def process_recommendations(entities, scores, n=10):
    r = sum([Counter({e.items[i]: e.scores[i] * scores[e.key.id()]
        for i in range(len(e.items))}) for e in entities], Counter()).items()
    heapq.heapify(r)
    return {'result': [{"item": k, "score": v} for k, v in heapq.nlargest(
        n, r, key= lambda x: x[1])]}