Content Based Similarity Detection. A computer implemented method includes computing a hash of each word in a collection of books to produce a numerical integer token using a reduced representation and computing an Inverse Document Frequency (IDF) vector comprising the number of books the token appears in, for every token in the collection of books. The method also includes creating a token occurrence count vector for each book in the collection and normalizing the token occurrence count vector using the IDF vector to create a Term Frequency-Inverse Document Frequency (TF-IDF) vector. Further, the method includes reducing each TF-IDF vector by using random projections to obtain a final signature representing each book in the collection, reducing each TF-IDF vector by using random projections to obtain a final signature representing each book in the collection and using a trained machine learning algorithm, determining whether each of the list of candidate books is similar to the target book.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method comprising: computing a hash of each word in a collection of books to produce a numerical integer token using a reduced representation; computing an Inverse Document Frequency (IDF) vector comprising the number of books said token appears in, for every token in said collection of books; creating a token occurrence count vector for each said book in said collection; normalizing said token occurrence count vector using said IDF vector to create a Term Frequency-Inverse Document Frequency (TF-IDF) vector; reducing each said TF-IDF vector by using random projections to obtain a final signature representing each said book in said collection; creating at least two similarity scores between a target book and a list of candidate books; and using a trained machine learning algorithm, determining whether each of said list of candidate books is similar to said target book. 2. The computer implemented method of claim 1 further comprising: creating an efficient data structure to create a reduced list of potentially matching books for each said book in said collection to reduce computational costs. 3. The computer implemented method of claim 1 wherein said reducing comprises computing a term frequency-inverse document frequency representation of said word count vector. 4. The computer implemented method of claim 3 wherein said reducing comprises computing a hash of said frequency-inverse document frequency representation of said work count vector. 5. The computer implemented method of claim 4 wherein said hash comprises a MurmurHash function. 6. The computer implemented method of claim 1 wherein said creating comprises removing a first term of a reduced size vector. 7. The computer implemented method of claim 1 wherein said determining comprises a Jaccard similarity. 8. An article of manufacture including a computer readable medium having instructions stored thereon that, responsive to execution by an electronic system, cause said electronic system to perform operations comprising: computing a hash of each word in a collection of books to produce a numerical integer token using a reduced representation; computing an Inverse Document Frequency (IDF) vector comprising the number of books said token appears in, for every token in said collection of books; creating a token occurrence count vector for each said book in said collection; normalizing said token occurrence count vector using said IDF vector to create a Term Frequency-Inverse Document Frequency (TF-IDF) vector; reducing each said TF-IDF vector by using random projections to obtain a final signature representing each said book in said collection; creating at least two similarity scores between a target book and a list of candidate books; and using a trained machine learning algorithm, determining whether each of said list of candidate books is similar to said target book. 9. The article of manufacture of claim 8 wherein said operations further comprise: creating an efficient data structure to create a reduced list of potentially matching books for each book in a comparison space to reduce computational costs. 10. The article of manufacture of claim 8 wherein said reducing comprises computing a term frequency-inverse document frequency representation of said word count vector. 11. The article of manufacture of claim 10 wherein said reducing comprises computing a hash of said frequency-inverse document frequency representation of said work count vector. 12. The article of manufacture of claim 11 wherein said computing a hash comprises a MurmurHash function. 13. The article of manufacture of claim 8 wherein said creating comprises removing a first term of a reduced size vector. 14. The article of manufacture of claim 8 wherein said determining comprises a Jaccard similarity. 15. An electronic system comprising: one or more processors; a memory coupled to said one or more processors, wherein said memory comprises a plurality of electronic books; wherein said electronic system is configured to: compute a hash of each word in a collection of books to produce a numerical integer token using a reduced representation; compute an Inverse Document Frequency (IDF) vector comprising the number of books said token appears in, for every token in said collection of books; create a token occurrence count vector for each said book in said collection; normalize said token occurrence count vector using said IDF vector to create a Term Frequency-Inverse Document Frequency (TF-IDF) vector; reduce each said TF-IDF vector by using random projections to obtain a final signature representing each said book in said collection; create at least two similarity scores between a target book and a list of candidate books; and using a trained machine learning algorithm, determine whether each of said list of candidate books is similar to said target book. 16. The electronic system of claim 15 further configured to: reduce said size of each said word count vector to create a reduced size vector by computing a term frequency-inverse document frequency representation of said word count vector. 17. The electronic system of claim 16 further configured to: reduce said size of each said word count vector to create a reduced size vector by computing a hash of said frequency-inverse document frequency representation of said work count vector. 18. The electronic system of claim 16 wherein said hash comprises a MurmurHash function. 19. The electronic system of claim 15 further configured to: create several types of similarity scores amount all pairs of said books by using several measures of similarities by removing a first and second terms of a reduced size vector. 20. The electronic system of claim 15 further configured to: determine whether two books are similar based on a comparison of more than one type of said similarity scores by using a Jaccard similarity.