I have two lists: one, the interests of the user; and second, the keywords about a book. I want to recommend the book to the user based on his given interests list. I am usi
Firstly a word can have many senses and when you try to find similar words you might need some word sense disambiguation http://en.wikipedia.org/wiki/Word-sense_disambiguation.
Given a pair of words, if we take the most similar pair of senses as the gauge of whether two words are similar, we can try this:
from nltk.corpus import wordnet as wn
from itertools import product
wordx, wordy = "cat","dog"
sem1, sem2 = wn.synsets(wordx), wn.synsets(wordy)
maxscore = 0
for i,j in list(product(*[sem1,sem2])):
score = i.wup_similarity(j) # Wu-Palmer Similarity
maxscore = score if maxscore < score else maxscore
There are other similarity functions that you can use. http://nltk.googlecode.com/svn/trunk/doc/howto/wordnet.html. The only problem is when you encounter words not in wordnet. Then i suggest you fallback on difflib.