OpenRaiser/Intern-Atlas huggingface dataset:
TripSafe: Retrieving Safety-related Abnormal Trips in Real-time with Trajectory Data
Authors: Yueyang Su, Di Yao, Xiaolei Zhou, Yuxuan Zhang, Yunxia Fan, Lu Bai, Jingping Bi
Venue: SIGIR
Year: 2023

Nowadays safety has become one of the most critical factors for ride-hailing service. Ride-hailing platforms have conducted meticulous background checks for drivers to minimize the risk of abnormal trips, e.g. violence and sexual assault. However, current methods are labor-consuming and highly rely on the personal information of drivers, which may harm the fairness of the order dispatching system. In this paper, we utilize the trip trajectories as inputs and propose a dual variational auto-encoder(VAE) framework, namely TripSafe, to estimate the probability of abnormal safety incidents. Specifically, TripSafe models the moving behavior and route information, as two independent components and employs VAEs to pre-train generative models for normal trips. Then, a fusion network is adopted to fine-tune the whole model with a few labeled samples. In practice, TripSafe monitors the data update and calculate the anomaly score of partial-observed trips in real-time. Experiments on real ridehailing data show that TripSafe is superior to the state-of-the-art baselines with about 14.2%~28.9% improvements on F1 score.
Personalized Stock Recommendation with Investors' Attention and Contextual Information
Authors: Takehiro Takayanagi, Kiyoshi Izumi, Atsuo Kato, Naoyuki Tsunedomi, Yukina Abe
Venue: SIGIR
Year: 2023

The personalized stock recommendation is a task to recommend suitable stocks for each investor. The personalized recommendations are valuable, especially in investment decision making as the objective of building a portfolio varies by each retail investor. In this paper, we propose a Personalized Stock Recommendation with Investors' Attention and Contextual Information (PSRIC). PSRIC aims to incorporate investors' financial decision-making process into a stock recommendation, and it consists of an investor modeling module and a context module. The investor modeling module models the investor's attention toward various stock information. The context module incorporates stock dynamics and investor profiles. The result shows that the proposed model outperforms the baseline models and verifies the usefulness of both modules in ablation studies.
SCHash: Speedy Simplicial Complex Neural Networks via Randomized Hashing
Authors: Xuan Tan, Wei Wu, Chuan Luo
Venue: SIGIR
Year: 2023

Graphs, as a non-linear data structure, are ubiquitous in practice, and efficient graph analysis can benefit important information retrieval applications in the era of big data. Currently, one of the fundamental graph mining problems is graph embedding, which aims to represent the graph as a low-dimensional feature vector with the content and structural information in the graph preserved. Although the graph embedding technique has evolved considerably, traditional methods mainly focus on node pairwise relationship in graphs, which makes the representational power of such schemes limited. Recently, a number of works have explored the simplicial complexes, which describe the higher-order interactions between nodes in the graphs, and further proposed several Graph Neural Network (GNN) algorithms based on simplicial complexes. However, these GNN approaches are highly inefficient in terms of running time and space, due to massive parameter learning. In this paper, we propose a simple and speedy graph embedding algorithm dubbed SCHash. Through adopting the Locality Sensitive Hashing (LSH) technique, SCHash captures the higher-order information derived from the simplicial complex in the GNN framework, and it can achieve a good balance between accuracy and efficiency. Our extensive experiments clearly show that, in terms of accuracy, the performance of our proposed SCHash algorithm is comparable to that of state-of-the-art GNN algorithms; also, SCHash achieves higher accuracy than the existing LSH algorithms. In terms of efficiency, SCHash runs faster than GNN algorithms by 2 ~ 4 orders of magnitude, and is more efficient than the existing LSH algorithms.
Multi-order Matched Neighborhood Consistent Graph Alignment in a Union Vector Space
Authors: Wei Tang, Haifeng Sun, Jingyu Wang, Qi Qi, Jing Wang, Hao Yang, Shimin Tao
Venue: SIGIR
Year: 2023

