This study will advance computer tailoring by adapting machine learning collective intelligence algorithms that have been used outside healthcare by companies like Amazon and Google to enhance the personal relevance of the health communication.

Smoking is still the number one preventable cause of cancer death. New approaches are needed to engage smokers in the 21st century in smoking cessation. I propose to develop S4S (Smokers for Smoker), a next-generation patient-centered computer tailored health communication (CTHC) system. Unlike current rule-based CTHCs, S4S will replace rules with complex machine learning algorithms, and use the collective experiences of thousands of smokers engaged in a web-assisted tobacco intervention to enhance personally-relevant tailoring for new smokers entering the system. The investigators will adapt collective intelligence algorithms that have been used outside healthcare by companies like Amazon and Google to enhance CTHC. Using knowledge from scientific experts, current CTHC collect baseline patient "profiles" and then use expert-written, rule-based systems to tailor messages to patient subsets. Such theory-based "market segmentation has been effective in helping patients reach lifestyle goals. However, there is a natural limit in the ability of a rule-based system to truly personalize content, and adapt personalization over time. Current CTHC have reached this limit, and the investigators propose to go beyond. The investigators first aim is to develop the Web 2.0 "S4S" recommender system. The investigators second aim is to evaluate S4S within the context of a NCI funded web-assisted tobacco intervention (Decide2Quit.org).

Inclusion Criteria:

• Current Smokers

Exclusion Criteria:

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