IR

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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, ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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, ...

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 ...

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-...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 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 ...

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 ...

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 ...

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 ...