OpenRaiser/Intern-Atlas huggingface dataset:
Fresh Content Recommendation at Scale: A Multi-funnel Solution and the Potential of LLMs
Authors: Jianling Wang, Haokai Lu, Minmin Chen
Venue: WSDM
Year: 2024

Recommendation system serves as a conduit connecting users to an incredibly large, diverse and ever growing collection of contents. In practice, missing information on fresh contents needs to be filled in order for them to be exposed and discovered by their audience. In this context, we are delighted to share our success stories in building a dedicated fresh content recommendation stack on a large commercial platform and also shed a light on the utilization of Large Language Models (LLMs) for fresh content recommendations within an industrial framework. To nominate fresh contents, we built a multi-funnel nomination system that combines (i) a two-tower model with strong generalization power for coverage, and (ii) a sequence model with near real-time update on user feedback for relevance, which effectively balances between coverage and relevance. Beyond that, by harnessing the reasoning and generalization capabilities of LLMs, we are presented with exciting prospects to enhance recommendation systems. We share our initial efforts on employing LLMs as data augmenters to bridge the knowledge gap on cold-start items during the training phase. This innovative approach circumvents the costly generation process during inference, presenting a model-agnostic, forward-looking solution for fresh content recommendation.
CityCAN: Causal Attention Network for Citywide Spatio-Temporal Forecasting
Authors: Chengxin Wang, Yuxuan Liang, Gary Tan
Venue: WSDM
Year: 2024

Citywide spatio-temporal (ST) forecasting is a fundamental task for many urban applications, including traffic accident prediction, taxi demand planning, and crowd flow forecasting. The goal of this task is to generate accurate predictions concurrently for all regions within a city. Prior works take great effort on modeling the ST correlations. However, they often overlook intrinsic correlations and inherent data distribution across the city, both of which are influenced by urban zoning and functionality, resulting in inferior performance on citywide ST forecasting. In this paper, we introduce CityCAN, a novel causal attention network, to collectively generate predictions for every region of a city. We first present a causal framework to identify useful correlations among regions, filtering out useless ones, via an intervention strategy. In the framework, a Global Local-Attention Encoder, which leverages attention mechanisms, is designed to jointly learn both local and global ST correlations among correlated regions. Then, we design a citywide loss to constrain the prediction distribution by incorporating the citywide distribution. Extensive experiments on three real-world applications demonstrate the effectiveness of CityCAN.
FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes
Authors: Haonan Wang, Ziwei Wu, Jingrui He
Venue: WSDM
Year: 2024

Empirical loss minimization during machine learning training can inadvertently introduce bias, stemming from discrimination and societal prejudices present in the data. To address the shortcomings of traditional fair machine learning methods-which often rely on sensitive information of training data or mandate significant model alterations-we present FairIF, a unique two-stage training framework. Distinctly, FairIF enhances fairness by recalibrating training sample weights using the influence function. Notably, it employs sensitive information from a validation set, rather than the training set, to determine these weights. This approach accommodates situations with missing or inaccessible sensitive training data. Our FairIF ensures fairness across demographic groups by retraining models on the reweighted data. It stands out by offering a plug-and-play solution, obviating the need for changes in model architecture or the loss function. We demonstrate that the fairness performance of FairIF is guaranteed during testing with only a minimal impact on classification performance. Additionally, we analyze that our framework adeptly addresses issues like group size disparities, distribution shifts, and class size discrepancies. Empirical evaluations on three synthetic and five real-world datasets across six model architectures confirm FairIF's efficiency and scalability. The experimental results indicate superior fairness-utility trade-offs compared to other methods, regardless of bias types or architectural variations. Moreover, the adaptability of FairIF to utilize pretrained models for subsequent tasks and its capability to rectify unfairness originating during the pretraining phase are further validated through our experiments.
Cost-Effective Active Learning for Bid Exploration in Online Advertising
Authors: Zixiao Wang, Zhenzhe Zheng, Yanrong Kang, Jiani Huang
Venue: WSDM
Year: 2024

