METHOD AND SYSTEM OF GENERATING GUIDANCE INFORMATION

• US 20110004508A1
• Filed: 07/02/2009
• Published: 01/06/2011
• Est. Priority Date: 07/02/2009
• Status: Abandoned Application

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Huang, Shen, Zhang, Xiaodi, Shen, Dan, Wang, Oiang
• Application Number:
  US12/497,248
• Publication Number:
  US 20110004508A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls

Abstract

One embodiment provides a system for generating questions related to product aspects. The system may comprise: a product review analyzer to extract product values associated with the products from product reviews, in which the product values may include product categories, product aspects and product aspect evaluations; a product review summary builder to build product review summaries based on the extracted product values associated with the products; and a question generator to generate a set of questions regarding the product aspects based on the product review summaries.

Claims

1. A system comprising:

a product review analyzer to extract product values associated with products from product reviews, the product values including product categories, product aspects and product aspect evaluations;

a product review summary builder to build product review summaries based on the extracted product values associated with the products; and

a question generator to generate a set of questions regarding the product aspects based on the product review summaries, wherein each question is associated with each product aspect of each product category.

2. The system of claim 1, wherein the product review analyzer includes a learning machine, the learning machine to analyze the product review summaries by utilizing training data to extract the product values associated with the products.

3. The system of claim 2, wherein the training data includes labeled samples related to product aspect tasks and sentiment tasks to enable the learning machine to extract the product aspects and the product aspect evaluations associated with the products.

4. The system of claim 1, further comprising a question filter to select a predetermined amount of filtered questions from the set of generated questions based on an input from the customer, wherein the predetermined amount of filtered questions are presented to the customer via a display.

5. The system of claim 4, further comprising a question organizer to rank the filtered questions as a function of aspect review frequencies and data indicating positive aspect evaluations associated with each product aspect of each product category.

6. The system of claim 1, further comprising a product review library to collect the product reviews associated with the products.

7. A system, comprising:

a question generator to generate a set of questions regarding product aspects based on product review summaries related to products, wherein the products are associated with product values, the product values including product categories, product aspects and product aspect evaluations, and wherein each question is associated with each product aspect of each product category;

a question filter to select a predetermined amount of filtered questions from the set of generated questions based on an input from the customer;

a question organizer to organize the filtered questions to be presented to the customer via a display;

an answer receiver to receive answers to the filtered questions; and

a product advisor to propose a list of products from the products based on the answers to the filtered questions.

8. The system of claim 7, wherein the filtered questions are ranked, by the question organizer, as a function of aspect review frequencies and data indicating positive aspect evaluations associated with the product aspects.

9. The system of claim 7, wherein the list of proposed products are ranked, by the product organizer, as a function of the aspect evaluations that match an interest expressed in the answer from the customer.

10. The system of claim 7, further comprising a product review analyzer to extract the product values of the products from the product reviews by analyzing the product reviews with training data.

11. The system of claim 10, wherein the training data includes labeled samples related to product aspect tasks and sentiment tasks to enable the learning machine to extract the product aspects and the product aspect evaluations associated with the products.

12. The system of claim 7, further comprising a product review summary builder to build the product review summaries based on the extracted product values associated with the products.

13. A computer implemented method comprising:

collecting, in a product review library, product reviews associated with products;

extracting, via a product review analyzer, product values associated with the products by analyzing the product reviews, the product values including product categories, product aspects and product aspect evaluations;

building, at a product review summary builder, product review summaries based on the extracted product values associated with the products; and

generating, at a question generator, at least one question associated with one of the product aspects based on the product review summaries.

14. The method of claim 13, wherein the product review analyzer analyzes the product reviews by a learning machine to extract product values associated with the products.

15. The method of claim 14, wherein training data is used by the learning machine to extract the product aspects and the product aspect evaluations associated with the products, the training data includes labeled samples related to product aspect tasks and sentiment tasks.

16. The method of claim 13, further comprising:

selecting, by a question filter, a predetermined number of filtered questions from the set of questions based on an input from the customer to present to the customer via a display, wherein the filtered questions are ranked, by a question organizer, as a function of aspect review frequencies and positive aspect evaluations associated with the product aspects.

17. The method of claim 16, further comprising:

proposing, by a product advisor, a list of products selected from the products based on an answer to the one or more the filtered questions to present to the customer via the display, wherein the proposed products are ranked, by a product organizer, as a function of the aspect evaluations that match an interest expressed in the answer from the customer.

18. A machine-readable medium comprising instructions, which when executed by one or more processors, perform the following operations:

collecting, in a product review library, product reviews associated with products;

extracting, via a product review analyzer, product values associated with the products by analyzing the product reviews, the product values including product categories, product aspects and product aspect evaluations;

building, by a product review summary builder, product review summaries based on the extracted product values associated with the products; and

generating, by a question generator, a set of questions associated with the product aspects based on the product review summaries, wherein each question is associated with each product aspect.

19. The machine-readable medium of claim 18, wherein the instructions, when executed by the one or more processors, further perform the following operation:

selecting, by a question filter, a predetermined number of filtered questions from the set of questions based on an input from the customer to present to the customer via a display, wherein the filtered questions are ranked, by a question organizer, as a function of aspect review frequencies and positive aspect evaluations associated with the product aspects.

20. The machine-readable medium of claim 18, wherein the instructions, when executed by the one or more processors, further perform the following operation:

proposing, by a product advisor, a list of products selected from the products based on an answer to the one or more the filtered questions to present to the customer via the display, wherein the proposed products are ranked, by a product organizer, as a function of the aspect evaluations that match an interest expressed in the answer from the customer.