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A review of evolutionary and immune-inspired information filtering

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Abstract

In recent years evolutionary and immune-inspired approaches have been applied to content-based and collaborative filtering. These biologically inspired approaches are well suited to problems like profile adaptation in content-based filtering and rating sparsity in collaborative filtering, due to their distributed and dynamic characteristics. In this paper we introduce the relevant concepts and algorithms and review the state of the art in evolutionary and immune-inspired information filtering. Our intention is to promote the interplay between information filtering and biologically inspired computing and boost developments in this emerging interdisciplinary field.

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Notes

  1. For a more general description of EAs see Eiben and Smith (2003).

  2. Other crossover techniques include uniform crossover and arithmetic crossover (Eiben and Smith 2003).

  3. For further details on the immune system the interested reader is referred to immunology textbooks, like Kirkwood and Lewis (1989).

  4. Textbooks in AIS include Dasgupta (1998), de Castro and Timmis (2002), and Tarakanov et al. (2003).

  5. In Table 1, “\(\surd\)” denotes the existence of a process, in the absence of further details.

  6. The nave Bayesian classifier “was adapted to intercept [continuous] user input in the same way as AISEC” (Secker et al. 2003).

  7. http://movielens.umn.edu.

  8. http://trec.nist.gov/data/t10_filtering/T10filter_guide.htm.

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  1. Computing Department, The Open University, Milton Keynes, UK

    Nikolaos Nanas & Anne de Roeck

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  1. Nikolaos Nanas
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  2. Anne de Roeck
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Correspondence to Nikolaos Nanas.

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Nanas, N., de Roeck, A. A review of evolutionary and immune-inspired information filtering. Nat Comput 9, 545–573 (2010). https://doi.org/10.1007/s11047-009-9126-z

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