Welcome to the 46th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023), taking place in Taipei, Taiwan, from July 23-27, 2023.
As the premier scientific conference in the expansive field of information retrieval, SIGIR 2023 is thoughtfully organized as a hybrid event in the aftermath of the COVID-19 pandemic. While we strongly advocate for in-person participation, we also offer virtual attendance options. This arrangement not only contributes to reducing carbon footprints but also mitigates travel complexities for individuals from certain regions. Furthermore, this edition sets a significant milestone not only for its traditional and innovative tracks, but also as a response to the SIGIR Executive Committee's prompt to actively integrate sustainable development considerations into its organization and implementation. In a pioneering attempt to incorporate the theme of sustainability, SIGIR 2023 is carrying out a carbon emissions survey, hoping it can serve as a reference for subsequent conferences.
Multi-lingual Semantic Search for Domain-specific Applications: Adobe Photoshop and Illustrator Help Search
Search has become an integral part of Adobe products and users rely on it to learn about tool usage, shortcuts, quick links, and ways to add creative effects and to find assets such as backgrounds, templates, and fonts. Within applications such as ...
Bootstrapping Query Suggestions in Spotify's Instant Search System
Instant search systems present results to the user at every keystroke. This type of search system works best when the query ambiguity is low, the catalog is limited, and users know what they are looking for. However, Spotify's catalog is large and ...
COUPA: An Industrial Recommender System for Online to Offline Service Platforms
Aiming at helping users locally discover retail services (e.g., entertainment and dining) on Online to Offline (O2O) service platforms, we propose COUPA, an industrial system targeting for characterizing user preference with inspiring considerations of ...
A Consumer Compensation System in Ride-hailing Service
In the ride-hailing business, compensation is mostly used to motivate consumers to place more orders and grow the market scale. However, most of the previous studies focus on car-hailing services. Few works investigate localized smart transportation ...
Interactive Recommendation System for Meituan Waimai
As the largest local retail & instant delivery platform in China, Meituan Waimai has deployed a personalized recommender system on server and recommend nearby stores to users through APP homepage. To capture real-time intention of users and flexibly ...
Integrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network Search
Embedding based retrieval has seen its usage in a variety of search applications like e-commerce, social networking search etc. While the approach has demonstrated its efficacy in tasks like semantic matching and contextual search, it is plagued by the ...
Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level Generation
End-to-end generation-based approaches have been investigated and applied in task-oriented dialogue systems. However, in industrial scenarios, existing methods face the bottlenecks of reliability (e.g., domain-inconsistent responses, repetition problem, ...
Long-Form Information Retrieval for Enterprise Matchmaking
Understanding customer requirements is a key success factor for both business-to-consumer (B2C) and business-to-business (B2B) enterprises. In a B2C context, most requirements are directly related to products and therefore expressed in keyword-based ...
Learning Query-aware Embedding Index for Improving E-commerce Dense Retrieval
The embedding index has become an essential part of the dense retrieval (DR) system, which enables a fast search for billion of items in online E-commerce applications. To accelerate the retrieval process in industrial scenarios, most of the previous ...
A Practical Online Allocation Framework at Industry-scale in Constrained Recommendation
Online allocation is a critical challenge in constrained recommendation systems, where the distribution of goods, ads, vouchers, and other content to users with limited resources needs to be managed effectively. While the existing literature has made ...
TMML: Text-Guided MuliModal Product Location For Alleviating Retrieval Inconsistency in E-Commerce
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 ...
MDI: A Debiasing Method Combining Unbiased and Biased Data
In recent years, many methods have been proposed to alleviate the biases in recommender systems by combining biased data and unbiased data. Among these methods, data imputation method is effective, but previous works only employ a straightforward model ...
Context-Aware Classification of Legal Document Pages
For many business applications that require the processing, indexing, and retrieval of professional documents such as legal briefs (in PDF format etc.), it is often essential to classify the pages of any given document into their corresponding types ...
Facebook Content Search: Efficient and Effective Adapting Search on A Large Scale
Facebook content search is a critical channel that enables people to discover the best content to deepen their engagement with friends and family, creators, and communities. Building a highly personalized search engine to serve billions of daily active ...
Practice and Challenges in Building a Business-oriented Search Engine Quality Metric
One of the most challenging aspects of operating a large-scale web search engine is to accurately evaluate and monitor the search engine's result quality regardless of search types. From a business perspective, in the face of such challenges, it is ...
Building a Graph-Based Patent Search Engine
Performing prior art searches is an essential step in both patent drafting and invalidation. The task is challenging due to the large number of existing patent documents and the domain knowledge required to analyze the documents. We present a graph-based ...
A Data-centric Solution to Improve Online Performance of Customer Service Bots
The online performance of customer service bots is often less than satisfactory because of the gap between limited training data and real-world user questions. As a straightforward way to improve online performance, model iteration and re-deployment are ...
Enhancing Dynamic Image Advertising with Vision-Language Pre-training
In the multimedia era, image becomes an effective medium in search advertising. Dynamic Image Advertising (DIA), a system that matches queries with appropriate ad images and generates multimodal ads, is introduced to improve user experience and ad ...
Graph Enhanced BERT for Query Understanding
Query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. However, it is inherently challenging since it needs to capture semantic information from short and ambiguous queries ...
KATIE: A System for Key Attributes Identification in Product Knowledge Graph Construction
We present part of Huawei's efforts in building a Product Knowledge Graph (PKG). We want to identify which product attributes (i.e. properties) are relevant and important in terms of shopping decisions to product categories (i.e. classes). This is ...
A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data
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 ...
