ASSESSING QUALITY OF USER REVIEWS

• US 20110173191A1
• Filed: 01/14/2010
• Published: 07/14/2011
• Est. Priority Date: 01/14/2010
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Lu, Yue, Polanyi, Livia, Ntoulas, Alexandros, Tsaparas, Panayiotis
• Granted Patent: US 8,990,124 B2
• Time in Patent Office: Days
• US Class Current: 707/723
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

User generated reviews and scores associated with the reviews may be used to train a review scoring model with textual features of the reviews. The review scoring model may be used to predict scores for newly received reviews. One or more constraints based on social networking application data associated with an author of a review may be used to adjust the predicted score of the review.

Claims

1. A method comprising:

receiving a review at a computer device through a network, wherein the review has an associated author;

receiving metadata associated with the author at the computer device through the network;

determining a feature vector for the review based on the text of the review by the computer device; and

determining a score for the review based on the determined feature vector by the computer device.

2. The method of claim 1, wherein determining a feature vector for the review comprises performing at least one of a text-statistics based analysis, a syntactic analysis, conformity based analysis, a sentiment based analysis, or a feature based analysis.

3. The method of claim 1, wherein determining a score for the review further comprises determining a score for the review based on the determined feature vector and the metadata associated with the author.

4. The method of claim 3, wherein determining a score for the review based on the determined feature vector and the metadata associated with the author comprises:

determining a score for the review using the feature vector and a review scoring model; and

applying one or more constraints to the determined score based on the metadata associated with the author.

5. The method of claim 4, wherein the one or more constraints comprise one or more of author consistency constraints, link consistency constraints, co-citation constraints, or trust consistency constraints.

6. The method of claim 5, wherein the metadata comprises social networking data.

7. The method of claim 6, wherein the social networking data comprises a number of nodes that are connected to a node associated with the author.

8. The method of claim 6, wherein the social networking data comprises a representative review score associated with authors associated with nodes that are connected to a node associated with the author.

9. The method of claim 1, further comprising associating the determined score with the review.

10. The method of claim 1, further comprising determining one or more features from the metadata.

11. A method comprising:

receiving a plurality of reviews through a network by a computer device, wherein each review has an associated author and an associated score;

receiving metadata associated with each author associated with a review from the plurality of reviews through the network by the computer device;

determining a feature vector for each review from the plurality of reviews using the received metadata associated with the author of the review and the text of each review by the computer device; and

training a review scoring model using the feature vector for each review from the plurality of reviews and the associated scores by the computer device.

12. The method of claim 11, further comprising:

receiving a review, wherein the review is a review other than the reviews from the plurality of reviews;

determining a feature vector for the review; and

generating a score for the review using the feature vector for the review and the review scoring model.

13. The method of claim 12, further comprising receiving metadata associated with the received review, and applying one or more constraints to the generated score using the received metadata and the metadata associated with each author associated with the plurality of reviews.

14. The method of claim 13, wherein the constraints comprise one or more of author consistency constraints, link consistency constraints, co-citation constraints, or trust consistency constraints.

15. The method of claim 14, wherein the metadata comprises social networking data.

16. The method of claim 11, wherein determining a feature vector for each review from the plurality of reviews comprises determining a value for at least one text feature based on the text of each review.

17. The method of claim 16, wherein the text features comprise one of text-statistics features, syntactic analysis features, conformity based features, sentiment based features, or product features.

18. A system comprising:

at least one computer device;

a review scoring model; and

a review scoring engine adapted to;

receive a plurality of reviews, wherein each review has an associated author and an associated score;

receive metadata associated with each author associated with a review from the plurality of reviews;

determine a feature vector for each review from the plurality of reviews using the received metadata associated with the author of the review and the text of each review; and

train the review scoring model using the feature vector for each review from the plurality of reviews and the associated scores.

19. The system of claim 18, wherein the review scoring engine is further adapted to:

receive a review other than the reviews from the plurality of reviews;

determine a feature vector for the review; and

generate a score for the review using the feature vector for the review and the review scoring model.

20. The system of claim 19, wherein the review scoring engine is further adapted to:

receive metadata associated with the received review; and

apply one or more constraints to the generated score using the received metadata and the metadata associated with each author associated with the plurality of reviews.