OpenRaiser/Intern-Atlas huggingface dataset:
M2EU: Meta Learning for Cold-start Recommendation via Enhancing User Preference Estimation
Authors: Zhenchao Wu, Xiao Zhou
Venue: SIGIR
Year: 2023

The cold-start problem is commonly encountered in recommender systems when delivering recommendations to users or items with limited interaction information and can seriously harm the performance of the system. To cope with this issue, meta-learning-based approaches have come to the rescue in recent years by enabling models to learn user preferences globally in the pre-training stage followed by local fine-tuning for a target user with only a few interactions. However, we argue that the user representation learned in this way may be inadequate to capture user preference well since solely utilizing his/her own interactions may be far from enough in cold-start scenarios. To tackle this problem, we propose a novel meta-learning method named M2EU to enrich the representations of cold-start users by incorporating the information from other similar users who are identified based on the similarity of both inherent attributes and historical interactions. In addition, we design an attention mechanism according to the variances of ratings in the aggregation of similar user embeddings. To further enhance the capability of user preference modeling, we devise different neural layers to generate user or item embeddings at the rating level and utilize the weight-sharing strategy to guarantee adequate parameters learning of neural layers in our meta-learning approach. In meta-training with mini-batching, we adopt an incremental learning scheme to learn a set of generalized parameters for all tasks. Experimental results on the public benchmark datasets demonstrate that M2EU outperforms state-of-the-art methods through extensive quantitative evaluations in various cold-start scenarios.
Improving News Recommendation via Bottlenecked Multi-task Pre-training
Authors: Xiongfeng Xiao, Qing Li, Songlin Liu, Kun Zhou
Venue: SIGIR
Year: 2023

Recent years have witnessed the boom of deep neural networks in online news recommendation service. As news articles mainly consist of textual content, pre-trained language models~(PLMs) (e.g. BERT) have been widely adopted as the backbone to encode them into news embeddings, which would be utilized to generate the user representations or perform the semantic matching. However, existing PLMs are mostly pre-trained on large-scale general corpus, and have not been specially adapted for capturing the rich information within news articles. Therefore, their produced news embeddings may be not informative enough to represent the news content or characterize the relations among news. To solve it, we propose a bottlenecked multi-task pre-training approach, which relies on an information-bottleneck encoder-decoder architecture to compress the useful semantic information into the news embedding. Concretely, we design three pre-training tasks, to enforce the news embedding to recover the news contents of itself, its frequently oc-occurring neighbours, and the news with similar topics. We conduct experiments on the MIND dataset and show that our approach can outperform competitive pre-training methods.
SimTDE: Simple Transformer Distillation for Sentence Embeddings
Authors: Jian Xie, Xin He, Jiyang Wang, Zimeng Qiu, Ali Kebarighotbi, Farhad Ghassemi
Venue: SIGIR
Year: 2023

In this paper we introduce SimTDE, a simple knowledge distillation framework to compress sentence embeddings transformer models with minimal performance loss and significant size and latency reduction. SimTDE effectively distills large and small transformers via a compact token embedding block and a shallow encoding block, connected with a projection layer, relaxing dimension match requirement. SimTDE simplifies distillation loss to focus only on token embedding and sentence embedding. We evaluate on standard semantic textual similarity (STS) tasks and entity resolution (ER) tasks. It achieves 99.94% of the state-of-the-art (SOTA) SimCSE-Bert-Base performance with 3 times size reduction and 96.99% SOTA performance with 12 times size reduction on STS tasks. It also achieves 99.57% of teacher's performance on multi-lingual ER data with a tiny transformer student model of 1.4M parameters and 5.7MB size. Moreover, compared to other distilled transformers SimTDE is 2 times faster at inference given similar size and still 1.17 times faster than a model 33% smaller (e.g. MiniLM). The easy-to-adopt framework, strong accuracy and low latency of SimTDE can widely enable runtime deployment of SOTA sentence embeddings.
Decoupled Hyperbolic Graph Attention Network for Cross-domain Named Entity Recognition
Authors: Jingyun Xu, Yi Cai
Venue: SIGIR
Year: 2023