As a bid optimization algorithm in the first-price auction (FPA), bid shading is used in online advertising to avoid overpaying for advertisers. However, we find the bid shading approach would incur serious local optima. This effect prevents the advertisers from maximizing long-term surplus. In this work, we identify the reasons behind this local optima - it comes from the lack of winning price information, which results in the conflict between short-term surplus and the winning rate prediction model training, and is further propagated through the over-exploitation of the model. To rectify this problem, we propose a cost-effective active learning strategy, namely CeBE, for bid exploration. Specifically, we comprehensively consider the uncertainty and density of samples to calculate exploration utility, and use a 2+ε-approximation greedy algorithm to control exploration costs. Instead of selecting bid prices that maximize the expected surplus for all bid requests, we employ the bid exploration strategy to determine the bid prices. By trading off a portion of surplus, we can train the model using higher-quality data to enhance its performance, enabling the system to achieve a long-term surplus. Our method is straightforward and applicable to real-world industrial environment: it is effective across various categories of winning rate prediction models. We conducted empirical studies to validate the efficacy of our approach. In comparison to the traditional bid shading system, CeBE can yield an average surplus improvement of 8.16% across various models and datasets.
Towards Better Chinese Spelling Check for Search Engines: A New Dataset and Strong Baseline
Authors: Yue Wang, Zilong Zheng, Zecheng Tang, Juntao Li, Zhihui Liu, Kunlong Chen, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Min Zhang
Venue: WSDM
Year: 2024

Misspellings in search engine queries may prevent search engines from returning accurate results. For Chinese mobile search engines, due to the different input methods (e.g., hand-written and T9 input methods), more types of misspellings exist, making this problem more challenging. As an essential module of search engines, Chinese Spelling Check~(CSC) models aim to detect and correct misspelled Chinese characters from user-issued queries. Despite the great value of CSC to the search engine, there is no CSC benchmark collected from real-world search engine queries. To fill this blank, we construct and release the Alipay Search Engine Query (AlipaySEQ) spelling check dataset. To the best of our knowledge, AlipaySEQ is the first Chinese Spelling Check dataset collected from the real-world scenario of Chinese mobile search engines. It consists of 15,522 high-quality human annotated and 1,175,151 automatically generated samples. To demonstrate the unique challenges of AlipaySEQ in the era of Large Language Models~(LLMs), we conduct a thorough study to analyze the difference between AlipaySEQ and existing SIGHAN benchmarks and compare the performance of various baselines, including existing task-specific methods and LLMs. We observe that all baselines fail to perform satisfactorily due to the over-correction problem. Especially, LLMs exhibit below-par performance on AlipaySEQ, which is rather surprising. Therefore, to alleviate the over-correction problem, we introduce a model-agnostic CSC Self-Refine Framework (SRF) to construct a strong baseline. Comprehensive experiments demonstrate that our proposed SRF, though more effective against existing models on both the AlipaySEQ and SIGHAN15, is still far from achieving satisfactory performance on our real-world dataset. With the newly collected real-world dataset and strong baseline, we hope more progress can be achieved on such a challenging and valuable task.
Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels
Authors: Fali Wang, Tianxiang Zhao, Suhang Wang
Venue: WSDM
Year: 2024

Few-shot node classification poses a significant challenge for Graph Neural Networks (GNNs) due to insufficient supervision and potential distribution shifts between labeled and unlabeled nodes. Self-training has emerged as a widely popular framework to leverage the abundance of unlabeled data, which expands the training set by assigning pseudo-labels to selected unlabeled nodes. Efforts have been made to develop various selection strategies based on confidence, information gain, etc. However, none of these methods takes into account the distribution shift between the training and testing node sets. The pseudo-labeling step may amplify this shift and even introduce new ones, hindering the effectiveness of self-training. Therefore, in this work, we explore the potential of explicitly bridging the distribution shift between the expanded training set and test set during self-training. To this end, we propose a novel Distribution-Consistent Graph Self-Training (DC-GST) framework to identify pseudo-labeled nodes that both are informative and capable of redeeming the distribution discrepancy and formulate it as a differentiable optimization task. A distribution-shift-aware edge predictor is further adopted to augment the graph and increase the model's generalizability in assigning pseudo labels. We evaluate our proposed method on four publicly available benchmark datasets and extensive experiments demonstrate that our framework consistently outperforms state-of-the-art baselines.
LLMRec: Large Language Models with Graph Augmentation for Recommendation
Authors: Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, Chao Huang
Venue: WSDM
Year: 2024

The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as noise, availability issues, and low data quality, which in turn hinder the accurate modeling of user preferences and adversely impact recommendation performance. In light of the recent advancements in large language models (LLMs), which possess extensive knowledge bases and strong reasoning capabilities, we propose a novel framework called LLMRec that enhances recommender systems by employing three simple yet effective LLM-based graph augmentation strategies. Our approach leverages the rich content available within online platforms (e.g., Netflix, MovieLens) to augment the interaction graph in three ways: (i) reinforcing user-item interaction egde, (ii) enhancing the understanding of item node attributes, and (iii) conducting user node profiling, intuitively from the natural language perspective. By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders. Besides, to ensure the quality of the augmentation, we develop a denoised data robustification mechanism that includes techniques of noisy implicit feedback pruning and MAE-based feature enhancement that help refine the augmented data and improve its reliability. Furthermore, we provide theoretical analysis to support the effectiveness of LLMRec and clarify the benefits of our method in facilitating model optimization. Experimental results on benchmark datasets demonstrate the superiority of our LLM-based augmentation approach over state-of-the-art techniques. To ensure reproducibility, we have made our code and augmented data publicly available at: https://github.com/HKUDS/LLMRec.git.
AutoPooling: Automated Pooling Search for Multi-valued Features in Recommendations
Authors: He Wei, Yuekui Yang, Shaoping Ma, Haiyang Wu, Yangyang Tang, Meixi Liu, Yang Zhang
Venue: WSDM
Year: 2024

