Collaborative-filtering content model for recommending items
View Patent ↗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.
1. 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.
2. The method of claim 1 , further comprising:
serving information relating to the one or more recommended items to the current user, wherein:
information relating to the one or more recommended items comprises text, an image, a hyperlink, video, or audio.
3. The method of claim 1 , further comprising:
storing the plurality of documents representing the plurality of items in a repository.
4. The method of claim 1 , wherein an item further comprises a set of two or more items or a specific sequence of two or more items.
5. The method of claim 1 , wherein the plurality of similarity measurements comprises a plurality of different levels of similarity.
6. The method of claim 1 , wherein the predicated rating of an item comprises the rating predicted to be given by the current user for the item.
7. The method of claim 1 , wherein determining the one or more recommended items further comprises selecting the items having the top Y predicated ratings, Y comprising a predetermined number.
8. The method of claim 1 , wherein:
an attribute/value pair comprises an attribute and a value for the attribute describing an item; and
taxonomy information comprises category and hierarchical information describing an item.
9. The method of claim 1 , wherein metadata in a document representing an item further comprises at least one keyword associated with the item or at least one search term associated with the item.
10. 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.
11. The system of claim 10 , wherein:
the scoring module is further configured for sewing information relating to the one or more recommended items to the current user; and
information relating to the one or more recommended items comprises text, an image, a hyperlink, video, or audio.
12. The system of claim 10 , further comprising:
a repository, connected with the scoring module, for storing the plurality of documents representing the plurality of items.
13. The system of claim 10 , wherein an item further comprises a set of two or more items or a specific sequence of two or more items.
14. The system of claim 10 , wherein the plurality of similarity measurements comprises a plurality of different levels of similarity.
15. The system of claim 10 , wherein the predicated rating of an item comprises the rating predicted to be given by the current user for the item.
16. The system of claim 10 , wherein the scoring module is further configured for determining the one or more recommended items by selecting the items having the top Y predicated ratings, Y comprising a predetermined number.
17. The system of claim 10 , wherein:
an attribute/value pair comprises an attribute and a value for the attribute describing an item; and
taxonomy information comprises category and hierarchical information describing an item.
18. The system of claim 10 , wherein metadata in a document representing an item further comprises at least one keyword associated with the item or at least one search term associated with the item.