US 7,584,171 B2 · Collaborative-filtering content model for recommending items

Abstract

Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

Claims18

Dependent claims

A method for implementing a collaborative-filtering based recommendation system for recommending one or more items among a plurality of items to a current user of a network, an item representing a product, service, webpage, audio, or article, the method comprising:

producing a model using a plurality of documents generated for the plurality of items, each document representing an item and comprising item information describing the item, the item information being encoded as metadata into the document, the metadata comprising at least one attribute/value pair and taxonomy information describing the item, the model comprising a plurality of similarity measurements, each similarity measurement reflecting a level of similarity between two items in the plurality of items, wherein the level of similarity between two items is determined using the two documents representing the two items and is not based on user ratings of the two items;
receiving a first rating of a first item from the current user;
and
determining the one or more recommended items by producing a predicated rating for each item in the plurality of items, the predicated rating of an item being produced using the received first rating and a similarity measurement, retrieved from the model, that reflects a level of similarity between the item and the first item.

1 more element

A system for implementing a collaborative-filtering based recommendation system for recommending one or more items among a plurality of items to a current user of a network, an item representing a product, service, webpage, audio or article, the system comprising:

a server computer system comprising:
a scoring module configured for:
producing a model using a plurality of documents generated for the plurality of items, each document representing an item and comprising item information describing the item, the item information being encoded as metadata into the document, the metadata comprising at least one attribute/value pair and taxonomy information describing the item, the model comprising a plurality of similarity measurements, each similarity measurement reflecting a level of similarity between two items in the plurality of items, wherein the level of similarity between two items is determined using the two documents representing the two items and is not based on user ratings of the two items;
receiving a first rating of a first item from the current user;
and
determining the one or more recommended items by producing a predicated rating for each item in the plurality of items, the predicated rating of an item being produced using the received first rating and a similarity measurement, retrieved from the model, that reflects a level of similarity between the item and the first item.

3 more elements

Dependencies are inferred from the claim text.

Collaborative-filtering content model for recommending items

Life of this patent

Collaborative-filtering content model for recommending items

Life of this patent

Abstract

Claims18