A Multi-Domain Benchmark for Personalized Search Evaluation

Bassani, Elias; Kasela, Pranav; Raganato, Alessandro; Pasi, Gabriella

doi:10.1145/3511808.3557536

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…To create the dataset, we define as best answer for a given question the answer selected as the best one by the user who asked the question, if available; otherwise, we assume the best answer to the one with the highest score, if it has received a score greater than a fixed threshold 𝛾 𝑠 4 . We note that this assumption, for the best answer being the most voted answer if no answer has been flagged as best by the user asking the question, is used only for the expert detection procedure, which will be explained subsequently and not as relevance judgement for the test data.…”

Section: Se-pef Definitionmentioning

confidence: 99%

SE-PEF: a Resource for Personalized Expert Finding

Kasela,

Pasi,

Perego

2023

Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia P

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The problem of personalization in Information Retrieval has been under study for a long time. A well-known issue related to this task is the lack of publicly available datasets to support a comparative evaluation of personalized search systems. To contribute in this respect, this paper introduces SE-PEF (StackExchange -Personalized Expert Finding), a resource useful for designing and evaluating personalized models related to the Expert Finding (EF) task. The contributed dataset includes more than 250k queries and 565k answers from 3 306 experts, which are annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. The results of the preliminary experiments conducted show the appropriateness of SE-PEF to evaluate and to train effective EF models.

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Section: Se-pef Definitionmentioning

confidence: 99%

SE-PEF: a Resource for Personalized Expert Finding

Kasela,

Pasi,

Perego

2023

Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia P

Self Cite

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SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples

Färber,

Lamprecht,

Krause

et al. 2023

Lecture Notes in Computer Science

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We present SemOpenAlex, an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. SemOpenAlex is licensed under CC0, providing free and open access to the data. We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources. Moreover, we provide embeddings for knowledge graph entities using high-performance computing. SemOpenAlex enables a broad range of use-case scenarios, such as exploratory semantic search via our website, large-scale scientific impact quantification, and other forms of scholarly big data analytics within and across scientific disciplines. Additionally, it enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. Finally, SemOpenAlex can serve for RDF query optimization benchmarks, creating scholarly knowledge-guided language models, and as a hub for semantic scientific publishing. Data and Services: https://semopenalex.org https://w3id.org/SemOpenAlex Code: https://github.com/metaphacts/semopenalex/ Data License: Creative Commons Zero (CC0) Code License: MIT License

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indxr: A Python Library for Indexing File Lines

Bassani,

Tonellotto

2024

Lecture Notes in Computer Science

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A Multi-Domain Benchmark for Personalized Search Evaluation

Cited by 5 publications

References 26 publications

SE-PEF: a Resource for Personalized Expert Finding

SE-PEF: a Resource for Personalized Expert Finding

SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples

indxr: A Python Library for Indexing File Lines

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