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Collaborative recommendations using item-to-item similarity mappings

Patent 6266649 Issued on July 24, 2001. Estimated Expiration Date: Icon_subject September 18, 2018. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.

Patent References

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More ...

Inventors

Application

No. 157198 filed on 09/18/1998

US Classes:

705/26, Electronic shopping (e.g., remote ordering)705/14, Distribution or redemption of coupon, or incentive or promotion program705/27Presentation of image or description of sales item (e.g., electronic catalog browsing)

Examiners

Primary: Hafiz, Tariq R.
Assistant: Harle, J

Attorney, Agent or Firm

Foreign Patent References

  • 0 265 083 EP 04/10/1988
  • 0751471 EP 01/10/1997
  • 0 827 063 A1 EP. 03/10/1998
  • 2 336 925 GB 04/10/1988

International Class

G06F 017/60

Abstract

A recommendations service recommends items to individual users based on a set of items that are known to be of interest to the user, such as a set of items previously purchased by the user. In the disclosed embodiments, the service is used to recommend products to users of a merchant's Web site. The service generates the recommendations using a previously-generated table which maps items to lists of "similar" items. The similarities reflected by the table are based on the collective interests of the community of users. For example, in one embodiment, the similarities are based on correlations between the purchases of items by users (e.g., items A and B are similar because a relatively large portion of the users that purchased item A also bought item B). The table also includes scores which indicate degrees of similarity between individual items. To generate personal recommendations, the service retrieves from the table the similar items lists corresponding to the items known to be of interest to the user. These similar items lists are appropriately combined into a single list, which is then sorted (based on combined similarity scores) and filtered to generate a list of recommended items. Also disclosed are various methods for using the current and/or past contents of a user's electronic shopping cart to generate recommendations. In one embodiment, the user can create multiple shopping carts, and can use the recommendation service to obtain recommendations that are specific to a designated shopping cart. In another embodiment, the recommendations are generated based on the current contents of a user's shopping cart, so that the recommendations tend to correspond to the current shopping task being performed by the user.

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