Pseudo-Relevance Feedback Based on Matrix Factorization

Zamani, Hamed; Dadashkarimi, Javid; Shakery, Azadeh; Croft, W. Bruce

doi:10.1145/2983323.2983844

Cited by 47 publications

(45 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of these works should be mentioned here: Robertson, Walker, Hancock-Beaulieu, Gatford, and Payne (1995) proposed a well-known and successful automatic PRF algorithm in the Okapi system. In addition, many other competitive approaches (for instance, Daoud & Huang, 2013;Metzler & Croft, 2005;Colace, De Santo, Greco, & Napoletano, 2015;Zamani et al, 2016;Mbarek, Tmar, Hattab, & Boughanem, 2010) have also achieved significant performance in improving retrieval effectiveness. Miao et al (2012) proposed a proximity-based feedback model (called PRoc) that adapts the Rocchio model to capture the proximity relationships between candidate terms and corresponding query in feedback documents.…”

Section: Related Workmentioning

confidence: 99%

“…Pseudo-relevance feedback (PRF) is an effective method of solving this problem by utilizing feedback information obtained based on the first-pass retrieval results (for instance, Rocchio, 1971;Ksentini, Tmar, & Gargouri, 2016;Vaidyanathan, Das, & Srivastava, 2015;Zamani, Dadashkarimi, Shakery, & Croft, 2016;Ye & Huang, 2016;Lang, Metzler, Wang, & Li, 2010). Therefore, this behavior results in the retrieval of irrelevant documents.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, there is an imperative need to develop a retrieval system with a stronger ability to overcome the shortcomings of current systems when faced with inaccurate information, such as ambiguous, incomplete, and imprecise original queries. Pseudo‐relevance feedback (PRF) is an effective method of solving this problem by utilizing feedback information obtained based on the first‐pass retrieval results (for instance, Rocchio, ; Ksentini, Tmar, & Gargouri, ; Vaidyanathan, Das, & Srivastava, ; Zamani, Dadashkarimi, Shakery, & Croft, ; Ye & Huang, ; Lang, Metzler, Wang, & Li, ). The query expansion (QE) technique is one of the key approaches used in PRF to boost retrieval performance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A simple kernel co‐occurrence‐based enhancement for pseudo‐relevance feedback

Pan

Huang

et al. 2019

Asso for Info Science & Tech

View full text Add to dashboard Cite

Pseudo-relevance feedback is a well-studied query expansion technique in which it is assumed that the topranked documents in an initial set of retrieval results are relevant and expansion terms are then extracted from those documents. When selecting expansion terms, most traditional models do not simultaneously consider term frequency and the co-occurrence relationships between candidate terms and query terms. Intuitively, however, a term that has a higher co-occurrence with a query term is more likely to be related to the query topic. In this article, we propose a kernel co-occurrence-based framework to enhance retrieval performance by integrating term co-occurrence information into the Rocchio model and a relevance language model (RM3). Specifically, a kernel co-occurrence-based Rocchio method (KRoc) and a kernel co-occurrence-based RM3 method (KRM3) are proposed. In our framework, co-occurrence information is incorporated into both the factor of the term discrimination power and the factor of the within-document term weight to boost retrieval performance. The results of a series of experiments show that our proposed methods significantly outperform the corresponding strong baselines over all data sets in terms of the mean average precision and over most data sets in terms of P@10. A direct comparison of standard Text Retrieval Conference data sets indicates that our proposed methods are at least comparable to state-of-the-art approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A simple kernel co‐occurrence‐based enhancement for pseudo‐relevance feedback

Pan

Huang

et al. 2019

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…A variety of different techniques can be utilized for the feedback mechanism, such as Rocchio's algorithm ( [6]) which moves the query point in space closer to the relevant items. It is among the early RF methods, recently considered with different variants [40], [41], [42]. We have adapted a modified version of Rocchio's algorithm.…”

Section: Diverse Relevance Feedback For Time Seriesmentioning

confidence: 99%

Diverse Relevance Feedback for Time Series with Autoencoder Based Summarizations

Eravci

Ferhatosmanoğlu

2018

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

We present a relevance feedback based browsing methodology using different representations for time series data. The outperforming representation type, e.g., among dual-tree complex wavelet transformation, Fourier, symbolic aggregate approximation (SAX), is learned based on user annotations of the presented query results with representation feedback. We present the use of autoencoder type neural networks to summarize time series or its representations into sparse vectors, which serves as another representation learned from the data. Experiments on 85 real data sets confirm that diversity in the result set increases precision, representation feedback incorporates item diversity and helps to identify the appropriate representation. The results also illustrate that the autoencoders can enhance the base representations, and achieve comparably accurate results with reduced data sizes.

show abstract

“…Pseudo-relevance feedback has long been employed as a powerful method for estimating query language models in a large number of studies [6,5,12]. Cross-lingual relevance model (CLRLM) and CLTRLM are state-of-the-art methods in cross-lingual environments [1,11,3].…”

Section: Related Workmentioning

confidence: 99%

Dimension Projection Among Languages Based on Pseudo-Relevant Documents for Query Translation

Dadashkarimi

Shahshahani

Tebbifakhr

et al. 2017

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Using top-ranked documents in response to a query has been shown to be an effective approach to improve the quality of query translation in dictionarybased cross-language information retrieval. In this paper, we propose a new method for dictionary-based query translation based on dimension projection of embedded vectors from the pseudo-relevant documents in the source language to their equivalents in the target language. To this end, first we learn low-dimensional vectors of the words in the pseudo-relevant collections separately and then aim to find a query-dependent transformation matrix between the vectors of translation pairs appeared in the collections. At the next step, representation of each query term is projected to the target language and then, after using a softmax function, a querydependent translation model is built. Finally, the model is used for query translation. Our experiments on four CLEF collections in French, Spanish, German, and Italian demonstrate that the proposed method outperforms a word embedding baseline based on bilingual shuffling and a further number of competitive baselines. The proposed method reaches up to 87% performance of machine translation (MT) in short queries and considerable improvements in verbose queries.

show abstract

Pseudo-Relevance Feedback Based on Matrix Factorization

Cited by 47 publications

References 41 publications

A simple kernel co‐occurrence‐based enhancement for pseudo‐relevance feedback

A simple kernel co‐occurrence‐based enhancement for pseudo‐relevance feedback

Diverse Relevance Feedback for Time Series with Autoencoder Based Summarizations

Dimension Projection Among Languages Based on Pseudo-Relevant Documents for Query Translation

Contact Info

Product

Resources

About