Building user profiles based on sequences for content and collaborative filtering

•A generic method to transform users to sequences using collaborative and content-based information.•We define a user similarity metric based on LCS algorithm (extensible to any string-based comparison algorithm) than can produce competitive recommendations.•Definition of various parameters (confidence, preference, normalizations, and threshold) to produce better recommendations in the LCS-based algorithm.•Normalization functions help to improve accuracy without hurting diversity or novelty.•Preference filtering does not decrement the performance but reduces its computational cost.