Large-scale recommender systems usually contain hundreds of multi-valued feature fields, which have different number of values in each field. For easier computation in traditional fixed-shape neural networks, pooling operators are widely used to compress the multi-valued feature into a fixed-dimension vector. Most existing works set a single pooling method for all fields, but this leads to sub-optimal results, because different feature fields have different information distributions and thus require different pooling methods. In this work, we propose an AutoML-based framework, called AutoPooling, which can automatically and efficiently search for the optimal pooling operator for each multivalued feature. Specifically, learnable weights are assigned to all candidate pooling operators in each feature field. Then an AutoML-based algorithm is used to learn both model parameters and the field-aware weights. Finally, the optimal pooling operator can be acquired based on the associated weights. We evaluate the proposed framework on both public and industrial datasets. The results show that AutoPooling significantly outperforms the benchmarks. Further experiment results show that our method is robust in various deep recommend models and different search spaces.
Unified Visual Preference Learning for User Intent Understanding
Authors: Yihua Wen, Si Chen, Yu Tian, Wanxian Guan, Pengjie Wang, Hongbo Deng, Jian Xu, Bo Zheng, Zihao Li, Lixin Zou, Chenliang Li
Venue: WSDM
Year: 2024

In the world of E-Commerce, the core task is to understand the personalized preference from various kinds of heterogeneous information, such as textual reviews, item images and historical behaviors. In current systems, these heterogeneous information are mainly exploited to generate better item or user representations. For example, in scenario of visual search, the importance of modeling query image has been widely acknowledged. But, these existing solutions focus on improving the representation quality of the query image, overlooking the personalized visual preference of the user. Note that the visual features affect the user's decision significantly, e.g., the user could be more likely to click the items with her preferred design. Hence, it is fruitful to exploit the visual preference to deliver better capacity for personalization. To this end, we propose a simple yet effective target-aware visual preference learning framework (named Tavern) for both item recommendation and search. The proposed Tavern works as an individual and generic model that can be smoothly plugged into different downstream systems. Specifically, for visual preference learning, we utilize the image of the target item to derive the visual preference signals for each historical clicked item. This procedure is modeled as a form of representation disentanglement, where the visual preference signals are extracted by taking off the noisy information irrelevant to visual preference from the shared visual information between the target and historical items. During this process, a novel selective orthogonality disentanglement is proposed to avoid the significant information loss. Then, a GRU network is utilized to aggregate these signals to form the final visual preference representation. Extensive experiments over three large-scale real-world datasets covering visual search, product search and recommendation well demonstrate the superiority of our proposed Tavern against existing technical alternatives. Further ablation study also confirms the validity of each design choice.
Vector Search with OpenAI Embeddings: Lucene Is All You Need
Authors: Jasper Xian, Tommaso Teofili, Ronak Pradeep, Jimmy Lin
Venue: WSDM
Year: 2024

We provide a reproducible, end-to-end demonstration of vector search with OpenAI embeddings using Lucene on the popular MS MARCO passage ranking test collection. The main goal of our work is to challenge the prevailing narrative that a dedicated vector store is necessary to take advantage of recent advances in deep neural networks as applied to search. Quite the contrary, we show that hierarchical navigable small-world network (HNSW) indexes in Lucene are adequate to provide vector search capabilities in a standard bi-encoder architecture. This suggests that, from a simple cost-benefit analysis, there does not appear to be a compelling reason to introduce a dedicated vector store into a modern "AI stack" for search, since such applications have already received substantial investments in existing, widely deployed infrastructure.
Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation
Authors: Zhibo Xiao, Luwei Yang, Tao Zhang, Wen Jiang, Wei Ning, Yujiu Yang
Venue: WSDM
Year: 2024

