Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2002
DOI: 10.1145/564376.564417
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Set-based model

Abstract: The objective of this paper is to present a new technique for computing term weights for index terms, which leads to a new ranking mechanism, referred to as set-based model. The components in our model are no longer terms, but termsets. The novelty is that we compute term weights using a data mining technique called association rules, which is time efficient and yet yields nice improvements in retrieval effectiveness. The set-based model function for computing the similarity between a document and a query cons… Show more

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Cited by 18 publications
(6 citation statements)
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“…The retrieval process is handled by the Set-Based model. As mentioned, the notion of termsets, related to query terms, contributes highly to complexity reduction, due to processing significantly lower data volume (Possas et al (2002), Pôssas et al (2005)). It is important to notice that the model implements association rule mining algorithms (Agrawal and Srikant (1994)), to combine two frequent termsets in a different element of each set, thus creating a new one, which if frequent, will be considered a termset of the model.…”
Section: Graphs and Set-based Modelmentioning
confidence: 99%
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“…The retrieval process is handled by the Set-Based model. As mentioned, the notion of termsets, related to query terms, contributes highly to complexity reduction, due to processing significantly lower data volume (Possas et al (2002), Pôssas et al (2005)). It is important to notice that the model implements association rule mining algorithms (Agrawal and Srikant (1994)), to combine two frequent termsets in a different element of each set, thus creating a new one, which if frequent, will be considered a termset of the model.…”
Section: Graphs and Set-based Modelmentioning
confidence: 99%
“…On the other hand, on large values, the percentage model tends to perform as the complete graph-based extension of the set-based model (GSB) (Kalogeropoulos et al (2020)). To further amplify our case, taking into consideration that the MAP metric could be misleading, we counted the number of queries that our proposed models outperformed the set-based model (Possas et al (2002)). That is expressed by the difference in average precision for each query between the set-based model and the rest, as it is depicted in figure 8.…”
Section: Models Performance On Rankingmentioning
confidence: 99%
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“…What the BWI can provide is a further reduction of the search space of Apriori without incurring in large memory consumption. In IR, FI mining has been applied to indexing and retrieving documents; in this application, documents and queries can be represented as term sets where a term set is an itemset and items are terms ( Pôssas, Ziviani, Meira, & Ribeiro-Neto, 2002 ), ( Pôssas, Ziviani, Meira, & Ribeiro-Neto, 2005 ). Basically, all these approaches are based on counting frequent term sets and prescribe the selection of the most frequent words, the join of these frequent words with themselves, the selection of the most frequent word pairs, and so on.…”
Section: Related Workmentioning
confidence: 99%
“…However, this method will greatly increase the complexity of the model, making it intractable in practice. Pôssas et al [11] followed a similar direction using term sets. Term sets group terms that co-occur frequently in documents.…”
Section: Related Workmentioning
confidence: 99%