surprise_build_recommendations_two_stage.py

for idx, row in dfp_archetypes.iterrows():
    print("ARCHETYPE:", row["userid"])
    inventory_positive = dfp_inventory_positive.loc[row["userid"]]
    
    # Get the candidates
    buffer = []
    for contentid in inventory_positive:
        closest_contentids = get_closest_neighbors(model_retriever_items, contentid, 10, type_="item")
        buffer.extend(closest_contentids)
    sp_count_contentids = pd.Series(dict(Counter(buffer))).sort_values(ascending=False)
    candidates = sp_count_contentids.index.tolist()
    dfp_items_candidates = dfp_items[dfp_items["contentid"].isin(candidates)]
    
    # Build the recommendations
    inventory = dfp_inventory.loc[row["userid"]]
    dfp_recommendations = build_recommendations(model_ranker, row["userid"], inventory, dfp_items_candidates)