Authors: Yinghua Zhang, Yangqiu Song, Kun Bai, Qiang Yang
Venue: CIKM
Year: 2022

Fine-tuning can be vulnerable to adversarial attacks. Existing works about black-box attacks on fine-tuned models (BAFT) are limited by strong assumptions. To fill the gap, we propose two novel BAFT settings, cross-domain and cross-domain cross-architecture BAFT, which only assume that (1) the target model for attacking is a fine-tuned model, and (2) the source domain data is known and accessible. To successfully attack fine-tuned models under both settings, we propose to first train an adversarial generator against the source model, which adopts an encoder-decoder architecture and maps a clean input to an adversarial example. Then we search in the low-dimensional latent space produced by the encoder of the adversarial generator. The search is conducted under the guidance of the surrogate gradient obtained from the source model. Experimental results on different domains and different network architectures demonstrate that the proposed attack method can effectively and efficiently attack the fine-tuned models.
Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN
Authors: Xuxin Zhang, Di Wang, Dehong Gao, Wen Jiang, Wei Ning, Yang Zhou, Chen Wang
Venue: CIKM
Year: 2022

Click-through rate (CTR) prediction is one of the core tasks in industrial applications such as online advertising and recommender systems. However, the performance of existing CTR models is hampered by the cold-start users who have very few historical behavior data, given that these models often rely on enough sequential behavior data to learn the embedding vectors. In this paper, we propose a novel framework dubbed GF2 to alleviate the cold-start problem in deep learning based CTR prediction. GF2 augments the embeddings of cold-start users after the embedding layer in the deep CTR model based on the Generative Adversarial Network (GAN), and the obtained generator by GAN can be further fine-tuned locally to enhance the CTR prediction in cold-start settings. GF2 is general for deep CTR models that use embeddings to model the features of users, and it has already been deployed in real-world online display advertising system. Experimental results on two large-scale real-world datasets show that GF2 can significantly improve the prediction performance over three polular deep CTR models.
A Hyperbolic-to-Hyperbolic User Representation with Multi-aspect for Social Recommendation
Authors: Hang Zhang, Hao Wang, Guifeng Wang, Jiayu Liu, Qi Liu
Venue: CIKM
Year: 2022

Social recommender systems play a key role in solving the problem of information overload. In order to better extract latent hierarchical property in the data, they usually explore the user-user connections and user-item interactions in hyperbolic space. Existing methods resort tangent spaces to realize some operations (e.g., matrix multiplication) on hyperbolic manifolds. However, frequently projecting between the hyperbolic space and the tangent space will destroy the global structure of the manifold and reduce the accuracy of predictions. Besides, decisions made by users are often influenced by multi-aspect potential preferences, which are usually represented as a vector for each user. To this end, we design a novel hyperbolic-to-hyperbolic user representation with multi-aspect social recommender system, namely H2HMSR, which directly works in hyperbolic space. Extensive experiments on three public datasets demonstrate that our model can adequately extract social information of users with multi-aspect preferences and outperforms hyperbolic and Euclidean counterparts.
Graph Representation Learning via Adaptive Multi-layer Neighborhood Diffusion Contrast
Authors: Jijie Zhang, Yan Yang, Yong Liu, Meng Han, Shaowei Yin
Venue: CIKM
Year: 2022

In recent years, graph neural network (GNN) has become the most important method for graph representation learning. However, most GNNs focus on using the message passing mechanism to guide the information aggregation between neighbors, which results in the over-smoothing and weak robustness. To address the above issues, we propose a novel graph representation learning framework via Adaptive Multi-layer Neighborhood Diffusion Contrast, called AM-NDC in this paper. Without using the message passing mechanism, AM-NDC can still capture the complex structural information between nodes through a neighborhood diffusion contrast loss. Experimental results show that AM-NDC outperforms the existing state-of-the-art models in both node classification and robustness against adversarial attacks. Our dataset and code are available at https://github.com/YJ199804/AM-NDC.
DeepVT: Deep View-Temporal Interaction Network for News Recommendation
Authors: Xuanyu Zhang, Qing Yang, Dongliang Xu
Venue: CIKM
Year: 2022

