similarity_matrix[userID][otheruserId] = np.corrcoef(np.array(vec_user), np.array(vec_otheruser))[0][1] #similarity_matrix[userID][otheruserId] = cosine_similarity(np.array(vec_user), np.array(vec_otheruser))[0][1] 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14....
similarityMatrix[j][i] = similarityMatrix[i][j]; } } }privatedoublecalculateSimilarity(intuser1,intuser2){// 这里使用皮尔逊相关系数计算相似度,可根据需要选择其他方法doublesum1=0;doublesum2=0;doublesumProduct=0;intn=userRatedItems.get(user1).size();for(inti=0; i < n; i++) { sum1 +...
double[][]matrix={{1,2,0},{2,0,1},{0,1,2}}; 1. 2. 3. 4. 5. 步骤2: 计算余弦相似度 我们需要编写一个方法来计算两向量之间的余弦相似度。具体代码如下: publicclassCosineSimilarity{publicstaticdoublecosineSimilarity(double[]vectorA,double[]vectorB){doubledotProduct=0.0;doublenormA=0.0;doub...
double sim = cosineSimilarity(u1, u2);if (sim > 0) { if (!similarityMatrix.containsKey(u1)) { similarityMatrix.put(u1, new HashMap<>());} similarityMatrix.get(u1).put(u2, sim);} } } return similarityMatrix;} private double cosineSimilarity(int u1, int u2) { Set<Integer> commonIt...
Overall what I did is, I first calculate the TfIdf matrix of all the documents and then document vectors of each documents. Then I used those document vectors to calculate cosine similarity. You think clarification is not enough. Hit me.. ...
= targetItem) { double similarity = cosineSimilarity(userItemMatrix[i], targetVector); similarityMap.put(i, similarity); } } return similarityMap.entrySet().stream() .sorted(Map.Entry.<Integer, Double>comparingByValue().reversed()) .limit(topN) .map(Map.Entry::getKey) .toList();...
("A Faster Algorithm Computing String Edit Distances"). This method splits the matrix in blocks of size t x t. Each possible block is precomputed to produce a lookup table. This lookup table can then be used to compute the string similarity (or distance) in O(nm/t). Usually, t is ...
getLayerSize()); return cosineSimilarity(avgVector1, avgVector2); } 7.计算一组向量的平均值向量 代码语言:java AI代码解释 /** * 计算一组向量的平均值向量 * @param vectors 向量列表 * @param modelSize 向量维度大小 * @return 平均向量 */ private static INDArray getAverageVector(List<INDArray>...
tfidf_matrix = tfidf_vectorizer.fit_transform(movies[‘genres’].apply(lambda x: ‘‘.join(x))) 计算电影之间的相似度 cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix) 定义一个函数来根据电影标题获取推荐列表 def get_recommendations(title): idx = movies.index[movies[‘title’] ==...
self.user_similarity_matrix=cosine_similarity(self.user_item_matrix)returnself.user_similarity_matrixdefrecommend(self,user_index,num_recommendations=5):# 计算目标用户与其他用户的相似度ifself.user_similarity_matrixisNone:self.calculate_user_similarity()# 获取目标用户的相似度向量similarity_scores=self....