def get_similar_news(df, news_matrix, content_idx, topN, totalN): news_similarity = [] for i in range(len(news_matrix)): sim = get_cosine_similarity(news_matrix[content_idx], news_matrix[i]) news_similarity.append(sim) news_similarity = np.array(news_similarity) # 類似度の低い順にソートした結果のインデックスを⽤意して、降順に並び替え arg_sort = np.argsort(news_similarity) arg_sort = arg_sort[::-1] return df["title"][content_idx], df["title"][arg_sort], news_similarity[arg_sort],arg_sort, df["Opinion"][arg_sort], df["Reason"][arg_sort] based_news, similar_news, similarity_scores, arg_sort, opinion, reason = ¥ get_similar_news(df=dataset, news_matrix=news_matrix, content_idx=content_idx, topN=topN,totalN=totalN) 3 . ⼿ 法 ・ 仮 説