Adaptive Temporal Query Modeling

Peetz, Maria-Hendrike; Meij, Edgar; Rijke, Maarten de; Weerkamp, Wouter

doi:10.1007/978-3-642-28997-2_40

Cited by 20 publications

(16 citation statements)

References 10 publications

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“…In the first set of experiments we compare the performance of our proposed method for estimating the query timeliness with the previous approaches that focused on the document [2,6,3,4,24] and query volume change [6] as discussed in Sections 2 and 3.…”

Section: Estimation Of the Query Timelinessmentioning

confidence: 99%

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Cheng

Arvanitis

Hristidis

2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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Researchers have recognized the importance of utilizing temporal features for improving the performance of information retrieval systems. Specifically, the timeliness of a web document can be a significant factor for determining whether it is relevant for a search query. Previous works have proposed time-aware retrieval models with particular focus on news queries, where recent web documents related with a real-world event are generally preferable. These queries typically exhibit bursts in the volume of published documents or submitted queries. However, no work has studied the role of time in queries such as "credit card overdraft fees" that have no major spikes in either document or query volumes over time, yet they still favor more recently published documents. In this work, we focus on this class of queries that we refer to as "timely queries". We show that the change in the terms distribution of results of timely queries over time is strongly correlated with the users' perception of time sensitivity. Based on this observation, we propose a method to estimate the query timeliness requirements and we propose principled ways to incorporate document freshness into the ranking model. Our study shows that our method yields a more accurate estimation of timeliness compared to volume-based approaches. We experimentally compare our ranking strategy with other time-sensitive and non time-sensitive ranking algorithms and we show that it improves the results' retrieval quality for timely queries.

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Section: Estimation Of the Query Timelinessmentioning

confidence: 99%

“…We first examine to what extent previous approaches [2,6,3,4,24] can predict the timeliness of timely queries. For this experiment, for each query in our workload, we issued a web search where we also specified an one month time range.…”

Section: Timeliness Estimation Based On Volume-based Approachesmentioning

confidence: 99%

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Cheng

Arvanitis

Hristidis

2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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“…They arranged the top retrieved documents into bins and assigned estimated relevance value to these bins. Peetz et al presented an adaptive temporal query modeling [15] for blog feed retrieval, in that they analyzed the top retrieved documents in terms of temporal histogram to find the bursts. They used documents with the highest scores from the bursts for query expansion and weighted each feedback document with the distance from the peak that contains most documents.…”

Section: Related Workmentioning

confidence: 99%

“…The advent of social media (e.g., blogs, microblogs) has increased the interest in time-based retrieval models. Recent studies on time-based models in IR have focused on using temporal distributions of retrieved documents in the pseudorelevance feedback setting [2,7,12,15]. This work has shown that selecting a relevant time period for a specific query, and then extracting expanded terms by using weights derived from the relevant time can improve retrieval performance.…”

Section: Introductionmentioning

confidence: 99%

Temporal models for microblogs

Choi

Croft

2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Time information impacts relevance in retrieval for the queries that are sensitive to trends and events. Microblog services particularly focused on recent news and events so dealing with the temporal aspects of microblogs is essential for providing effective retrieval. Recent work on time-based retrieval has shown that selecting the relevant time period for query expansion is promising. In this paper, we suggest a method for selecting the time period for query expansion based on a user behavior (i.e., retweets) that can be collected easily. We then use these time periods for query expansion in a pseudo-relevance feedback setting. More specifically, we use the difference in the temporal distribution between the top retrieved documents and retweets. The experimental results based on the TREC Microblog collection show that our method for selecting periods for query expansion improves retrieval performance compared to another approach.

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“…In [26], the authors presented an adaptive temporal query modeling for blog feed retrieval, in that they analyzed the top retrieved documents in terms of temporal histogram to find the bursts. They used documents with the highest scores from the bursts for query expansion and weighted each feedback document with the distance from the peak that contains most documents.…”

Section: Related Research Workmentioning

confidence: 99%

Exploiting Temporal Information in Documents and Query to Improve the Information Retrieval Process: Application to Medical Articles

Majdoubi¹,

Nabli²

2017

ijacsa

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Abstract-In the medical field, scientific articles represent a very important source of knowledge for researchers of this domain. But due to the large volume of scientific articles published on the web, an efficient detection and use of this knowledge is quite a difficult task. In this paper, we propose a novel method for semantic indexing of medical articles by using the semantic resource MeSH (Medical Subject Headings) and the temporal information provided in the documents. The proposed indexing approach was evaluated by intensive experiments. These experiments were conducted on document test collections of real world clinical extracted from scientific collections, namely, CISMEF and CLEF. The results generated by these experiments demonstrate the effectiveness of our indexing approach.

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Adaptive Temporal Query Modeling

Cited by 20 publications

References 10 publications

How fresh do you want your search results?

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Temporal models for microblogs

Exploiting Temporal Information in Documents and Query to Improve the Information Retrieval Process: Application to Medical Articles

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