To address the scarcity of massive labeled data, cross-domain named entity recognition (cross-domain NER) attracts increasing attention. Recent studies focus on decomposing NER into two separate tasks (i.e., entity span detection and entity type classification) to reduce the complexity of the cross-domain transfer. Despite the promising results, there still exists room for improvement. In particular, the rich domain-shared syntactic and semantic information, which are respectively important for entity span detection and entity type classification, are still underutilized. In light of these two challenges, we propose applying graph attention networks (GATs) to encode the above two kinds of information. Moreover, considering that GATs mainly operate in the Euclidean space, which may fail to capture the latent hierarchical relations among words for learning high-quality word representations, we further propose to embed words into Hyperbolic spaces. Finally, a decouple hyperbolic graph attention network (DH-GAT) is introduced for cross-domain NER. Empirical results on 10 domain pairs show that DH-GAT achieves state-of-the-art performance on several standard metrics, and further analyses are presented to better understand each component's effectiveness.
A Lightweight Constrained Generation Alternative for Query-focused Summarization
Authors: Zhichao Xu, Daniel Cohen
Venue: SIGIR
Year: 2023

Query-focused summarization (QFS) aims to provide a summary of a document that satisfies information need of a given query and is useful in various IR applications, such as abstractive snippet generation. Current QFS approaches typically involve injecting additional information, e.g. query-answer relevance or fine-grained token-level interaction between a query and document, into a finetuned large language model. However, these approaches often require extra parameters & training, and generalize poorly to new dataset distributions. To mitigate this, we propose leveraging a recently developed constrained generation model Neurological Decoding (NLD) as an alternative to current QFS regimes which rely on additional sub-architectures and training. We first construct lexical constraints by identifying important tokens from the document using a lightweight gradient attribution model, then subsequently force the generated summary to satisfy these constraints by directly manipulating the final vocabulary likelihood. This lightweight approach requires no additional parameters or finetuning as it utilizes both an off-the-shelf neural retrieval model to construct the constraints and a standard generative language model to produce the QFS. We demonstrate the efficacy of this approach on two public QFS collections achieving near parity with the state-of-the-art model with substantially reduced complexity.
Towards a More User-Friendly and Easy-to-Use Benchmark Library for Recommender Systems
Authors: Lanling Xu, Zhen Tian, Gaowei Zhang, Junjie Zhang, Lei Wang, Bowen Zheng, Yifan Li, Jiakai Tang, Zeyu Zhang, Yupeng Hou, Xingyu Pan, Wayne Xin Zhao, Xu Chen, Ji-Rong Wen
Venue: SIGIR
Year: 2023

In recent years, the reproducibility of recommendation models has become a severe concern in recommender systems. In light of this challenge, we have previously released a unified, comprehensive and efficient recommendation library called RecBole, attracting much attention from the research community. With the increasing number of users, we have received a number of suggestions and update requests. This motivates us to make further improvements on our library, so as to meet the user requirements and contribute to the research community. In this paper, we present a significant update of RecBole, making it more user-friendly and easy-to-use as a comprehensive benchmark library for recommendation. More specifically, the highlights of this update are summarized as: (1) we include more benchmark models and datasets, improve the benchmark framework in terms of data processing, training and evaluation, and release reproducible configurations to benchmark the recommendation models; (2) we upgrade the user friendliness of our library by providing more detailed documentation and well-organized frequently asked questions, and (3) we propose several development guidelines for the open-source library developers. These extensions make it much easier to reproduce the benchmark results and stay up-to-date with the recent advances on recommender systems. Our update is released at the link: https://github.com/RUCAIBox/RecBole.
Multi-behavior Self-supervised Learning for Recommendation
Authors: Jingcao Xu, Chaokun Wang, Cheng Wu, Yang Song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou, Kun Gai
Venue: SIGIR
Year: 2023

Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understand and leverage multi-behavior data from users. Despite recent efforts towards making use of heterogeneous data, multi-behavior recommendation still faces great challenges. Firstly, sparse target signals and noisy auxiliary interactions remain an issue. Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task. Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework together with an adaptive optimization method. Specifically, we devise a behavior-aware graph neural network incorporating the self-attention mechanism to capture behavior multiplicity and dependencies. To increase the robustness to data sparsity under the target behavior and noisy interactions from auxiliary behaviors, we propose a novel self-supervised learning paradigm to conduct node self-discrimination at both inter-behavior and intra-behavior levels. In addition, we develop a customized optimization strategy through hybrid manipulation on gradients to adaptively balance the self-supervised learning task and the main supervised recommendation task. Extensive experiments on five real-world datasets demonstrate the consistent improvements obtained by MBSSL over ten state-of-the-art (SOTA) baselines. We release our model implementation at: https://github.com/Scofield666/MBSSL.git.
Faster Dynamic Pruning via Reordering of Documents in Inverted Indexes
Authors: Erman Yafay, Ismail Sengor Altingovde
Venue: SIGIR
Year: 2023