Personalized news recommendation aims to provide people with customized content, which can effectively improve the reading experience. Because user interests in news are diverse and changeable, how to learn accurate user representations is the core challenge in news recommendation. However, most of the previous works only apply news-level representation for user modeling directly, the views of news, such as title, abstract, and category, are only implied and compressed into a single vector of news, which makes it impossible for different views in different news to interact with each other. In this paper, we first focus on the view-level information for user modeling and propose Deep View-Temporal Interaction Network (DeepVT) for news recommendation. It mainly contains two components, i.e., 2D semi-causal convolutional neural network (SC-CNN) and multi-operator attention (MoA). SC-CNN can synthesize interaction information at the view-level and temporal information at the news-level simultaneously and efficiently. And MoA integrates different similarity operators in self-attention functions to avoid attention bias and enhance robustness. By collaboration with SC-CNN, the global interaction at the view-level becomes more sufficient. Experiments on a large-scale real-world dataset, Microsoft News Dataset (MIND), show that our model outperforms previous models in terms of all metrics significantly.
Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion
Authors: Fuwei Zhang, Zhao Zhang, Xiang Ao, Fuzhen Zhuang, Yongjun Xu, Qing He
Venue: CIKM
Year: 2022

Recent years have witnessed remarkable progress on knowledge graph embedding (KGE) methods to learn the representations of entities and relations in static knowledge graphs (SKGs). However, knowledge changes over time. In order to represent the facts happening in a specific time, temporal knowledge graph (TKG) embedding approaches are put forward. While most existing models ignore the independence of semantic and temporal information. We empirically find that current models have difficulty distinguishing representations of the same entity or relation at different timestamps. In this regard, we propose a TimeLine-Traced Knowledge Graph Embedding method (TLT-KGE) for temporal knowledge graph completion. TLT-KGE aims to embed the entities and relations with timestamps as a complex vector or a quaternion vector. Specifically, TLT-KGE models semantic information and temporal information as different axes of complex number space or quaternion space. Meanwhile, two specific components carving the relationship between semantic and temporal information are devised to buoy the modeling. In this way, the proposed method can not only distinguish the independence of the semantic and temporal information, but also establish a connection between them. Experimental results on the link prediction task demonstrate that TLT-KGE achieves substantial improvements over state-of-the-art competitors. The source code will be available on https://github.com/zhangfw123/TLT-KGE.
Two-Level Graph Path Reasoning for Conversational Recommendation with User Realistic Preference
Authors: Rongmei Zhao, Shenggen Ju, Jian Peng, Ning Yang, Fanli Yan, Siyu Sun
Venue: CIKM
Year: 2022

Conversational recommender systems model user dynamic preferences and recommend items based on multi-turn interactions. Though the conversational recommender system has achieved good performance, it has two limitations. On the one hand, researchers usually random select an anchor item from user's historical interactions to simulate the interaction with the real user, but some items in the historical interactions do not fit the user realistic preferences (item noise). On the other hand, it pays too much attention to user dynamic preferences, but nurses some static preferences that are difficult to change over a short period. In fact, when there is no explicit attribute preference in user's conversation, the user static preferences can also be used to make recommendations. To address the aforementioned issues, a novel method that combines graph path reasoning with multi-turn conversation is proposed, called Graph Path reasoning for conversational Recommendation (GPR). In GPR, a soft-clustering is designed to classify items and then set operations are utilized to filter the noise in the user's historical interactions. To capture user dynamic preferences and take account of the user inherent static preferences, GPR asks questions about attributes in the attribute-level reasoning and asks whether the items fit user static preferences in the item-level reasoning on a heterogeneous graph. In the multi-turn of two-level graph path reasoning, a reinforcement learning is used to obtain the optimal path and accurately recommend items to users. Extensive experiments conducted on two benchmark datasets verify that GPR can significantly improve recommendation performance and reduce the turn of path reasoning.
KSG: Knowledge and Skill Graph
Authors: Feng Zhao, Ziqi Zhang, Donglin Wang
Venue: CIKM
Year: 2022

The knowledge graph (KG) is an essential form of knowledge representation that has grown in prominence in recent years. Because it concentrates on nominal entities and their relationships, traditional knowledge graphs are static and encyclopedic in nature. On this basis, event knowledge graph (Event KG) models the temporal and spatial dynamics by text processing to facilitate downstream applications, such as question-answering, recommendation and intelligent search. Existing KG research, on the other hand, mostly focuses on text processing and static facts, ignoring the vast quantity of dynamic behavioral information included in photos, movies, and pre-trained neural networks. In addition, no effort has been done to include behavioral intelligence information into the knowedge graph for deep reinforcement learning (DRL) and robot learning. In this paper, we propose a noval dynamic knowledge and skill graph (KSG), and then we develop a basic and specific KSG based on CN-DBpedia. The nodes are divided into entity and attribute nodes, with entity nodes containing the agent, environment, and skill (DRL policy or policy representation), and attribute nodes containing the entity description, pre-train network, and offline dataset. KSG can search for different agents' skills in various environments and provide transferable information for acquiring new skills. This is the first study that we are aware of that looks into dynamic KSG for skill retrieval and learning. Extensive experimental results on new skill learning show that KSG boosts new skill learning efficiency.
MAE4Rec: Storage-saving Transformer for Sequential Recommendations
Authors: Kesen Zhao, Xiangyu Zhao, Zijian Zhang, Muyang Li
Venue: CIKM
Year: 2022