In this paper, we study the unsupervised plain graph alignment problem, which aims to find node correspondences across two graphs without any side information. The majority of previous works addressed UPGA based on structural information, which will inevitably lead to subgraph isomorphism issues. That is, unaligned nodes could take similar local structural information. To mitigate this issue, we present the Multi-order Matched Neighborhood Consistent (MMNC) which tries to match nodes by aligning the learned node embeddings with only a small number of pseudo alignment seeds. In particular, we extend matched neighborhood consistency (MNC) to vector space and further develop embedding-based MNC (EMNC). By minimizing the EMNC-based loss function, we can utilize the limited pseudo alignment seeds to approximate the orthogonal transformation matrix between two groups of node embeddings with high efficiency and accuracy. Through extensive experiments on public benchmarks, we show that the proposed methods achieve a good balance between alignment accuracy and speed over multiple datasets compared with existing methods.
Dynamic Graph Evolution Learning for Recommendation
Authors: Haoran Tang, Shiqing Wu, Guandong Xu, Qing Li
Venue: SIGIR
Year: 2023

Graph neural network (GNN) based algorithms have achieved superior performance in recommendation tasks due to their advanced capability of exploiting high-order connectivity between users and items. However, most existing GNN-based recommendation models ignore the dynamic evolution of nodes, where users will continuously interact with items over time, resulting in rapid changes in the environment (e.g., neighbor and structure). Moreover, the heuristic normalization of embeddings in dynamic recommendation is de-coupled with the model learning process, making the whole system suboptimal. In this paper, we propose a novel framework for generating satisfying recommendations in dynamic environments, called Dynamic Graph Evolution Learning (DGEL). First, we design three efficient real-time update learning methods for nodes from the perspectives of inherent interaction potential, time-decay neighbor augmentation, and symbiotic local structure learning. Second, we construct the re-scaling enhancement networks for dynamic embeddings to adaptively and automatically bridge the normalization process with model learning. Third, we leverage the interaction matching task and the future prediction task together for joint training to further improve performance. Extensive experiments on three real-world datasets demonstrate the effectiveness and improvements of our proposed DGEL. The code is available at https://github.com/henrictang/DGEL.
Towards Multi-Interest Pre-training with Sparse Capsule Network
Authors: Zuoli Tang, Lin Wang, Lixin Zou, Xiaolu Zhang, Jun Zhou, Chenliang Li
Venue: SIGIR
Year: 2023

The pre-training paradigm, i.e., learning universal knowledge across a wide spectrum of domains, has increasingly become a new de-facto practice in many fields, especially for transferring to new domains. The recent progress includes universal pre-training solutions for recommendation. However, we argue that the common treatment utilizing the masked language modeling or simple data augmentation via contrastive learning is not sufficient for pre-training a recommender system, since a user's intent could be more complex than predicting the next word or item. It is more intuitive to go a step further by devising the multi-interest driven pre-training framework for universal user understanding. Nevertheless, incorporating multi-interest modeling in recommender system pre-training is non-trivial due to the dynamic, contextual, and temporary nature of the user interests, particularly when the users are from different domains. The limited effort on this line has greatly rendered it as an open question. In this paper, we propose a novel Multi-Interest Pre-training with Sparse Capsule framework (named Miracle). Miracle performs a universal multi-interest modeling with a sparse capsule network and an interest-aware pre-training task. Specifically, we utilize a text-aware item embedding module, including an MoE adaptor and a deeply-contextual encoding component, to model contextual and transferable item representations. Then, we propose a sparse interest activation mechanism coupled with a position-aware capsule network for adaptive interest extraction. Furthermore, an interest-level contrastive pre-training task is introduced to guide the sparse capsule network to learn universal interests precisely. We conduct extensive experiments on eleven real-world datasets and eight baselines. The results show that our method significantly outperforms a series of SOTA on these benchmark datasets. The code is available at https://github.com/WHUIR/Miracle.
TMML: Text-Guided MuliModal Product Location For Alleviating Retrieval Inconsistency in E-Commerce
Authors: Youhua Tang, Xiong Xiong, Siyang Sun, Baoliang Cui, Yun Zheng, Haihong Tang
Venue: SIGIR
Year: 2023

