Sowiport User Search Sessions Data Set (SUSS)

Hoek, Wilko van; Gesis,; Forscher,

doi:10.7802/1380

Cited by 7 publications

(4 citation statements)

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“…In our below experiments, we use the Sowiport User Search Session dataset (SUSS). 5 [22] It includes 558,008 sessions with about 8 million interactions (179,796 of these are queries) and was collected from April 2014 to April 2015 from users of the Sowiport digital library search system. Within the sessions, 58 different actions describe the users' activities while interacting with the interface of the search engine (e.g., formulating a query or clicking on a document).…”

Section: Experimental Scenariosmentioning

confidence: 99%

The SimIIR 2.0 Framework

Zerhoudi

Günther

Plassmeier

et al. 2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

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Section: Experimental Scenariosmentioning

confidence: 99%

The SimIIR 2.0 Framework

Zerhoudi

Günther

Plassmeier

et al. 2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

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“…In order to examine users' query change behaviors, we use Sowiport 1 User Search Session Data Set (SUSS) 2 [22] for our experiments, which includes 484,437 individual search sessions, 179,796 queries and around 8 million log entries that was collected over a period of one year (from April 2014 to April 2015).…”

Section: Datasetmentioning

confidence: 99%

Query Change as a Contextual Markov Model for Simulating User Search Behaviour

Zerhoudi

Schlötterer

Seifert

2021

Forum for Information Retrieval Evaluation

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Search engine users issue queries to formulate their information need and gain useful insights. However, it is challenging for search engines to understand different users' search type intents and return appropriate results. Simulating user search behaviour allows information retrieval systems (IR) to parameterise the a-priori distribution of search types using different back-end configurations and user interface variants to improve the retrieval functionality. In this paper, we propose a formal Markov approach in which we utilise the context discovery process to model user-type specific behaviour by capturing the user's query change in a search session. Contextual Markov models have been used in the past to improve the prediction of user intentions, we investigate here their efficiency in simulating user-type specific interactions. Additionally, we provide an empirical and classification-based evaluation that can be used in simulation assessment. Overall, we report that the proposed approach reliably simulates user-type specific behaviour on a real-world academic search engine log dataset.

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“…Currently we discuss this option with the operator of Sowiport. Nevertheless, we want to emphasize that the documents' metadata is mostly already available through Sowiport's OAI interface, and additional usage data is available [Mayr 2016].…”

Section: Document Metadatamentioning

confidence: 99%

RARD: The Related-Article Recommendation Dataset

Beel¹,

Carevic²,

Schaible³

et al. 2017

D-Lib Magazine

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The main contribution of this paper is to introduce and describe a new recommender-systems dataset (RARD II). It is based on data from a recommender-system in the digital library and reference management software domain. As such, it complements datasets from other domains such as books, movies, and music. The RARD II dataset encompasses 89m recommendations, covering an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. For example, in addition to the usual ratings matrices, RARD II includes the original recommendation logs, which provide a unique insight into many aspects of the algorithms that generated the recommendations. In this paper, we summarise the key features of this dataset release, describing how it was generated and discussing some of its unique features.

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Sowiport User Search Sessions Data Set (SUSS)

Cited by 7 publications

References 0 publications

The SimIIR 2.0 Framework

The SimIIR 2.0 Framework

Query Change as a Contextual Markov Model for Simulating User Search Behaviour

RARD: The Related-Article Recommendation Dataset

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