Sequential recommender systems (SRS) aim to infer the users' preferences from their interaction history and predict items that will be of interest to the users. The majority of SRS models typically incorporate all historical interactions for next-item recommendations. Despite their success, feeding all interactions into the model without filtering may lead to severe practical issues: (i) redundant interactions hinder the SRS model from capturing the users' intentions; (ii) the computational cost is huge, as the computational complexity is proportional to the length of the interaction sequence; (iii) more memory space is necessitated to store all interaction records from all users. To this end, we propose a novel storage-saving SRS framework, MAE4Rec, based on a unidirectional self-attentive mechanism and masked autoencoder. Specifically, in order to lower the storage consumption, MAE4Rec first masks and discards a large percentage of historical interactions, and then infers the next interacted item solely based on the latent representation of unmarked ones. Experiments on two real-world datasets demonstrate that the proposed model achieves competitive performance against state-of-the-art SRS models with more than 40% compression of storage.
Towards Edge-Cloud Collaborative Machine Learning: A Quality-aware Task Partition Framework
Authors: Zimu Zheng, Yunzhe Li, Han Song, Lanjun Wang, Fei Xia
Venue: CIKM
Year: 2022

Edge-cloud collaborative tasks with real-world services emerge in recent years and attract worldwide attention. Unfortunately, state-of-the-art edge-cloud collaborative machine-learning services are still not that reliable due to the data heterogeneity on the edge, where we usually have access to a mixed-up training set, which is intrinsically collected from various distributions of underlying tasks. Finding such hidden tasks that need to be revealed from given datasets is called the Task Partition problem. Manual task partition is usually expensive, unscalable, and biased. Accordingly, we propose Quality-aware Task Partition (QTP) problem, in which final tasks are partitioned by the performance of task models. To the best of our knowledge, this work is the first one to study the QTP problem with an emphasis on task quality. We also implement a public service, HiLens on Huawei Cloud, to support the whole process. We develop a polynomial-time algorithm namely the Task-Forest algorithm (TForest). TForest shows its superiority based on a case study with 57 real-world cameras. Compared with STOA baselines, TForest has on average 9.2% higher F1-scores and requires 43.1% fewer samples when deploying new cameras. Partial code of the framework has been adopted and released to KubeEdge-Sedna.
eDental: Managing Your Dental Care in Diet Diaries
Authors: Kaiping Zheng, Thao Nguyen, Changshuo Liu, Charlene Enhui Goh, Beng Chin Ooi
Venue: CIKM
Year: 2022

The demand for satisfactory dental care management has attracted a great deal of attention from both dentists and patients. Reviews of existing systems and approaches reveal that they either fail to take into account patients' daily diets that are a significant risk factor for dental decay, or are too complicated for patients. To facilitate patients' tracking and management of their dietary risk factors for dental decay, and improve dentists' identification of decay-related dietary patterns, we develop a system called eDental, in collaboration with dentists and oral surgeons, as a mechanism to record users' detailed daily diet diaries by snapping food photos. The system identifies the food using a state-of-the-art deep learning model and analyzes patients' dental care conditions and potential dental risks. eDental is a full-fledged oral care system with easy-to-use user interfaces. In this demonstration, we showcase eDental's key functionalities for managing patients' dental care via diet diaries.
Network Report: A Structured Description for Network Datasets
Authors: Xinyi Zheng, Ryan A. Rossi, Nesreen K. Ahmed, Dominik Moritz
Venue: CIKM
Year: 2022

