11. A method for recommending keywords, comprising: receiving a set of product information including a product title; extracting and parsing the product title into a set of parsed elements; finding a plurality of candidate keywords corresponding to at least a subset of the set of parsed elements based at least in part on stored predetermined mappings between parsed data and keywords; determining a plurality of composite correlation scores for corresponding ones of the plurality of candidate keywords, wherein determining the plurality of composite correlation scores includes determining a first composite correlation score associated with a first candidate keyword of the plurality of candidate keywords, wherein determining the first composite correlation score associated with the first candidate keyword includes determining an industry index value associated with the first candidate keyword, including: determining a first similarity value between one or more industries associated with the first candidate keyword and one or more industries associated with sets of product information that are relevant to the first candidate keyword; determining a second similarity value between the one or more industries associated with the first candidate keyword and one or more industries of one or more seller users associated with the sets of product information that are relevant to the first candidate keyword; and determining the industry index value associated with the first candidate keyword based at least in part on a combination of the first similarity value and the second similarity value; and sorting at least a subset of the plurality of candidate keywords into a ranked list based on at least a subset of the plurality of composite correlation scores; and selecting a set of one or more keywords to recommend from the plurality of candidate keywords based at least in part on the ranked list.
11. A method for recommending keywords, comprising: receiving a set of product information including a product title; extracting and parsing the product title into a set of parsed elements; finding a plurality of candidate keywords corresponding to at least a subset of the set of parsed elements based at least in part on stored predetermined mappings between parsed data and keywords; determining a plurality of composite correlation scores for corresponding ones of the plurality of candidate keywords, wherein determining the plurality of composite correlation scores includes determining a first composite correlation score associated with a first candidate keyword of the plurality of candidate keywords, wherein determining the first composite correlation score associated with the first candidate keyword includes determining an industry index value associated with the first candidate keyword, including: determining a first similarity value between one or more industries associated with the first candidate keyword and one or more industries associated with sets of product information that are relevant to the first candidate keyword; determining a second similarity value between the one or more industries associated with the first candidate keyword and one or more industries of one or more seller users associated with the sets of product information that are relevant to the first candidate keyword; and determining the industry index value associated with the first candidate keyword based at least in part on a combination of the first similarity value and the second similarity value; and sorting at least a subset of the plurality of candidate keywords into a ranked list based on at least a subset of the plurality of composite correlation scores; and selecting a set of one or more keywords to recommend from the plurality of candidate keywords based at least in part on the ranked list. 18. The method of claim 11 , further comprising cleaning the product title prior to parsing the product title into the set of parsed elements.