Time-based relevance models

Keikha, Mostafa; Gerani, Shima; Crestani, Fábio

doi:10.1145/2009916.2010062

Cited by 29 publications

(38 citation statements)

References 5 publications

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“…They used temporal features for query performance prediction [3] and temporal query classification [6] tasks. Keikha et al proposed a time-based relevance model [7] for blog feed retrieval, which uses the P(t|Q) introduced in [3] as a weight of the terms in the pseudo-relevance feedback setting. In this work, we extend the framework of the time-based relevance model to incorporate the temporal factor into ranking.…”

Section: Related Workmentioning

confidence: 99%

“…Our temporal model for microblogs builds upon a time-based relevance model [7] that incorporates time factors into the language model framework. In this section, we first introduce the time-based relevance model in detail and then, suggest a method for selecting the relevant time using retweets for the query.…”

Section: Temporal Models For Microblogmentioning

confidence: 99%

“…The pseudo-relevance model generates expansion terms based on the top k retrieved documents (denote R) as Eq. (1) Keikha et al [7] proposed a time-based relevance model which incorporates time factor into relevance model framework. They introduced a generative model of the query that first selects a time and then selects a term based on the time and query as Eq.…”

Section: Time-based Relevance Modelmentioning

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%

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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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Section: Related Workmentioning

confidence: 99%

Section: Temporal Models For Microblogmentioning

confidence: 99%

Section: Time-based Relevance Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Temporal models for microblogs

Choi

Croft

2012

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

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“…In addition, they presented a ranking concerning implicit temporal needs, and the experimental results showed that their approach improved the retrieval effectiveness of temporal queries for web search. Keikha et al [65,66] proposed a time-based query expansion technique that selects terms for expansion from different times. Then, the technique was used for retrieving and ranking blogs, which also captures the dynamics of the topic both in aspects and vocabulary usage over time.…”

Section: Related Workmentioning

confidence: 99%

Time-aware approaches to information retrieval

Kanhabua

2012

SIGIR Forum

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In this thesis, we address major challenges in searching temporal document collections. In such collections, documents are created and/or edited over time. Examples of temporal document collections are web archives, news archives, blogs, personal emails and enterprise documents. Unfortunately, traditional IR approaches based on term-matching only can give unsatisfactory results when searching temporal document collections. The reason for this is twofold: the contents of documents are strongly time-dependent, i.e., documents are about events happened at particular time periods, and a query representing an information need can be time-dependent as well, i.e., a temporal query.Our contributions in this thesis are different time-aware approaches within three topics in IR: content analysis, query analysis, and retrieval and ranking models. In particular, we aim at improving the retrieval effectiveness by 1) analyzing the contents of temporal document collections, 2) performing an analysis of temporal queries, and 3) explicitly modeling the time dimension into retrieval and ranking.Leveraging the time dimension in ranking can improve the retrieval effectiveness if information about the creation or publication time of documents is available. In this thesis, we analyze the contents of documents in order to determine the time of non-timestamped documents using temporal language models. We subsequently employ the temporal language models for determining the time of implicit temporal queries, and the determined time is used for re-ranking search results in order to improve the retrieval effectiveness.We study the effect of terminology changes over time and propose an approach to handling terminology changes using time-based synonyms. In addition, we propose different methods for predicting the effectiveness of temporal queries, so that a particular query enhancement technique can be performed to improve the overall performance. When the time dimension is incorporated into ranking, documents will be ranked according to both textual and temporal similarity. In this case, time uncertainty should also be taken into account. Thus, we propose a ranking model that considers the time uncertainty, and improve ranking by combining multiple features using learning-to-rank techniques.Through extensive evaluation, we show that our proposed time-aware approaches outperform traditional retrieval methods and improve the retrieval effectiveness in searching temporal document collections.

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Geographical Latent Variable Models for Microblog Retrieval

Kotov

Rakesh

Agichtein

et al. 2015

Lecture Notes in Computer Science

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Although topic models designed for textual collections annotated with geographical meta-data have been previously shown to be effective at capturing vocabulary preferences of people living in different geographical regions, little is known about their utility for information retrieval in general or microblog retrieval in particular. In this work, we propose simple and scalable geographical latent variable generative models and a method to improve the accuracy of retrieval from collections of geo-tagged documents through document expansion that is based on the topics identified by the proposed models. In particular, we experimentally compare the retrieval effectiveness of four geographical latent variable models: two geographical variants of post-hoc LDA, latent variable model without hidden topics and a topic model that can separate background from geographically-specific topics. The experiments conducted on TREC microblog dataset demonstrate significant improvement in search accuracy of the proposed method over both the traditional probabilistic retrieval model and retrieval models utilizing geographical post-hoc variants of LDA.

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Time-based relevance models

Cited by 29 publications

References 5 publications

Temporal models for microblogs

Temporal models for microblogs

Time-aware approaches to information retrieval

Geographical Latent Variable Models for Microblog Retrieval

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