| 摘要 |
A compositional recommender framework using modular recommendation functions is described. Each modular recommendation function can use a discrete technology, such as using clustering, a database lookup, or other means. A first recommendation function can recommend to a user items, such as books to check out, automobiles to purchase, people to date, etc. Another modular recommendation function can be daisy chained with the first to recommend items that are similar or related to the first recommended items, such as users who have also checked out the same recommended book, trailers that can be towed by the recommended automobiles, or vacations booked by people that were recommended as people to date. The modular recommendation functions can be used to build customized recommendation engines for different industries.
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