A novel recommendation approach based on chronological cohesive units in content consuming logs

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.