Image retrieval system (IRS) is commonly used in E-Commerce platforms for a wide range of applications such as price comparison and commodity recommendation. However, customers may experience inconsistent retrieval problems. Although the retrieved image contains the query object, the main product of the retrieved image is not associated with the query product. This is caused by the wrong product instance location when building the product image retrieval library. We can easily determine which product is on sale through the hint of the title, so we propose Text-Guided MuliModal Product Location (TMML) to use additional product titles to assist in locating the actual selling product instance. We design a weakly-aligned region-text data collection method to generate region-text pseudo-label by utilizing the IRS and user behavior from the E-commerce platform. To mitigate the impact of data noise, we propose a Mutual-Aware Contrastive Loss. Our results show that the proposed TMML outperforms the state-of-the-art method GLIP [11] by 3.95% in top-1 precision on our multi-objects test set, and 2.53% error located images in AliExpress has been corrected, which greatly alleviates the retrieval inconsistencies in IRS.
Event-Aware Adaptive Clustering Uplift Network for Insurance Creative Ranking
Authors: Wanjie Tao, Huihui Liu, Xuqi Li, Qun Dai, Hong Wen, Zulong Chen
Venue: SIGIR
Year: 2023

In the classical e-commerce platforms, the personalized product-tying recommendation has proven to be of great added value, which improves users' purchase willingness to product-tying by displaying the suitable marketing creative. In this paper, we present a new recommendation problem, i.e., the Pop-up One-time Marketing (POM), where the product-tying marketing creative only pops up one time when the user pays for the main item. POM has become a ubiquitous application in e-commerce platforms, e.g., buy the mobile tying mobile case and buy flight ticket tying insurance. However, many existing recommendation methods are sub-optimal for the creative marketing in the POM scenario due to unconsidering the unique characteristics in the scenario. To tackle this problem, we propose a novel framework named Event-aware Adaptive Clustering Uplift Network (EACU-Net) for the POM scenario, which is to our best knowledge the first attempt along this line. EACU-Net contains three modules: (1) the event-aware graph cascading learning, which employs a heterogeneous graph network to comprehensively learn the embedding for the user attributes, event categories, and creative elements by stage. (2) an adaptive clustering uplift network, which learns the sensitivity of users to creatives under the same context. (3) an event-aware information gain network to learn more information from samples with event affection. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superior performance of the proposed model compared with the state-of-the-art method.
Where Does Your News Come From? Predicting Information Pathways in Social Media
Authors: Alexander K. Taylor, Nuan Wen, Po-Nien Kung, Jiaao Chen, Violet Peng, Wei Wang
Venue: SIGIR
Year: 2023

As social networks become further entrenched in modern society, it becomes increasingly important to understand and predict how information (e.g., news coverage of a given event) is propagated across social media (i.e., information pathway), which helps the understandings of the impact of real-world information. Thus, in this paper, we propose a novel task, Information Pathway Prediction (IPP), which depicts the propagation paths of a given passage as a community tree (rooted at the information source) on constructed community interaction graphs where we first aggregate individual users into communities formed around news sources and influential users, and then elucidate the patterns of information dissemination across media based on such community nodes. We argue that this is an important and useful task because, on one hand, community-level interactions offer more stability than those at the user level; on the other hand, individual users are often influenced by their community, and modeling community-level information propagation will help the traditional link-prediction problem. To tackle the IPP task, we introduce Lightning, a novel content-aware link prediction GNN model and demonstrate using a large Twitter dataset consisting of all COVID related tweets that Lightning outperforms state-of-the-art link prediction baselines by a significant margin.
Multi-Grained Topological Pre-Training of Language Models in Sponsored Search
Authors: Zhoujin Tian, Chaozhuo Li, Zhiqiang Zuo, Zengxuan Wen, Xinyue Hu, Xiao Han, Haizhen Huang, Senzhang Wang, Weiwei Deng, Xing Xie, Qi Zhang
Venue: SIGIR
Year: 2023

