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Searching has become an essential method for acquiring knowledge. The field of Search as Learning (SAL) has traditionally explored how users engage with search engines for learning tasks, yet these engines frequently falter with complex cognitive ...

It is our great pleasure to welcome you to the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region - the Second SIGIR-AP, hosted at Waseda University Nishiwaseda Campus, Tokyo, ...

In the information retrieval (IR) area, dense retrieval (DR) models use deep learning techniques to encode queries and passages into embedding space to compute their semantic relations. It is important for DR models to balance both efficiency and ...

Providing natural language-based explanations to justify recommendations helps to improve users' satisfaction and gain users' trust. However, as current explanation generation methods are commonly trained with an objective to mimic existing user reviews, ...

Due to the advantages in the cost-efficiency and reproducibility, user simulation has become a promising solution to the user-centric evaluation of information retrieval systems. Nonetheless, accurately simulating user search behaviors has long been a ...

Scaling laws have been observed in a wide range of tasks, particularly in language generation. Previous studies have found that the performance of large language models adheres to predictable patterns with respect to the size of models and datasets. This ...

Collaborative search supports multiple users working together to accomplish a specific search task. Research has found that designing lightweight collaborative search plugins within instant messaging platforms aligns better with users' collaborative ...

The ability of the foundation models heavily relies on large-scale, diverse, and high-quality pretraining data. In order to improve data quality, researchers and practitioners often have to manually curate datasets from difference sources and develop ...

With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval ...

Point-of-interest (POI) search is important for location-based services, such as navigation and online ride-hailing service. The goal of POI search is to find the most relevant destinations from a large-scale POI database given a text query. To improve ...

Legal case retrieval is a special Information Retrieval (IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users’ information needs in legal case retrieval could be significantly different from ...

While dense retrieval has become a promising alternative to the traditional text retrieval models, such as BM25, some recent studies show that multi-vector dense retrieval models are more effective than the single-vector method in retrieval tasks. ...

Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in ...

Session search is a widely adopted technique in search engines that seeks to leverage the complete interaction history of a search session to better understand the information needs of users and provide more relevant ranking results. The vast majority of ...

Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the application of DR is still limited. In contrast to statistic ...

Reading is a vital and complex cognitive activity during users’ information-seeking process. Several studies have focused on understanding users’ reading behavior in desktop search. Their findings greatly contribute to the design of information retrieval ...

Legal case retrieval, which aims to retrieve relevant cases given a query case, has drawn increasing research attention in recent years. While much research has worked on developing automatic retrieval models, how to characterize relevance in this ...

Result ranking is one of the major concerns for Web search technologies. Most existing methodologies rank search results in descending order of relevance. To model the interactions among search results, reinforcement learning (RL algorithms have been ...

A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models have demonstrated impressive performance on ad-hoc search benchmarks, we ...

The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This issue is usually approached by utilizing the interaction history to conduct ...