Abstract
This paper presents an attempt to tackle information retrieval (IR) with meta-heuristics. For this aim, we propose two ACO algorithms for information retrieval on large-scale data sets. The main hard issue of this study resides in modeling information retrieval using meta-heuristics that often necessitate links between documents in order to realize move operations from one document to another during the search process. The first novelty in this work is the design of such model to adapt ACO approaches and even other meta-heuristics to IR. The second one resides in the hybridization of ACO approaches with tabu search in order to achieve more efficiency. The designed algorithms and a classical information retrieval method were implemented for comparison purposes. Experiments were conducted on CACM, RCV1 and random benchmarks. Numerical results show that ACO is scalable while achieving the same performance as the traditional IR process in terms of solutions quality.
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1.1 CACM
CACM is a collection of article abstracts published in ACM journal between 1958 and 1979. Table 6 shows its characteristics. Although the designed algorithms aim at searching large scale collections of documents, they work in a good way on this small collection and outperform the exact algorithms in terms of runtime. However the collection remains too small to observe the real impact brought by the proposed approach.
1.2 RCV1
Reuters Corpus Volume I (RCV1) is a collection of more than 800.000 documents representing archives published by Reuters, Ltd. It is now publicly available for use by researchers, Table 7 shows its parameters sizes.
1.3 The Random collection
A larger random collection has been created automatically, Table 8 shows its characteristics.
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Drias, Y., Kechid, S. (2015). Hybrid ACO and Tabu Search for Large Scale Information Retrieval. In: Laalaoui, Y., Bouguila, N. (eds) Artificial Intelligence Applications in Information and Communication Technologies. Studies in Computational Intelligence, vol 607. Springer, Cham. https://doi.org/10.1007/978-3-319-19833-0_2
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DOI: https://doi.org/10.1007/978-3-319-19833-0_2
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