Relevance models measure the semantic closeness between queries and the candidate ads, widely recognized as the nucleus of sponsored search systems. Conventional relevance models solely rely on the textual data within the queries and ads, whose performance is hindered by the scarce semantic information in these short texts. Recently, user behavior graphs have been incorporated to provide complementary information beyond pure textual semantics.Despite the promising performance, behavior-enhanced models suffer from exhausting resource costs due to the extra computations introduced by explicit topological aggregations. In this paper, we propose a novel Multi-Grained Topological Pre-Training paradigm, MGTLM, to teach language models to understand multi-grained topological information in behavior graphs, which contributes to eliminating explicit graph aggregations and avoiding information loss. Extensive experimental results over online and offline settings demonstrate the superiority of our proposal.
Multimodal Neural Databases
Authors: Giovanni Trappolini, Andrea Santilli, Emanuele Rodolà, Alon Y. Halevy, Fabrizio Silvestri
Venue: SIGIR
Year: 2023

The rise in loosely-structured data available through text, images, and other modalities has called for new ways of querying them. Multimedia Information Retrieval has filled this gap and has witnessed exciting progress in recent years. Tasks such as search and retrieval of extensive multimedia archives have undergone massive performance improvements, driven to a large extent by recent developments in multimodal deep learning. However, methods in this field remain limited in the kinds of queries they support and, in particular, their inability to answer database-like queries. For this reason, inspired by recent work on neural databases, we propose a new framework, which we name Multimodal Neural Databases (MMNDBs). MMNDBs can answer complex database-like queries that involve reasoning over different input modalities, such as text and images, at scale. In this paper, we present the first architecture able to fulfill this set of requirements and test it with several baselines, showing the limitations of currently available models. The results show the potential of these new techniques to process unstructured data coming from different modalities, paving the way for future research in the area.
Data-Aware Proxy Hashing for Cross-modal Retrieval
Authors: Rong-Cheng Tu, Xian-Ling Mao, Wenjin Ji, Wei Wei, Heyan Huang
Venue: SIGIR
Year: 2023

Recently, numerous proxy hash code based methods, which sufficiently exploit the label information of data to supervise the training of hashing models, have been proposed. Although these methods have made impressive progress, their generating processes of proxy hash codes are based only on the class information of the dataset or labels of data but do not take the data themselves into account. Therefore, these methods will probably generate some inappropriate proxy hash codes, thus damaging the retrieval performance of the hash models. To solve the aforementioned problem, we propose a novel Data-Aware Proxy Hashing for cross-modal retrieval, called DAPH. Specifically, our proposed method first train a data-aware proxy network that takes the data points, label vectors of data, and the class vectors of the dataset as inputs to generate class-based data-aware proxy hash codes, label-fused image-aware proxy hash codes and label-fused text-aware proxy hash codes. Then, we propose a novel hash loss that exploits the three types of data-aware proxy hash codes to supervise the training of modality-specific hashing networks. After training, DAPH is able to generate discriminate hash codes with the semantic information preserved adequately. Extensive experiments on three benchmark datasets show that the proposed DAPH outperforms the state-of-the-art baselines in cross-modal retrieval tasks.
Personalized Retrieval over Millions of Items
Authors: Hemanth Vemuri, Sheshansh Agrawal, Shivam Mittal, Deepak Saini, Akshay Soni, Abhinav V. Sambasivan, Wenhao Lu, Yajun Wang, Mehul Parsana, Purushottam Kar, Manik Varma
Venue: SIGIR
Year: 2023