The recommendation has been playing a key role in many industries, e.g., e-commerce, streaming media, social media, etc. Recently, a new recommendation scenario, called Trigger-Induced Recommendation (TIR), where users are able to explicitly express their instant interests via trigger items, is emerging as an essential role in many e-commerce platforms, e.g., Alibaba.com and Amazon. Without explicitly modeling the user's instant interest, traditional recommendation methods usually obtain sub-optimal results in TIR. Even though there are a few methods considering the trigger and target items simultaneously to solve this problem, they still haven't taken into account temporal information of user behaviors, the dynamic change of user instant interest when the user scrolls down and the interactions between the trigger and target items. To tackle these problems, we propose a novel method -- Deep Evolutional Instant Interest Network (DEI2N), for click-through rate prediction in TIR scenarios. Specifically, we design a User Instant Interest Modeling Layer to predict the dynamic change of the intensity of instant interest when the user scrolls down. Temporal information is utilized in user behavior modeling. Moreover, an Interaction Layer is introduced to learn better interactions between the trigger and target items. We evaluate our method on several offline and real-world industrial datasets. Experimental results show that our proposed DEI2N outperforms state-of-the-art baselines. In addition, online A/B testing demonstrates the superiority over the existing baseline in real-world production environments.
Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters
Authors: Yukang Xie, Chengyu Wang, Junbing Yan, Jiyong Zhou, Feiqi Deng, Jun Huang
Venue: WSDM
Year: 2024

Recently, Large Language Models (LLMs) have achieved amazing zero-shot learning performance over a variety of Natural Language Processing (NLP) tasks, especially for text generative tasks. Yet, the large size of LLMs often leads to the high computational cost of model training and online deployment. In our work, we present ALTER, a system that effectively builds the multi-tAsk Learners with mixTure-of-task-adaptERs upon small language models (with <1B parameters) to address multiple NLP tasks simultaneously, capturing the commonalities and differences between tasks, in order to support domain-specific applications. Specifically, in ALTER, we propose the Mixture-of-Task-Adapters (MTA) module as an extension to the transformer architecture for the underlying model to capture the intra-task and inter-task knowledge. A two-stage training method is further proposed to optimize the collaboration between adapters at a small computational cost. Experimental results over a mixture of NLP tasks show that our proposed MTA architecture and the two-stage training method achieve good performance. Based on ALTER, we have also produced MTA-equipped language models for various domains.
Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding
Authors: Hongshen Xu, Lu Chen, Zihan Zhao, Da Ma, Ruisheng Cao, Zichen Zhu, Kai Yu
Venue: WSDM
Year: 2024

The growing prevalence of visually rich documents, such as webpages and scanned/digital-born documents (images, PDFs, etc.), has led to increased interest in automatic document understanding and information extraction across academia and industry. Although various document modalities, including image, text, layout, and structure, facilitate human information retrieval, the interconnected nature of these modalities presents challenges for neural networks. In this paper, we introduce WebLM, a multimodal pre-training network designed to address the limitations of solely modeling text and structure modalities of HTML in webpages. Instead of processing document images as unified natural images, WebLM integrates the hierarchical structure of document images to enhance the understanding of markup-language-based documents. Additionally, we propose several pre-training tasks to model the interaction among text, structure, and image modalities effectively. Empirical results demonstrate that the pre-trained WebLM significantly surpasses previous state-of-the-art pre-trained models across several webpage understanding tasks. The pre-trained models and code are available at https://github.com/X-LANCE/weblm.
Towards Alignment-Uniformity Aware Representation in Graph Contrastive Learning
Authors: Rong Yan, Peng Bao, Xiao Zhang, Zhongyi Liu, Hui Liu
Venue: WSDM
Year: 2024

Graph Contrastive Learning (GCL) methods benefit from two key properties: alignment and uniformity, which encourage the representation of related objects together while pushing apart different objects. Most GCL methods aim to preserve alignment and uniformity through random graph augmentation strategies and indiscriminately negative sampling. However, their performance is highly sensitive to graph augmentation, which requires cumbersome trial-and-error and expensive domain-specific knowledge as guidance. Besides, these methods perform negative sampling indiscriminately, which inevitably suffers from sampling bias, i.e., negative samples from the same class as the anchor. To remedy these issues, we propose a unified GCL framework towards Alignment-Uniformity Aware Representation learning (AUAR), which can achieve better alignment while improving uniformity without graph augmentation and negative sampling. Specifically, we propose intra- and inter-alignment loss to align the representations of the node with itself and its cluster centroid to maintain label-invariant. Furthermore, we introduce a uniformity loss with theoretical analysis, which pushes the representations of unrelated nodes from different classes apart and tends to provide informative variance from different classes. Extensive experiments demonstrate that our method gains better performance than existing GCL methods in node classification and clustering tasks across three widely-used datasets.