Dynamic content preview

1. A content preview system, comprising:

a content feature analysis unit to analyze a content file for content features, wherein a weighted presence of respective ones of the content features in the content file is represented by a respective percentage value; and

a preview type determination unit to:

determine a relevance of respective candidate preview types to the analyzed content file as a product of at least the respective percentage value and a respective weight value of each of the content features, wherein the weight value of each of the content features corresponds to a respective relationship between the content features and candidate preview types,

determine which of the respective candidate preview types has a highest relevance to the analyzed content file, and

select, from among the respective candidate preview types, the preview type having the highest relevance as the preview type for the content file.

2. The content preview system of claim 1 , wherein the content file is an electronic book, and

wherein the content feature analysis unit is further to analyze the electronic book, to:

determine whether the electronic book has a story structure,

determine whether the electronic book has a dictionary-like structure,

calculate a ratio between text and images in the electronic book,

calculate a ratio between text and tables in the electronic book, and

determine whether the electronic book is a specialty publication.

3. The content preview system of claim 1 , wherein the content file is an electronic book, and

wherein the content feature analysis unit is further to partition content of the electronic book into words, images and tables.

4. The content preview system of claim 3 , wherein the content feature analysis unit is further to detect a frequency of occurrence of technical terms in the electronic book.

5. The content preview system of claim 3 , wherein the content feature analysis unit is further to detect change of a frequency of occurrence of a word throughout the electronic book.

6. The content preview system of claim 3 , wherein the content feature analysis unit is further to detect a repeated structure throughout the electronic book.

7. The content preview system of claim 1 , wherein the content file is an electronic book, and

wherein the respective candidate preview types include at least one of a preview of a beginning of the electronic book, a preview of a portion of every page of the electronic book, a preview of a portion of sentences in the electronic book, a mosaic view of every page of the electronic book, an embossed view of every page of the electronic book, and a shaded view of every page of the electronic book.

8. The content preview system of claim 1 , further comprising:

an information extraction unit to extract from a database at least one of content information associated with the content file and a purchase history of a user,

wherein the preview type determination unit is further to determine an amount of a preview of the content file based at least in part on at least one of the content information and the purchase history that are extracted by the information extraction unit.

9. The content preview system of claim 8 , wherein the content information includes customer review information regarding the content file and sales information regarding the content file.

10. The content preview system of claim 1 , further comprising:

a preview generation unit to generate a preview of the content file based at least in part on the preview type determined by the preview type determination unit.

11. A content server, comprising:

a database to store at least one of a content file, content information associated with the content file, and a purchase history of a user;

a content feature analysis unit to analyze the content file for content features, wherein a weighted presence of respective ones of the content features in the content file is represented by a respective percentage value;

an information extraction unit to extract from the database at least one of the content information associated with the content file and the purchase history of the user; and

a preview type determination unit to:

determine a relevance of respective candidate preview types to the analyzed content file based at least on the respective percentage value and a respective weight value of each of the content features, the content information extracted by the information extraction unit, and the purchase history extracted by the information extraction unit, wherein the weight value of each of the content features corresponds to a respective relationship between the content features and candidate preview types,

determine which of the respective candidate preview types has a highest relevance to the analyzed content file, and

select, from among the respective candidate preview types, the preview type having the highest relevance as the preview type for the content file.

12. The content server of claim 11 , further comprising:

a preview generation unit to generate a preview of the content file based at least in part on the preview type determined by the preview type determination unit.

13. The content server of claim 12 , further comprising:

a transmitter to transmit to a device of the user the preview generated by the preview generation unit.

14. A method performed under control of a content preview system, comprising:

analyzing a content file for content features, wherein a weighted presence of respective ones of the content features in the content file is represented by a respective percentage value;

determining a relevance of each of the respective candidate preview types to the content file as a product of at least the respective percentage value and respective weight value of each of content features, wherein the weight value of each of the content features corresponds to a respective relationship between the content features and candidate preview types;

determining which of the respective candidate preview types has a highest relevance to the analyzed content file; and

selecting, from among the respective candidate preview types, the preview type having the highest relevance as the preview type for the content file.

15. The method of claim 14 , further comprising:

extracting from a database content information associated with the content file,

wherein the determining of the preview type for the content file is further based on the extracted content information.

16. The method of claim 15 , wherein the content information includes customer review information regarding the content file and sales information regarding the content file.

17. The method of claim 14 , further comprising:

extracting from a database a purchase history of a user,

wherein the determining of the preview type for the content file is further based on the extracted purchase history.

18. The method of claim 14 , further comprising:

generating a preview of the content file based at least in part on the determined preview type for transmission to a client device via a content server.

19. The method of claim 14 , wherein the content file is an electronic book, and

wherein the content features of the content file include at least one of whether the electronic book has a story structure, whether the electronic book has a dictionary-like structure, a ratio between text and images in the electronic book, a ratio between text and tables in the electronic book, and whether the electronic book is a specialty publication.

20. The method of claim 14 , wherein the content file is an electronic book, and

wherein the respective candidate preview types include at least one of a preview of a beginning of the electronic book, a preview of a portion of every page of the electronic book, a preview of a portion of sentences in the electronic book, a mosaic view of every page of the electronic book, an embossed view of every page of the electronic book, and a shaded view of every page of the electronic book.