Widely used dynamic pruning algorithms (such as MaxScore, WAND and BMW) keep track of the k-th highest score (i.e., heap threshold) among the documents that are scored so far, to avoid scoring the documents that cannot get into the top-k result list. Obviously, the faster the heap threshold converges to its final value, the larger will be the number of skipped documents and hence, the efficiency gains of the pruning algorithms. In this paper, we tailor approaches that reorder the documents in the inverted index based on their access counts and ranks for previous queries. By storing such frequently retrieved documents at front of the postings lists, we aim to compute the heap threshold earlier during the query processing. Our approach yields substantial speedups (up to 1.33x) for all three dynamic pruning algorithms and outperforms two strong baselines that have been employed for document reordering in the literature.
Review-based Multi-intention Contrastive Learning for Recommendation
Authors: Wei Yang, Tengfei Huo, Zhiqiang Liu, Chi Lu
Venue: SIGIR
Year: 2023

Real recommendation systems contain various features, which are often high-dimensional, sparse, and difficult to learn effectively. In addition to numerical features, user reviews contain rich semantic information including user preferences, which are used as auxiliary features by researchers. The methods of supplementing data features based on reviews have certain effects. However, most of them simply concatenate review representations and other features together, without considering that the text representation contains a lot of noise information. In addition, the important intentions contained in user reviews are not modeled effectively. In order to solve the above problems, we propose a novel Review-based Multi-intention Contrastive Learning (RMCL) method. In detail, RMCL proposes an intention representation method based on mixed Gaussian distribution hypothesis. Further, RMCL adopts a multi-intention contrastive strategy, which establishes a fine-grained connection between user reviews and item reviews. Extensive experiments on five real-world datasets demonstrate significant improvements of our proposed RMCL model over the state-of-the-art methods.
Neural Methods for Cross-Language Information Retrieval
Authors: Eugene Yang, Dawn J. Lawrie, James Mayfield, Suraj Nair, Douglas W. Oard
Venue: SIGIR
Year: 2023

This half day tutorial introduces the participant to the basic concepts underlying neural Cross-Language Information Retrieval (CLIR). It discusses the most common algorithmic approaches to CLIR, focusing on modern neural methods; the history of CLIR; where to find and how to use CLIR training collections, test collections and baseline systems; how CLIR training and test collections are constructed; and open research questions in CLIR.
DCBT: A Simple But Effective Way for Unified Warm and Cold Recommendation
Authors: Jieyu Yang, Liang Zhang, Yong He, Ke Ding, Zhaoxin Huan, Xiaolu Zhang, Linjian Mo
Venue: SIGIR
Year: 2023

The cold-start problem of conversion rate prediction is a common challenge in online advertising systems. To alleviate this problem, a large number of methods either use content information or uncertainty methods, or use meta-learning based methods to improve the ranking performance of cold-start items. However, they can work for cold-start scenarios but fail to adaptively unify warm and cold recommendations into one model, requiring additional human efforts or knowledge to adapt to different scenarios. Meanwhile, none of them pay attention to the discrepancy between model predictions and true likelihoods of cold items, while over- or under-estimation is harmful to the ROI (Return on Investment) of advertising placements. In this paper, in order to address the above issues, we propose a framework called Distribution-Constrained Batch Transformer (DCBT). Specifically, the framework introduces a Transformer module into the batch dimension to automatically choose proper information from warm samples to enhance the representation of cold samples and preserve the property of warm samples. In addition, to avoid the distribution of cold samples being affected by the warm samples, the framework adds MMD loss to constrain the sample distribution before and after feeding into the Transformer module. Extensive offline experiments on two real-world datasets show that our proposed method attains state-of-the-art performance in AUC and PCOC (Predicted CVR over CVR) for cold items and warm items. An online A/B test demonstrates that the DCBT model obtained a 20.08% improvement in CVR and a 13.21% increase in GMV (Gross Merchandise Volume).
Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning
Authors: Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li
Venue: SIGIR
Year: 2023

