tansaku
commented
3 years ago the strange thing is that if I reduce the amount of movielens data being used (take the first 100 mappings), I still get sensible output from the model, e.g.
- 96 users
- 1665 movies
- 100 mappings
gives the following
ranking, titles = index(np.array(["1"]))
print(f"Top 10 recommendations for user {user_ids_vocabulary.get_vocabulary()[1]}: {titles}")
print(f"thing: {ranking}")
ranking, titles = index(np.array(["2"]))
print(f"Top 10 recommendations for user {user_ids_vocabulary.get_vocabulary()[2]}: {titles}")
print(f"thing: {ranking}")Top 10 recommendations for user 699: [[b'Harold and Maude (1971)' b'Rock, The (1996)'
b'Mulholland Falls (1996)' b'Four Weddings and a Funeral (1994)'
b'Aladdin (1992)' b'Sense and Sensibility (1995)' b'Local Hero (1983)'
b'Jungle2Jungle (1997)' b"Antonia's Line (1995)"
b'Man Without a Face, The (1993)']]
thing: [[0.16602841 0.14439449 0.13890035 0.13128598 0.11802915 0.11523528
0.11077308 0.10783543 0.09914517 0.09864655]]
Top 10 recommendations for user 663: [[b'Harold and Maude (1971)' b'Rock, The (1996)'
b'Mulholland Falls (1996)' b'Four Weddings and a Funeral (1994)'
b'Aladdin (1992)' b'Sense and Sensibility (1995)' b'Local Hero (1983)'
b'Jungle2Jungle (1997)' b"Antonia's Line (1995)"
b'Man Without a Face, The (1993)']]
thing: [[0.16602841 0.14439449 0.13890035 0.13128598 0.11802915 0.11523528
0.11077308 0.10783543 0.09914517 0.09864655]]implying that the number of mappings is not an issue - so could it just be the number of possible values of user id and movies - i.e. we've got over a 1000 movies, but the <100 interests in my data is the problem?
But even if I reduce the number of movies to 100 I still get great recommendations (although they are the same for each user) - so either I've got some silly broken thing in my approach, or my reduction in movielens data is not actually coming through due to some caching mechanism ...
GaetanDu
williamberrios
maciejkula
jasonlevine
Having successfully got my data into the correct format I've been able to run the quickstart recommender on my own dataset. However for some reason on a given run every user gets identical recommendations for the same item over and over again.
Looking at the movielens data in the same I see that it has the following properties:
My own data set has the following properties
I think I've gotten the data into the correct format. For example, the movie data mappings are like this:
while I've now got the interest data similarly structured like so
however the recommended interests with my dataset are locked into the same thing each time.
Running with the movielens data there's a nice spread of recommendations, and the training output is like this:
and we can see a nice pattern of recommendations for users:
but for the interests data we get this:
and here's the details for the first two users:
On different runs the recommendation might recommend a different interest, but it always gets stuck with the same one for all users. I've tried longer training runs, but I'm starting to wonder if there's a need to have some minimum number of "ratings" per user? I think that's the main difference between the data sets, i.e. 100 ratings for each user in the movielens dataset, but only five in my interests dataset. Or could it be the number of possible interests is just too small?
Or I'm making some stupid mistake in the code (in the interest vocabulary lookup table perhaps?). What are the requirements on the dataset size to allow this model to work?
Is there anything in the output I show above to indicate what's going wrong? The net getting stuck in a local minima perhaps?
I've tried longer training runs but that doesn't seem to make any difference. Perhaps the settings for the retrieval model need to be different given the fewer "ratings" or some other difference in the dataset proportions?