A Statistical Model for Relevance Feedback in Information Retrieval

Yu, Clement; Luk, Wo-Shun; Cheung, Tsang

doi:10.1145/321941.321947

Cited by 36 publications

(11 citation statements)

References 8 publications

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“…Modify the constant values of Rocchio's formula. Studies conducted by Yu et al (1976), Koster and Beney (2007), and Moschitti (2003) changed the values for the adjustment constants in order to achieve better retrieval accuracy.…”

Section: Oir 352mentioning

confidence: 99%

A ranking algorithm for query expansion based on the term's appearing probability in the single document

Chou

Cheng²,

Huang³

2011

Online Information Review

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Purpose -The purpose of this paper is to establish a new approach for solving the expansion term problem. Design/methodology/approach -This study develops an expansion term weighting function derived from the valuable concepts used by previous approaches. These concepts include probability measurement, adjustment according to situations, and summation of weights. Formal tests have been conducted to compare the proposed weighting function with the baseline ranking model and other weighting functions. Findings -The results reveal stable performance by the proposed expansion term weighting function. It proves more effective than the baseline ranking model and outperforms other weighting functions.Research limitations/implications -The paper finds that testing additional data sets and potential applications to real working situations is required before the generalisability and superiority of the proposed expansion term weighting function can be asserted. Originality/value -Stable performance and an acceptable level of effectiveness for the proposed expansion term weighting function indicate the potential for further study and development of this approach. This would add to the current methods studied by the information retrieval community for culling information from documents.

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Section: Oir 352mentioning

confidence: 99%

A ranking algorithm for query expansion based on the term's appearing probability in the single document

Chou

Cheng²,

Huang³

2011

Online Information Review

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“…Information retrieval (IR [35]) systems face similar objectivesubjective gaps: given an information retrieval request, Figure 2: Overview of the relevance feedback process commonly used in information retrieval when the available data has alternative (user-and query-dependent) interpretations…”

Section: Feedback In Information Retrievalmentioning

confidence: 99%

Ficsr

Yan¹,

Candan²,

Sapino

2007

Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data

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A critical reality in data integration is that knowledge from different sources may often be conflicting with each other. Conflict resolution can be costly and, if done without proper context, can be ineffective. In this paper, we propose a novel query-driven and feedback-based approach (FICSR 1 ) to conflict resolution when integrating data sources. In particular, instead of relying on traditional model based definition of consistency, we introduce a ranked interpretation. This not only enables FICSR to deal with the complexity of the conflict resolution process, but also helps achieve a more direct match between the users' (subjective) interpretation of the data and the system's (objective) treatment of the available alternatives. Consequently, the ranked interpretation leads to new opportunities for bi-directional (data inf orms ←→ user) feedback cycle for conflict resolution: given a query, (a) a preliminary ranking of candidate results on data can inform the user regarding constraints critical to the query, while (b) user feedback regarding the ranks can be exploited to inform the system about user's relevant domain knowledge. To enable this feedback process, we develop data structures and algorithms for efficient off-line conflict/agreement analysis of the integrated data as well as for on-line query processing, candidate result enumeration, and validity analysis. The results are brought together and evaluated in the FICSR system.

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“…There have been extensive studies on the probabilistic model [5,6,7,8]. It is generally agreed that the probabilistic approach provides a sound theoretical basis for the development of information retrieval systems.…”

Section: ~Troductionmentioning

confidence: 99%