Personalized retrieval seeks to retrieve items relevant to a user event (e.g. a page visit or a query) that are adapted to the user's personal preferences. For example, two users who happen to perform the same event such as visiting the same product page or asking the same query should receive potentially distinct recommendations adapted to their individual tastes. Personalization is seldom attempted over catalogs of millions of items since the cost of existing personalization routines scale linearly in the number of candidate items. For example, performing two-sided personalized retrieval (with both event and item embeddings personalized to the user) incurs prohibitive storage and compute costs. Instead, it is common to use non-personalized retrieval to obtain a small shortlist of items over which personalized re-ranking can be done quickly. Despite being scalable, this strategy risks losing items uniquely relevant to a user that fail to get shortlisted during non-personalized retrieval. This paper bridges this gap by developing the XPERT algorithm that identifies a form of two-sided personalization that can be scalably implemented over millions of items and hundreds of millions of users. Key to overcoming the computational challenges of personalized retrieval is a novel concept of morph operators that can be used with arbitrary encoder architectures, completely avoids the steep memory overheads of two-sided personalization, provides millisecond-time inference and offers multi-intent retrieval. On multiple public and proprietary datasets, XPERT offered upto 5% superior recall and AUC than state-of-the-art techniques. Code for XPERT is available at https://github.com/personalizedretrieval/xpert.
Exploring User and Item Representation, Justification Generation, and Data Augmentation for Conversational Recommender Systems
Authors: Sergey Volokhin
Venue: SIGIR
Year: 2023

Conversational Recommender Systems (CRS) aim to provide personalized and contextualized recommendations through natural language conversations with users. The objective of my proposed dissertation is to capitalize on the recent developments in conversational interfaces to advance the field of Recommender Systems in several directions. I aim to address several problems in recommender systems: user and item representation, justification generation, and data sparsity. A critical challenge in CRS is learning effective representations of users and items that capture their preferences and characteristics. First, we focus on user representation, where we use a separate corpus of reviews to learn user representation. We attempt to map conversational users into the space of reviewers using semantic similarity between the conversation and the texts of reviews. Second, we improve item representation by incorporating textual features such as item descriptions into the user-item interaction graph, which captures a great deal of semantic and behavioral information unavailable from the purely topological structure of the interaction graph. Justifications for recommendations enhance the explainability and transparency of CRS; however, existing approaches, such as rule-based and template-based methods, have limitations. In this work, we propose an extractive method using a corpus of reviews to identify relevant information for generating concise and coherent justifications. We address the challenge of data scarcity for CRS by generating synthetic conversations using SOTA generative pre trained transformers (GPT). These synthetic conversations are used to augment the data used for training the CRS. In addition, we also evaluate if the GPTs exhibit emerging abilities of CRS (or a non-conversational RecSys) due to the large amount of data they are trained on, which potentially includes the reviews and opinions of users.
Single-shot Feature Selection for Multi-task Recommendations
Authors: Yejing Wang, Zhaocheng Du, Xiangyu Zhao, Bo Chen, Huifeng Guo, Ruiming Tang, Zhenhua Dong
Venue: SIGIR
Year: 2023

Multi-task Recommender Systems (MTRSs) has become increasingly prevalent in a variety of real-world applications due to their exceptional training efficiency and recommendation quality. However, conventional MTRSs often input all relevant feature fields without distinguishing their contributions to different tasks, which can lead to confusion and a decline in performance. Existing feature selection methods may neglect task relations or require significant computation during model training in multi-task setting. To this end, this paper proposes a novel Single-shot Feature Selection framework for MTRSs, referred to as MultiSFS, which is capable of selecting feature fields for each task while considering task relations in a single-shot manner. Specifically, MultiSFS first efficiently obtains task-specific feature importance through a single forward-backward pass. Then, a data-task bipartite graph is constructed to learn field-level task relations. Subsequently, MultiSFS merges the feature importance according to task relations and selects feature fields for different tasks. To demonstrate the effectiveness and properties of MultiSFS, we integrate it with representative MTRS models and evaluate on three real-world datasets. The implementation code is available online to ease reproducibility.