Embedding Based Retrieval in Friend Recommendation
- Jiahui Shi,
- Vivek Chaurasiya,
- Yozen Liu,
- Shubham Vij,
- Yan Wu,
- Satya Kanduri,
- Neil Shah,
- Peicheng Yu,
- Nik Srivastava,
- Lei Shi,
- Ganesh Venkataraman,
- Jun Yu
Friend recommendation systems in online social and professional networks such as Snapchat helps users find friends and build connections, leading to better user engagement and retention. Traditional friend recommendation systems take advantage of the ...
Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities
Virtual assistants are becoming increasingly important speech-driven Information Retrieval platforms that assist users with various tasks. We discuss open problems and challenges with respect to modeling spoken information queries for virtual assistants, ...
Personalized Stock Recommendation with Investors' Attention and Contextual Information
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 ...
Synerise Monad: A Foundation Model for Behavioral Event Data
The complexity of industry-grade event-based datalakes grows dynamically each passing hour. Companies actively gather behavioral information on their customers, recording multiple types of events, such as clicks, likes, page views, card transactions, add-...
Extracting Complex Named Entities in Legal Documents via Weakly Supervised Object Detection
Accurate Named Entity Recognition (NER) is crucial for various information retrieval tasks in industry. However, despite significant progress in traditional NER methods, the extraction of Complex Named Entities remains a relatively unexplored area. In ...
Exploring the Spatiotemporal Features of Online Food Recommendation Service
Online Food Recommendation Service (OFRS) has remarkable spatiotemporal characteristics and the advantage of being able to conveniently satisfy users' needs in a timely manner. There have been a variety of studies that have begun to explore its ...
Alleviating Matching Bias in Marketing Recommendations
In marketing recommendations, the campaign organizers will distribute coupons to users to encourage consumption. In general, a series of strategies are employed to interfere with the coupon distribution process, leading to a growing imbalance between ...
GreenSeq: Automatic Design of Green Networks for Sequential Recommendation Systems
Transformer-based models have achieved tremendous success in sequential recommendation (SR), but they suffer from consuming excessive computational resources, particularly in the inference stage. Thus, developing lightweight yet effective SR models has ...
DCBT: A Simple But Effective Way for Unified Warm and Cold Recommendation
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 ...
Building K-Anonymous User Cohorts with Consecutive Consistent Weighted Sampling (CCWS)
To retrieve personalized campaigns and creatives while protecting user privacy, digital advertising is shifting from member-based identity to cohort-based identity. Under such identity regime, an accurate and efficient cohort building algorithm is ...
Implicit Query Parsing at Amazon Product Search
Query Parsing aims to extract product attributes, such as color, brand, and product type, from search queries. These attributes play a crucial role in search engines for tasks such as matching, ranking, and recommendation. There are two types of ...
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training
E-commerce search engines comprise a retrieval phase and a ranking phase, where the first one returns a candidate product set given user queries. Recently, vision-language pre-training, combining textual information with visual clues, has been popular in ...
Improving Programming Q&A with Neural Generative Augmentation
- Suthee Chaidaroon,
- Xiao Zhang,
- Shruti Subramaniyam,
- Jeffrey Svajlenko,
- Tanya Shourya,
- Iman Keivanloo,
- Ria Joy
Knowledge-intensive programming Q&A is an active research area in industry. Its application boosts developer productivity by aiding developers in quickly finding programming answers from the vast amount of information on the Internet. In this study, we ...
Contextual Multilingual Spellchecker for User Queries
Spellchecking is one of the most fundamental and widely used search features. Correcting incorrectly spelled user queries not only enhances the user experience but is expected by the user. However, most widely available spellchecking solutions are either ...
Exploring 360-Degree View of Customers for Lookalike Modeling
- Md Mostafizur Rahman,
- Daisuke Kikuta,
- Satyen Abrol,
- Yu Hirate,
- Toyotaro Suzumura,
- Pablo Loyola,
- Takuma Ebisu,
- Manoj Kondapaka
Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to these models reside on the ...
Semantic-enhanced Modality-asymmetric Retrieval for Online E-commerce Search
- Zhigong Zhou,
- Ning Ding,
- Xiaochuan Fan,
- Yue Shang,
- Yiming Qiu,
- Jingwei Zhuo,
- Zhiwei Ge,
- Songlin Wang,
- Lin Liu,
- Sulong Xu,
- Han Zhang
Semantic retrieval, which retrieves semantically matched items given a textual query, has been an essential component to enhance system effectiveness in e-commerce search. In this paper, we study the multimodal retrieval problem, where the visual ...
OFAR: A Multimodal Evidence Retrieval Framework for Illegal Live-streaming Identification
Illegal live-streaming identification, which aims to help live-streaming platforms immediately recognize the illegal behaviors in the live-streaming, such as selling precious and endangered animals, plays a crucial role in purifying the network ...
How Well do Offline Metrics Predict Online Performance of Product Ranking Models?
Online evaluation techniques are widely adopted by industrial search engines to determine which ranking models perform better under a certain business metric. However, online evaluation can only evaluate a small number of rankers and people resort to ...
AttriBERT - Session-based Product Attribute Recommendation with BERT
Finding the right product on e-commerce websites with millions of products is a daunting task for a large set of customers. On the search page, product attribute filters a.k.a. "refinements" emerge as a convenient navigational option for customers to ...
Cited By
- Formal T, Clinchant S, Déjean H and Lassance C SPLATE: Sparse Late Interaction Retrieval Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, (2635-2640)
- Kato M, Mothe J and Poblete B (2024). Report on the 46th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023): Reflections from the Program Co-Chairs, ACM SIGIR Forum, 57:2, (1-20), Online publication date: 1-Dec-2023.
Index Terms
- Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval