A novel recommendation approach based on chronological cohesive units in content consuming logs
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- @Article{KIM:2019:IS,
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author = "Jaekwang Kim and Jee-Hyong Lee",
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title = "A novel recommendation approach based on chronological
cohesive units in content consuming logs",
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journal = "Information Sciences",
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volume = "470",
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pages = "141--155",
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year = "2019",
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keywords = "genetic algorithms, genetic programming, Chronological
cohesive unit, Collaborative filtering, Association
rules, Sequential log",
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ISSN = "0020-0255",
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DOI = "doi:10.1016/j.ins.2018.08.046",
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URL = "http://www.sciencedirect.com/science/article/pii/S0020025516308258",
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abstract = "We propose a novel recommendation approach based on
chronological cohesive units (CCUs) of content
consuming logs. Chronological cohesive units are
defined as sub-sequences of logs in which items are
highly related to each other. We first generate rules
for splitting consuming logs into CCUs. We select
features which are effective for splitting of consuming
logs and combine them into a binary decision tree to
generate splitting rules with genetic programming. With
the rules, we split content consuming logs into CCUs,
and identify strongly associated items in the CCUs.
Next items are recommended with an association
rule-based approach. The proposed method is evaluated
using two-real datasets: web page navigation logs and
movie consuming logs. The experiments confirm that the
proposed approach is superior to the existing methods
in various aspects such as hit ratio, click-soon ratio,
sparsity, diversity and serendipity",
- }
Genetic Programming entries for
Jaekwang Kim
Jee-Hyong Lee
Citations