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As a critical task for large-scale commercial recommender systems, reranking rearranges items in the initial ranking lists from the previous ranking stage to better meet users’ demands. Foundational work in reranking has shown the potential of improving ...

Customer service is how companies interface with their customers. It can contribute heavily towards the overall customer satisfaction. However, high-quality service can become expensive, creating an incentive to make it as cost efficient as possible and ...

Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list to model interplay between items. Considering the inherent challenges of reranking such as combinatorial searching space, some previous ...

The results of information retrieval (IR) are usually presented in the form of a ranking list of candidate documents, such as web search for humans and retrieval-augmented generation for large language models (LLMs). List-aware retrieval aims to capture ...

Information retrieval aims to find information that meets users’ needs from the corpus. Different needs correspond to different IR tasks such as document retrieval, open-domain question answering, retrieval-based dialogue, and so on, while they share the ...

There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Both individual-level diversity and system-level diversity are important in this framework. The former focus on each ...

Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface design, the recommendation page has evolved to a multi-list style. Separately ...

As the final stage of the multi-stage recommender system (MRS), reranking directly affects users' experience and satisfaction, thus playing a critical role in MRS. Despite the improvement achieved in the existing work, three issues are yet to be solved. ...

Blibli.com provides a search engine for its customers. It used Solr search engine with only plain BM25 similarity function which is based on probability. In order to improve search engine performance, this research tried to implement an algorithm that ...

Accurate skill retrieval is a key factor for the success of modern conversational AI agents. The major challenges lie in the ambiguity in human spoken language and the wide spectrum of candidate skills. In this paper, we make the first attempt to attack ...

In the context of a multi-stage retrieval architecture, we explore candidate generation based on approximate nearest neighbor (ANN) search and lightweight reranking based on dense vector representations. These results serve as input to slower but more ...

IR-based Question Answering (QA) systems typically use a sentence selector to extract the answer from retrieved documents. Recent studies have shown that powerful neural models based on the Transformer can provide an accurate solution to Answer Sentence ...

As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily ...

We tackle the problem of question answering directly on a large document collection, combining simple “bag of words” passage retrieval with a BERT-based reader for extracting answer spans. In the context of this architecture, we present a data ...

Academic search engines have been widely used to access academic papers, where users’ information needs are explicitly represented as search queries. Some modern recommender systems have taken one step further by predicting users’ information needs ...

Generating natural language descriptions for videos (a.k.a video captioning) has attracted much research attention in recent years, and a lot of models have been proposed to improve the caption performance. However, due to the rapid progress in dataset ...

In modern recommender systems, diversity has been widely acknowledged as an important factor to improve user experience and, more recently, intent-aware approaches to diversification have been proposed to provide the user with a list of recommendations ...

Automatic face recognition techniques applied on particular group or mass database introduces error cases. Error prevention is crucial for the court. Reranking of recognition results based on anthropology analysis can significant improve the accuracy of ...

Bug fixing has a key role in software quality evaluation. Bug fixing starts with the bug localization step, in which developers use textual bug information to find location of source codes which have the bug. Bug localization is a tedious and time ...

Many content-based video search (CBVS) systems have been proposed to analyze the rapidly-increasing amount of user-generated videos on the Internet. Though the accuracy of CBVS systems have drastically improved, these high accuracy systems tend to be ...