案例–算法实现：User-Based CF 预测评分
评分预测公式：
p r e d ( u , i ) = r ^ u i = ∑ v ∈ U s i m ( u , v ) ∗ r v i ∑ v ∈ U ∣ s i m ( u , v ) ∣ pred(u,i)=\hat{r}_{ui}=\cfrac{\sum_{v\in U}sim(u,v)*r_{vi}}{\sum_{v\in U}|sim(u,v)|} pred(u,i)=r^ui=∑v∈U∣sim(u,v)∣∑v∈Usim(u,v)∗rvi
算法实现
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实现评分预测方法：
predict# ...... def predict(uid, iid, ratings_matrix, user_similar): ''' 预测给定用户对给定物品的评分值 :param uid: 用户ID :param iid: 物品ID :param ratings_matrix: 用户-物品评分矩阵 :param user_similar: 用户两两相似度矩阵 :return: 预测的评分值 ''' print("开始预测用户<%d>对电影<%d>的评分..."%(uid, iid)) # 1. 找出uid用户的相似用户 similar_users = user_similar[uid].drop([uid]).dropna() # 相似用户筛选规则：正相关的用户 similar_users = similar_users.where(similar_users>0).dropna() if similar_users.empty is True: raise Exception("用户<%d>没有相似的用户" % uid) # 2. 从uid用户的近邻相似用户中筛选出对iid物品有评分记录的近邻用户 ids = set(ratings_matrix[iid].dropna().index)&set(similar_users.index) finally_similar_users = similar_users.ix[list(ids)] # 3. 结合uid用户与其近邻用户的相似度预测uid用户对iid物品的评分 sum_up = 0 # 评分预测公式的分子部分的值 sum_down = 0 # 评分预测公式的分母部分的值 for sim_uid, similarity in finally_similar_users.iteritems(): # 近邻用户的评分数据 sim_user_rated_movies = ratings_matrix.ix[sim_uid].dropna() # 近邻用户对iid物品的评分 sim_user_rating_for_item = sim_user_rated_movies[iid] # 计算分子的值 sum_up += similarity * sim_user_rating_for_item # 计算分母的值 sum_down += similarity # 计算预测的评分值并返回 predict_rating = sum_up/sum_down print("预测出用户<%d>对电影<%d>的评分：%0.2f" % (uid, iid, predict_rating)) return round(predict_rating, 2) if __name__ == '__main__': ratings_matrix = load_data(DATA_PATH) user_similar = compute_pearson_similarity(ratings_matrix, based="user") # 预测用户1对物品1的评分 predict(1, 1, ratings_matrix, user_similar) # 预测用户1对物品2的评分 predict(1, 2, ratings_matrix, user_similar) -
实现预测全部评分方法：
predict_all# ...... def predict_all(uid, ratings_matrix, user_similar): ''' 预测全部评分 :param uid: 用户id :param ratings_matrix: 用户-物品打分矩阵 :param user_similar: 用户两两间的相似度 :return: 生成器，逐个返回预测评分 ''' # 准备要预测的物品的id列表 item_ids = ratings_matrix.columns # 逐个预测 for iid in item_ids: try: rating = predict(uid, iid, ratings_matrix, user_similar) except Exception as e: print(e) else: yield uid, iid, rating if __name__ == '__main__': ratings_matrix = load_data(DATA_PATH) user_similar = compute_pearson_similarity(ratings_matrix, based="user") for i in predict_all(1, ratings_matrix, user_similar): pass -
添加过滤规则
def _predict_all(uid, item_ids, ratings_matrix, user_similar): ''' 预测全部评分 :param uid: 用户id :param item_ids: 要预测的物品id列表 :param ratings_matrix: 用户-物品打分矩阵 :param user_similar: 用户两两间的相似度 :return: 生成器，逐个返回预测评分 ''' # 逐个预测 for iid in item_ids: try: rating = predict(uid, iid, ratings_matrix, user_similar) except Exception as e: print(e) else: yield uid, iid, rating def predict_all(uid, ratings_matrix, user_similar, filter_rule=None): ''' 预测全部评分，并可根据条件进行前置过滤 :param uid: 用户ID :param ratings_matrix: 用户-物品打分矩阵 :param user_similar: 用户两两间的相似度 :param filter_rule: 过滤规则，只能是四选一，否则将抛异常："unhot","rated",["unhot","rated"],None :return: 生成器，逐个返回预测评分 ''' if not filter_rule: item_ids = ratings_matrix.columns elif isinstance(filter_rule, str) and filter_rule == "unhot": '''过滤非热门电影''' # 统计每部电影的评分数 count = ratings_matrix.count() # 过滤出评分数高于10的电影，作为热门电影 item_ids = count.where(count>10).dropna().index elif isinstance(filter_rule, str) and filter_rule == "rated": '''过滤用户评分过的电影''' # 获取用户对所有电影的评分记录 user_ratings = ratings_matrix.ix[uid] # 评分范围是1-5，小于6的都是评分过的，除此以外的都是没有评分的 _ = user_ratings<6 item_ids = _.where(_==False).dropna().index elif isinstance(filter_rule, list) and set(filter_rule) == set(["unhot", "rated"]): '''过滤非热门和用户已经评分过的电影''' count = ratings_matrix.count() ids1 = count.where(count > 10).dropna().index user_ratings = ratings_matrix.ix[uid] _ = user_ratings < 6 ids2 = _.where(_ == False).dropna().index # 取二者交集 item_ids = set(ids1)&set(ids2) else: raise Exception("无效的过滤参数") yield from _predict_all(uid, item_ids, ratings_matrix, user_similar) if __name__ == '__main__': ratings_matrix = load_data(DATA_PATH) user_similar = compute_pearson_similarity(ratings_matrix, based="user") for result in predict_all(1, ratings_matrix, user_similar, filter_rule=["unhot", "rated"]): print(result) -
根据预测评分为指定用户进行TOP-N推荐：
# ...... def top_k_rs_result(k): ratings_matrix = load_data(DATA_PATH) user_similar = compute_pearson_similarity(ratings_matrix, based="user") results = predict_all(1, ratings_matrix, user_similar, filter_rule=["unhot", "rated"]) return sorted(results, key=lambda x: x[2], reverse=True)[:k] if __name__ == '__main__': from pprint import pprint result = top_k_rs_result(20) pprint(result)
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