similar_item.py

item_id = 1319
n_similar = 10

item_vecs = model.item_factors
customer_vecs = model.user_factors

item_norms = np.sqrt((item_vecs * item_vecs).sum(axis=1))

scores = item_vecs.dot(item_vecs[item_id]) / item_norms
top_idx = np.argpartition(scores, -n_similar)[-n_similar:]
similar = sorted(zip(top_idx, scores[top_idx] / item_norms[item_id]), key=lambda x: -x[1])

for item in similar:
    idx, score = item
    print(grouped_df.Description.loc[grouped_df.item_id == idx].iloc[0])