Embodiments relate to apparatuses and methods configured for automatic learning and/or optimizing an order of items appearing in a sequence. Particular embodiments employ an engine to recognize sequences (e.g., lists of items) repeatedly encountered by a user. Examples of such sequences can include grocery lists, and emails present in an in-box. The engine then references available metadata associated with the sequence and its items, in order to present the user with an optimized sequence tailored to one or more criteria. Examples of available metadata can include sensed location information (of the user and/or other entities), temporal information, contextual influences, historical actions by the user, and/or general population habits (e.g., as may be determined via crowdsourcing). Certain embodiments may further generate a modified sequence based upon suggestions afforded by metadata associated with the sequence. Embodiments may utilize a self-learning scoring algorithm to perform sequence recognition, optimization, and/or modification.