| 摘要 |
A hybrid approach for personalized recommendation of subject matter description is described, comprising: inputting the description into an analyzing engine, the analyzing engine performing the steps of: extracting at least one of metadata, ID and Title from the description; tokenizing the description to generate tokenized data; normalizing the tokenized data to produce Cast information; stemming the tokenized data to generate stemmed data; pattern matching the stemmed data to produce Genre information; word sense disambiguating the stemmed data to produce Feature information; tagging the word sense disambiguated data to produce Topic information; arriving a concise descriptor of the description. This information is probabilistically matched with at least one of: product placement information; customer profile information; clustering information; and collaborative filtering information; wherein the results are forwarded to a recommendation orchestrator to generate a personalized customer specific recommendation.
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