Creating efficient and effective search and recommendation algorithms has been the main objective of industry practitioners and academic researchers over the years. However, recent research has shown how these algorithms trained on historical data lead ...

With proliferation of personal computing devices and large number of logged-in experiences, search has evolved to a stage with many different product scenarios where personalization plays a crucial role for relevance quality and user satisfaction. The ...

Neural models have demonstrated remarkable performance across diverse ranking tasks. However, the processes and internal mechanisms along which they determine relevance are still largely unknown. Existing approaches for analyzing neural ranker behavior ...

Nowadays, many platforms provide users with both search and recommendation services as important tools for accessing information. The phenomenon has led to a correlation between user search and recommendation behaviors, providing an opportunity to model ...

As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grows to ensure accountability, fairness, and unbiased results. Despite recent ...

In 2021, the RISM Digital Center introduced RISM Online. This represented a shift in how we present the RISM data to a global audience, supporting new methods of digital research and keeping the RISM project central to modern music scholarship. RISM ...

Kernel scheduling is the problem of finding the most efficient implementation for a computational kernel. Identifying this implementation involves experimenting with the parameters of compiler optimizations, such as the size of tiling windows and ...

The advent of large language models (LLMs) presents both opportunities and challenges for the information retrieval (IR) community. On one hand, LLMs will revolutionize how people access information, meanwhile the retrieval techniques can play a crucial ...

Recent decisions to discontinue access to social media APIs are having detrimental effects on Internet research and the field of computational social science as a whole. This lack of access to data has been dubbed the Post-API era of Internet research. ...

Natural language and search interfaces intuitively facilitate data exploration and provide visualization responses to diverse analytical queries based on the underlying datasets. However, these interfaces often fail to interpret more complex analytical ...

The concept of artificial intelligence arose in the middle of the 20th century, but the rapid development of one specific area for the implementation of artificial intelligence, namely, neural networks, has only occurred over the past 10 years. ...

Extracting knowledge from unstructured or semi-structured textual information is essential for the machine learning applications that power DoorDash's search experience, and the development and maintenance of its product knowledge graph. Large language ...

Problem-solving in complex environments requires a cognitively demanding search for task solutions. Managing this search process presents a major challenge in organizations. We contribute to the literature on this topic by providing new evidence on the ...

Advancements in cloud computing and distributed computing have fostered research activities in Computer science. As a result, researchers have made significant progress in Neural Networks, Evolutionary Computing Algorithms like Genetic, and Differential ...

The confluence of Search and Recommendation (S&R) services is vital to online services, including e-commerce and video platforms. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a ...

This work presents the search and discovery architecture of Amazon Music, a highly efficient system designed to retrieve relevant music content for users. The architecture consists of three key stages: indexing, retrieval, and ranking. During the ...

The Transformer Memory as a Differentiable Search Index (DSI) has been proposed as a new information retrieval paradigm, which aims to address the limitations of dual-encoder retrieval framework based on the similarity score. The DSI framework ...

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 ...

Podcasts are spoken documents that, in recent years, have gained widespread popularity. Despite the growing research interest in this domain, conducting user studies remains challenging due to the lack of datasets that include user behaviour. In ...

As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grows to ensure accountability, fairness, and unbiased results. Despite many ...