How to handle release year difference in movie recommendation - machine-learning

Question

I have been part of the movie recommendation project. We have developed a doc2vec model using gensim.
You can have a look at gensim documentation if needed.
https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.most_similar
Trained the model and when i took top 10 similar movies for a film based on cast it gives way back old movies with release_yr as (1960, 1950, ...). So i have tried including the release_yr as a parameter to gensim model but still it shows me old movies. How can i solve this release_yr difference? When I see top10 recommendations for a film I need those movies whose release_yr difference is less (like past 10 years movies not more than that). How can i do that?
code for doc2vec model
def d2v_doc(titles_df):
tagged_data = [TaggedDocument(words=_d, tags=[str(titles_df['id_titles'][i])]) for i, _d in enumerate(titles_df['doc'])]
model_d2v = Doc2Vec(vector_size=300,min_count=10, dm=1)
model_d2v.build_vocab(tagged_data)
model_d2v.train(tagged_data,epochs=100,total_examples=model_d2v.corpus_count)
return model_d2v
titles_df dataframe contains columns(id_titles, title, release_year, actors, director, writer, doc)
col_names = ['actors', 'director','writer','release_year']
titles_df['doc'] = titles_df[col_names].apply(lambda x: ' '.join(x.astype(str)), axis=1).str.split()
Code for Top 10 similar movies
def titles_lookup(similar_doc,titles_df):
df = pd.DataFrame(similar_doc, columns =['id_titles', 'simialrity'])
df = pd.merge(df, titles_df[['id_titles','title','release_year']],on='id_titles',how='left')
print(df)
def demo_d2v_title(model,titles_df, id_titles):
similar_doc = model.docvecs.most_similar(id_titles)
titles_lookup(similar_doc,titles_df)
def demo(model,titles_df):
print('hunt for red october')
demo_d2v_title(model,titles_df, 'tt0099810')
The output for Top 10 similar movies for film - "hunt for red october"
id_titles similarity title release_year
0 tt0105112 0.541722 Patriot Games 1992.0
1 tt0267626 0.524941 K19: The Widowmaker 2002.0
2 tt0112740 0.496758 Crimson Tide 1995.0
3 tt0052151 0.471951 Run Silent Run Deep 1958.0
4 tt1922685 0.464007 Phantom 2013.0
5 tt0164184 0.462187 The Sum of All Fears 2002.0
6 tt0058962 0.459588 The Bedford Incident 1965.0
7 tt0109444 0.456760 Clear and Present Danger 1994.0
8 tt0063121 0.455807 Ice Station Zebra 1968.0
9 tt0146309 0.452572 Thirteen Days 2001.0
you can see from the output that i'm still getting old movies. Please help me how to solve that.
Thanks in advance.

Answer 1

Doc2Vec only knows text-similarity; it doesn't have the idea of other fields.
So if you want to discard matches according to some criteria other than text-similarity, that's only represented external to the Doc2Vec model, you'll have to do that in a separate step.
So, you could use .most_similar() with a topn=len(model.docvecs) parameter - to get back all moviews, ranked. Then, filter that result-set by discarding any whose year is too-far from your desired year. Then, trim that result-set to the top N that you really want.