Table-based reasoning has shown remarkable progress in a wide range of table-based tasks. It is a challenging task, which requires reasoning over both free-form natural language (NL) questions and (semi-)structured tabular data. However, previous table-based reasoning solutions usually suffer from significant performance degradation on ''huge'' evidence (tables). In addition, most existing methods struggle to reason over complex questions since the essential information is scattered in different places. To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning, and (ii) decompose a complex question into simpler sub-questions for text reasoning. First, we use a powerful LLM to decompose the evidence involved in the current question into the sub-evidence that retains the relevant information and excludes the remaining irrelevant information from the ''huge'' evidence. Second, we propose a novel ''parsing-execution-filling'' strategy to decompose a complex question into simper step-by-step sub-questions by generating intermediate SQL queries as a bridge to produce numerical and logical sub-questions with a powerful LLM. Finally, we leverage the decomposed sub-evidence and sub-questions to get the final answer with a few in-context prompting examples. Extensive experiments on three benchmark datasets (TabFact, WikiTableQuestion, and FetaQA) demonstrate that our method achieves significantly better results than competitive baselines for table-based reasoning. Notably, our method outperforms human performance for the first time on the TabFact dataset. In addition to impressive overall performance, our method also has the advantage of interpretability, where the returned results are to some extent tractable with the generated sub-evidence and sub-questions. For reproducibility, we release our source code and data at: https://github.com/AlibabaResearch/DAMO-ConvAI.
A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data
Authors: Wenting Ye, Hongfei Yang, Shuai Zhao, Haoyang Fang, Xingjian Shi, Naveen Neppalli
Venue: SIGIR
Year: 2023

The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing research typically uses customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute recommendations into language matching problem. It takes the product title description as model input to consider product functionality. We design a new transformation method to de-noise the signals derived from production data. In addition, we consider multilingual support from the engineering point of view. Our proposed end-to-end transformer-based model achieves both successes from offline and online experiments. The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.
OpenMatch-v2: An All-in-one Multi-Modality PLM-based Information Retrieval Toolkit
Authors: Shi Yu, Zhenghao Liu, Chenyan Xiong, Zhiyuan Liu
Venue: SIGIR
Year: 2023

Pre-trained language models (PLMs) have emerged as the foundation of the most advanced Information Retrieval (IR) models. Powered by PLMs, the latest IR research has proposed novel models, new domain adaptation algorithms as well as enlarged datasets. In this paper, we present a Python-based IR toolkit OpenMatch-v2. As a full upgrade of OpenMatch proposed in 2021, OpenMatch-v2 incorporates the most recent advancements of PLM-based IR research, providing support for new, cross-modality models and enhanced domain adaptation techniques with a streamlined, optimized infrastructure. The code of OpenMatch is publicly available at https://github.com/OpenMatch/OpenMatch.
Reducing Spurious Correlations for Relation Extraction by Feature Decomposition and Semantic Augmentation
Authors: Tianshu Yu, Min Yang, Chengming Li, Ruifeng Xu
Venue: SIGIR
Year: 2023

Deep neural models have become mainstream in relation extraction (RE), yielding state-of-the-art performance. However, most existing neural models are prone to spurious correlations between input features and prediction labels, making the models suffer from low robustness and generalization.In this paper, we propose a spurious correlation reduction method for RE via feature decomposition and semantic augmentation (denoted as FDSA). First, we decompose the original sentence representation into class-related features and context-related features. To obtain better context-related features, we devise a contrastive learning method to pull together the context-related features of the anchor sentence and its augmented sentences, and push away the context-related features of different anchor sentences. In addition, we propose gradient-based semantic augmentation on context-related features in order to improve the robustness of the RE model. Experiments on four datasets show that our model outperforms the strong competitors.
Hydrus: Improving Personalized Quality of Experience in Short-form Video Services
Authors: Zhiyu Yuan, Kai Ren, Gang Wang, Xin Miao
Venue: SIGIR
Year: 2023

Traditional approaches to improving users' quality of experience (QoE) focus on minimizing the latency on the server side. Through an analysis of 15 million users, however, we find that for short-form video apps, user experience depends on both response latency and recommendation accuracy. This observation brings a dilemma to service providers since improving recommendation accuracy requires adopting complex strategies that demand heavy computation, which substantially increases response latency. Our motivation is that users' sensitivity to response latency and recommendation accuracy varies greatly. In other words, some users would accept a 20ms increase in latency to enjoy higher-quality videos, while others prioritize minimizing lag above all else. Inspired by this, we present Hydrus, a novel resource allocation system that delivers the best possible personalized QoE by making tradeoffs between response latency and recommendation accuracy. Specifically, we formulate the resource allocation problem as a utility maximization problem, and Hydrus is guaranteed to solve the problem within a few milliseconds. We demonstrate the effectiveness of Hydrus through offline simulation and online experiments in Kuaishou, a massively popular video app with hundreds of millions of users worldwide. The results show that Hydrus can increase QoE by 35.6% with the same latency or reduce the latency by 10.1% with the same QoE. Furthermore, Hydrus can achieve 54.5% higher throughput without a decrease in QoE. In online A/B testing, Hydrus significantly improves click-through rate (CTR) and watch time; it can also reduce system resource costs without sacrificing QoE.