RECOMMENDATIONS UTILIZING META-DATA BASED PAIR-WISE LIFT PREDICTIONS

• US 20080097821A1
• Filed: 10/24/2006
• Published: 04/24/2008
• Est. Priority Date: 10/24/2006
• Status: Abandoned Application

• Current Assignee: Rovi Technologies Corporation (Adeia Inc.)
• Original Assignee: Microsoft Corporation
• Inventors: Meek, Christopher A., Chickering, David Max, Gunawardana, Asela J.
• Application Number:
  US11/552,467
• Publication Number:
  US 20080097821A1
• Time in Patent Office: Days
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

The subject disclosure pertains to systems and methods for facilitating generation of item recommendations based at least in part upon pair-wise lift. Pair-wise lift is a measure of correlation between a pair of items and is generally calculated based upon past usage data. If usage data is insufficient or unavailable, pair-wise lift for a pair of items can be estimated based upon metadata associated with the items. In other aspects, pair-wise lift can be used to generate an explanation for recommended items. An explanation for an item recommendation can be based upon common metadata features associated with the item pair. The relative impact each metadata feature has on predicted pair-wise lift can be evaluated to determine the common feature(s) most likely to have caused the item to be recommended.

Claims

1. A system for facilitating item recommendations, comprising:

a metadata component that obtains metadata associated with a base item and a candidate item; and

a pair-wise lift predictor component that predicts pair-wise lift as a function of the metadata of the base item and the candidate item.

2. The system of claim 1, the pair-wise lift predictor component comprises a four-state model that determines probability of popularity of the base item, probability of popularity of the candidate item and probability of co-occurrence of the base item and the candidate item.

3. The system of claim 2, the four-state model comprises a linear model.

4. The system of claim 3, the model is trained utilizing sampled actual usage data.

5. The system of claim 1, the predicted pair-wise lift is based at least in part upon actual usage data.

6. The system of claim 5, the predicted pair-wise lift is based at least in part upon a combination of the actual usage data and an estimated base item popularity count, an estimated candidate item popularity count and an estimated co-occurrence count for the base item and candidate item.

7. The system of claim 1, further comprising:

a co-occurrence component that generates a co-occurrence count for the base item and the candidate item; and

a popularity component that generates a popularity count for the base item and a popularity count for the candidate item, the predicted pair-wise lift is based at least in part upon the base item popularity count, the candidate item popularity count and the co-occurrence count.

8. The system of claim 7, the co-occurrence component comprises a logistic regression model.

9. The system of claim 7, the popularity component comprises a logistic regression model.

10. The system of claim 1, further comprising a metadata evaluation component that analyzes and formats the metadata for use in pair-wise lift prediction.

11. The system of claim 1, further comprising a pair-wise lift component that generates actual pair-wise lift when sufficient usage data is available.

12. The system of claim 1, further comprising a recommendation component that selects at least one recommended item based at least in part upon the predicted pair-wise lift.

13. A method for generating explanations for item recommendations, comprising:

obtaining a base item and a recommended item;

identifying at least one common feature of the base item and the recommended item based at least in part upon metadata associated with the base item and the recommended item;

predicting pair-wise lift as a function of the metadata;

determining the effect of the at least one common feature on the predicted pair-wise lift; and

generating an explanation corresponding to the recommended item based upon the effect of the at least one common feature.

14. The method of claim 13, further comprising utilizing a greedy search algorithm to determine the at least one common feature that has the greatest impact upon the predicted pair-wise lift.

15. The method of claim 13, further comprising:

generating a natural language text string based at least in part upon the explanation; and

providing the text string to a user interface for presentation to a user.

16. The method of claim 13, predicting pair-wise lift further comprises:

predicting popularity of the recommended item as a function of the metadata;

predicting popularity of the base item as a function of the metadata;

predicting co-occurrence of the recommended item and the base item as a function of the metadata; and

computing pair-wise lift as a function of the recommended item popularity, the base item popularity and the co-occurrence.

17. The method of claim 16, the co-occurrence and the popularity are based at least in part upon a joint, four state model.

18. The method of claim 16, the co-occurrence is based at least in part upon a logistic regression model.

19. The method of claim 16, the base item popularity and the recommended item popularity are based at least in part upon a logistic regression model.

20. A system for facilitating generation of recommendations, comprising:

means for estimating an occurrence count for a base item and an occurrence count for a candidate item as a function of metadata;

means for estimating a pair-wise count for the base item and the candidate item as a function of the metadata; and

means for generating an estimated pair-wise lift based upon the base occurrence count, the candidate occurrence count and the pair-wise count.