The rapid development of network science and technologies depends on shareable datasets. Currently, there is no standard practice for reporting and sharing network datasets. Some network dataset providers only share links, while others provide some contexts or basic statistics. As a result, critical information may be unintentionally dropped, and network dataset consumers may misunderstand or overlook critical aspects. Inappropriately using a network dataset can lead to severe consequences (e.g., discrimination) especially when machine learning models on networks are deployed in high-stake domains. Challenges arise as networks are often used across different domains (e.g., network science, physics, etc) and have complex structures. To facilitate the communication between network dataset providers and consumers, we propose network report. A network report is a structured description that summarizes and contextualizes a network dataset. Network report extends the idea of dataset reports (e.g., Datasheets for Datasets) from prior work with network-specific descriptions of the non-i.i.d. nature, demographic information, network characteristics, etc. We hope network reports encourage transparency and accountability in network research and development across different fields.
ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics
Authors: Zhuoxun Zheng, Baifan Zhou, Dongzhuoran Zhou, Ahmet Soylu, Evgeny Kharlamov
Venue: CIKM
Year: 2022

Data analytics including machine learning (ML) is essential to extract insights from production data in modern industries. However, industrial ML is affected by: the low transparency of ML towards non-ML experts; poor and non-unified descriptions of ML practices for reviewing or comprehension; ad-hoc fashion of ML solutions tailored to specific applications, which affects their re-usability. To address these challenges, we propose the concept and a system of executable knowledge graph (KG), which represent KGs that rely on semantic technologies to formally encode ML knowledge and solutions. These KGs can be translated to executable scripts in a reusable and modularised fashion. The demo attendees will use our system to modify, integrate and create executable KGs via a graphic user interface, which offer a user-friendly way to understand, configure, reuse, and create data analytics pipelines.
Executable Knowledge Graph for Transparent Machine Learning in Welding Monitoring at Bosch
Authors: Zhuoxun Zheng, Baifan Zhou, Dongzhuoran Zhou, Ahmet Soylu, Evgeny Kharlamov
Venue: CIKM
Year: 2022

With the development of Industry 4.0 technology, modern industries such as Bosch's welding monitoring witnessed the rapid widespread of machine learning (ML) based data analytical applications, which in the case of welding monitoring has led to more efficient and accurate welding monitoring quality. However, industrial ML is affected by the low transparency of ML towards non-ML experts needs. The lack of understanding by domain experts of ML methods hampers the application of ML methods in industry and the reuse of developed ML pipelines, as ML methods are often developed in an ad hoc manner for specific problems. To address these challenges, we propose the concept and a system of executable Knowledge Graph (KG), which formally encode ML knowledge and solutions in KGs, which serve as common language between ML experts and non-ML experts, thus facilitate their communication and increase the transparency of ML methods. We evaluated our system extensively with an industrial use case at Bosch, showing promising results.
Ledgit: A Service to Diagnose Illicit Addresses on Blockchain using Multi-modal Unsupervised Learning
Authors: Xiaoying Zhi, Yash Satsangi, Sean J. Moran, Shaltiel Eloul
Venue: CIKM
Year: 2022

Distributed ledger technology benefits society by enabling an ecosystem of decentralised finance. However the pseudo-anonymised nature of transactions has also been an enabler of new routes for illicit activities ranging from individual scams to organised crimes. Current solutions for identifying addresses involved in illicit activities (illicit addresses) rely on commercial intelligence services, which are costly due to the intensive investigative efforts required. We propose Ledgit, an automatic real-time service for diagnosing illicit addresses on the Bitcoin blockchain. Ledgit is based solely on publicly available data, and uses an unsupervised clustering method that combines information from textual reports and the blockchain graph to assign a risk score that a Bitcoin address is involved in illicit activities. We verify the system with labeled addresses, showing high performance in identifying illicit addresses. Finally, we provide an intuitive user interface that provides accessible risk assessment with graph and report analytics.
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
Authors: Honglu Zhou, Advith Chegu, Samuel S. Sohn, Zuohui Fu, Gerard de Melo, Mubbasir Kapadia
Venue: CIKM
Year: 2022

Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs). Prior work in DRL is largely constrained (e.g., limited to directed acyclic graphs), or has poor generalizability across tasks (e.g., evaluated solely on one task). Most Graph Neural Networks (GNNs) exhibit poor performance on digraphs due to the neglect of modeling neighborhoods and preserving asymmetry. In this paper, we address these notable challenges by leveraging hyperbolic collaborative learning from multi-ordered and partitioned neighborhoods, and regularizers inspired by socio-psychological factors. Our resulting formalism, Digraph Hyperbolic Networks (D-HYPR) -- albeit conceptually simple -- generalizes to digraphs where cycles and non-transitive relations are common, and is applicable to multiple downstream tasks including node classification, link presence prediction, and link property prediction. In order to assess the effectiveness of D-HYPR, extensive evaluations were performed across 8 real-world digraph datasets involving 21 prior techniques. D-HYPR statistically significantly outperforms the current state of the art. We release our code at https://github.com/hongluzhou/dhypr