RECOMMENDATION METHOD AND RECOMMENDER COMPUTER SYSTEM USING DYNAMIC LANGUAGE MODEL

• US 20120239382A1
• Filed: 07/25/2011
• Published: 09/20/2012
• Est. Priority Date: 03/18/2011
• Status: Abandoned Application

• Current Assignee: Industrial Technology Research Institute
• Original Assignee: Industrial Technology Research Institute
• Inventors: Shen, Min-Hsin, Chiu, Chung-Jen, Lee, Ching-hsien
• Application Number:
  US13/190,007
• Publication Number:
  US 20120239382A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536

Abstract

A recommendation method and a recommender computer system using dynamic language model are provided. The recommender computer system using dynamic language model includes a language model constructing computer module, a language model adapting computer module, a sentence selecting computer module and a sentence recommendation computer module. The language model constructing computer module is used for constructing a language model. The language model adapting computer module is used for dynamically emerging different language models to construct a dynamic language model. The sentence selecting computer module generates a plurality of recommended sentences from a database according to a search keyword. The sentence recommendation computer module analyzes the difference level between the recommended sentences and the dynamic language model and sorts recommended sentences to provide a recommendation list.

Claims

1. A recommendation method using dynamic language model, comprising:

providing one or a plurality of sentences by at least a computer peripheral device, wherein the one or a plurality of sentences comprises a plurality of words;

analyzing a plurality of word occurrence probabilities of the one or a plurality of sentences by at least an electric element;

analyzing a plurality of word continuation probabilities among the words by at least an electric element;

constructing one or a plurality of language models according to the word occurrence probabilities and the word continuation probabilities by at least an electric element;

emerging the one or a plurality of language models to construct a dynamic language model by at least an electric element;

providing a search keyword to generate a plurality of recommended sentences by search process according to the search keyword by at least a computer peripheral device;

analyzing a difference level between each of the recommended sentences and the dynamic language model in terms of the word occurrence probabilities and the word continuation probabilities so as to generate a plurality of difference levels by at least an electric element; and

sorting the recommended sentences according to the difference levels to provide a recommendation list by at least an electric element.

2. The recommendation method using dynamic language model according to claim 1, wherein the search keyword is a name of a book, and the recommended sentences are the content of the book.

3. The recommendation method using dynamic language model according to claim 1, wherein the search keyword is a word or a phrase, the recommended sentences are a plurality of exemplary sentences or a plurality of semantic interpretations of the word or the phrase.

4. The recommendation method using dynamic language model according to claim 1, wherein the step of providing the one or a plurality of sentences comprises:

providing a read book that has been read by a user; and

fetching the one or a plurality of sentences according to the content of the read book.

5. The recommendation method using dynamic language model according to claim 1, wherein the one or a plurality of language models comprises at least an initial language model or one or a plurality of adaptive language models.

6. The recommendation method using dynamic language model according to claim 5, wherein the step of providing the one or a plurality of sentences comprises:

providing a background data of a user; and

providing the one or a plurality of sentences to construct the initial language model according to the background data of the user.

7. The recommendation method using dynamic language model according to claim 5, wherein in the step of constructing the dynamic language models, the one or a plurality of adaptive language models and the previously constructed dynamic language model are merged to update the dynamic language model.

8. A recommender computer system using dynamic language model, comprising:

a language model constructing computer module used for analyzing a plurality of word occurrence probabilities of a plurality of words of one or a plurality of sentences and a plurality of word continuation probabilities among the words, and constructing one or a plurality of language models according to the word occurrence probabilities and the word continuation probabilities by at least an electric element;

a language model adapting computer module comprising an adapting unit for constructing a dynamic language model according to the one or a plurality of language models by at least an electric element;

a sentence selecting computer module used for generating a plurality of recommended sentences from a database containing one or a plurality of sentences by a search process according to a search keyword by at least an electric element; and

a sentence recommendation computer module used for analyzing the difference level between each of the recommended sentences and the dynamic language model in terms of the word occurrence probabilities and the word continuation probabilities so as to generate a plurality of difference levels and sort the recommended sentences according to the difference levels to provide a recommendation list by at least an electric element.

9. The recommender computer system using dynamic language model according to claim 8, wherein the language model constructing computer module, comprises:

a sentence providing unit used for providing the one or a plurality of sentences, wherein the one or a plurality of sentences comprises the words;

an analyzing unit used for analyzing the word occurrence probabilities of the words of the one or a plurality of sentences and analyzing the word continuation probabilities among the words; and

a constructing unit used for constructing the one or a plurality of language models according to the word occurrence probabilities and the word continuation probabilities.

10. The recommender computer system using dynamic language model according to claim 8, wherein the sentence selecting computer module, comprises:

a search clue providing unit used for providing the search keyword;

a database containing the one or a plurality of sentences; and

a searching unit used for generating the recommended sentences from the database by a search process according to the search keyword.

11. The recommender computer system using dynamic language model according to claim 8, wherein the sentence recommendation computer module, comprises:

a matching unit used for analyzing the difference level between each of the recommended sentences and the dynamic language model in terms of the word occurrence probabilities and the word continuation probabilities so as to generate a plurality of difference levels; and

a sorting unit used for sorting the recommended sentences according to the difference levels to provide a recommendation list.

12. The recommender computer system using dynamic language model according to claim 8, wherein the search keyword is a name of a book, and the recommended sentences are the content of the book.

13. The recommender computer system using dynamic language model according to claim 8, wherein the search keyword is a word or a phrase, the recommended sentences are a plurality of exemplary sentences or a plurality of semantic interpretations of the word or the phrase.

14. The recommender computer system using dynamic language model according to claim 9, wherein the sentence providing unit provides a read book that has been read by a user, and fetches the one or a plurality of sentences according to the content of the read book.

15. The recommender computer system using dynamic language model according to claim 8, wherein the one or a plurality of language models comprises at least an initial language model or one or a plurality of adaptive language models.

16. The recommender computer system using dynamic language model according to claim 9, wherein the sentence providing unit provides a background data of a user, and further provides the one or a plurality of sentences to construct the initial language model according to the background data of the user.

17. The recommender computer system using dynamic language model according to claim 8, wherein the adapting unit merges the one or a plurality of adaptive language models and the previously constructed dynamic language model to update the dynamic language model.