As global e-commerce platforms continue to expand, companies are entering new markets where they encounter cold-start challenges due to limited human labels and user behaviors. In this paper, we share our experiences in Coupang to provide a competitive cold-start performance of relevance matching for emerging e-commerce markets. Specifically, we present a Cold-Start Relevance Matching (CSRM) framework, utilizing a multilingual Large Language Model (LLM) to address three challenges: (1) activating cross-lingual transfer learning abilities of LLMs through machine translation tasks; (2) enhancing query understanding and incorporating e-commerce knowledge by retrieval-based query augmentation; (3) mitigating the impact of training label errors through a multi-round self-distillation training strategy. Our experiments demonstrate the effectiveness of CSRM-LLM and the proposed techniques, resulting in successful real-world deployment and significant online gains, with a 45.8% reduction in defect ratio and a 0.866% uplift in session purchase rate.

Understanding the search tasks and search behavior of users is necessary for optimizing search engine results. While much work has been done on understanding the users in Web search, little knowledge is available about the search tasks and behavior of users in the E-Commerce (E-Com) search applications. In this paper, we share the first empirical study of the queries and search behavior of users in E-Com search by analyzing search log from a major E-Com search engine. The analysis results show that E-Com queries can be categorized into five categories, each with distinctive search behaviors: (1) Shallow Exploration Queries are short vague queries that a user may use initially in exploring the product space. (2) Targeted Purchase Queries are queries used by users to purchase items that they are generally familiar with, thus without much decision making. (3) Major-Item Shopping Queries are used by users to shop for a major item which is often relatively expensive and thus requires some serious exploration, but typically in a limited scope of choices. (4) Minor-Item Shopping Queries are used by users to shop for minor items that are generally not very expensive, but still require some exploration of choices. (5) Hard-Choice Shopping Queries are used by users who want to deeply explore all the candidate products before finalizing the choice often appropriate when multiple products must be carefully compared with each other. These five categories form a taxonomy for E-Com queries and can shed light on how we may develop customized search technologies for each type of search queries to improve search engine utility.

In Web search and vertical search, recency ranking refers to retrieving and ranking documents by both relevance and freshness. As impoverished in-links and click information is the the biggest challenge for recency ranking, we advocate the use of Twitter data to address the challenge in this article. We propose a method to utilize Twitter TinyURL to detect fresh and high-quality documents, and leverage Twitter data to generate novel and effective features for ranking. The empirical experiments demonstrate that the proposed approach effectively improves a commercial search engine for both Web search ranking and tweet vertical ranking.

With the explosive growth of online news readership, recommending interesting news articles to users has become extremely important. While existing Web services such as Yahoo! and Digg attract users' initial clicks by leveraging various kinds of signals, how to engage such users algorithmically after their initial visit is largely under-explored. In this paper, we study the problem of post-click news recommendation. Given that a user has perused a current news article, our idea is to automatically identify "related" news articles which the user would like to read afterwards. Specifically, we propose to characterize relatedness between news articles across four aspects: relevance, novelty, connection clarity, and transition smoothness. Motivated by this understanding, we define a set of features to capture each of these aspects and put forward a learning approach to model relatedness. In order to quantitatively evaluate our proposed measures and learn a unified relatedness function, we construct a large test collection based on a four-month commercial news corpus with editorial judgments. The experimental results show that the proposed heuristics can indeed capture relatedness, and that the learned unified relatedness function works quite effectively.

Temporal information can be leveraged and incorporated to improve web search ranking. In this work, we propose a method to improve the ranking of search results by identifying the fundamental properties of temporal behavior of low-quality hosts and spam-prone queries in search logs and modeling those properties as quantifiable features. In particular, we introduce the concepts of host churn, a measure of changes in host visibility for user queries, and query volatility, a measure of semantic instability of query results, and propose the methods for construction of temporal profiles from search query logs that can be used for estimation of a set of features based on the introduced concepts. The utility of the proposed concepts has been experimentally demonstrated for two language-independent search tasks: the regression-based ranking of search results and a novel classification problem of detecting spam-prone queries introduced in this work.

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.

The standard method for combating spam, either in email or on the web, is to train a classifier on manually labeled instances. As the spammers change their tactics, the performance of such classifiers tends to decrease over time. Gathering and labeling more data to periodically retrain the classifier is expensive. We present a method based on an ensemble of classifiers that can detect when its performance might be degrading and retrain itself, all without manual intervention. Experiments with a real-world dataset from the blog domain show that our methods can significantly reduce the number of times classifiers are retrained when compared to a fixed retraining schedule, and they maintain classification accuracy even in the absence of manually labeled examples.

In this paper, we describe a Semantic Web application that detects Conflict of Interest (COI) relationships among potential reviewers and authors of scientific papers. This application discovers various 'semantic associations' between the reviewers and authors in a populated ontology to determine a degree of Conflict of Interest. This ontology was created by integrating entities and relationships from two social networks, namely "knows," from a FOAF (Friend-of-a-Friend) social network and "co-author," from the underlying co-authorship network of the DBLP bibliography. We describe our experiences developing this application in the context of a class of Semantic Web applications, which have important research and engineering challenges in common. In addition, we present an evaluation of our approach for real-life COI detection.