Personalized Query Expansion, the task of expanding queries with additional terms extracted from the user-related vocabulary, is a well-known solution to improve the retrieval performance of a system w.r.t. short queries. Recent approaches rely on word embeddings to select expansion terms from user-related texts. Although promising results have been delivered with former word embedding techniques, we argue that these methods are not suited for contextual word embeddings, which produce a unique vector representation for each term occurrence. In this article, we propose a Personalized Query Expansion method designed to solve the issues arising from the use of contextual word embeddings with the current Personalized Query Expansion approaches based on word embeddings. Specifically, we employ a clustering-based procedure to identify the terms that better represent the user interests and to improve the diversity of those selected for expansion, achieving improvements of up to 4% w.r.t. the best-performing baseline in terms of MAP@100. Moreover, our approach outperforms previous ones in terms of efficiency, allowing us to achieve sub-millisecond expansion times even in data-rich scenarios. Finally, we introduce a novel metric to evaluate the expansion terms’ diversity and empirically show the unsuitability of previous approaches based on word embeddings when employed along with contextual word embeddings, which cause the selection of semantically overlapping expansion terms.

ranxhub is an online repository for sharing artifacts deriving from the evaluation of Information Retrieval systems. Specifically, we provide a platform for sharing pre-computed runs: the ranked lists of documents retrieved for a specific set of queries by a retrieval model. We also extend ranx, a Python library for the evaluation and comparison of Information Retrieval runs, adding functionalities to integrate the usage of ranxhub seamlessly, allowing the user to compare the results of multiple systems in just a few lines of code. In this paper, we first outline the many advantages and implications that an online repository for sharing runs can bring to the table. Then, we introduce ranxhub and its integration with ranx, showing its very simple usage. Finally, we discuss some use cases for which ranxhub can be highly valuable for the research community.

Personalization in Information Retrieval has been a hot topic in both academia and industry for the past two decades. However, there is still a lack of high-quality standard benchmark datasets for conducting offline comparative evaluations in this context. To mitigate this problem, in the past few years, approaches to derive synthetic datasets suited for evaluating Personalized Search models have been proposed. In this paper, we put forward a novel evaluation benchmark for Personalized Search with more than 18 million documents and 1.9 million queries across four domains. We present a detailed description of the benchmark construction procedure, highlighting its characteristics and challenges. We provide baseline performance including pre-trained neural models, opening room for the evaluation of personalized approaches, as well as domain adaptation and transfer learning scenarios. We make both datasets and models available for future research.

This paper presents ranx.fuse, a Python library for Metasearch. Built following a user-centered design, it provides easy-to-use tools for combining the results of multiple search engines. ranx.fuse comprises 25 Metasearch algorithms implemented with Numba, a just-in-time compiler for Python code, for efficient vector operations and automatic parallelization. Moreover, in conjunction with the Metasearch algorithms, our library implements six normalization strategies that transform the search engines' result lists to make them comparable, a mandatory step for Metasearch. Finally, as many Metasearch algorithms require a training or optimization step, ranx.fuse offers a convenient functionality for their optimization that evaluates pre-defined hyper-parameters configurations via grid search. By relying on the provided functions, the user can optimally combine the results of multiple search engines in very few lines of code. ranx.fuse can also serve as a user-friendly tool for fusing the rankings computed by a first-stage retriever and a re-ranker, as a library providing several baselines for Metasearch, and as a playground to test novel normalization strategies.

Searching in a domain-specific corpus of structured documents (e.g., e-commerce, media streaming services, job-seeking platforms) is often managed as a traditional retrieval task or through faceted search. Semantic Query Labeling --- the task of locating the constituent parts of a query and assigning domain-specific predefined semantic labels to each of them --- allows leveraging the structure of documents during retrieval while leaving unaltered the keyword-based query formulation. Due to both the lack of a publicly available dataset and the high cost of producing one, there have been few published works in this regard. In this paper, basing on the assumption that a corpus already contains the information the users search, we propose a method for the automatic generation of semantically labeled queries and show that a semantic tagger --- based on BERT, gazetteers-based features, and Conditional Random Fields --- trained on our synthetic queries achieves results comparable to those obtained by the same model trained on real-world data. We also provide a large dataset of manually annotated queries in the movie domain suitable for studying Semantic Query Labeling. We hope that the public availability of this dataset will stimulate future research in this area.