一.实现思路

  1. 筹备测试数据
  2. 计算物品类似度,应用jaccard 计算类似度
  3. 获取每个物品对应的类似物品
  4. 获取最初的举荐数据

二.代码实现

1.筹备测试数据

users = ['User1', 'User2', 'User3', 'User4', 'User5', ]items = ['ItemA', 'ItemB', 'ItemC', 'ItemD', 'ItemE']datasets = [    [1, 0, 1, 1, 0],    [1, 0, 0, 1, 1],    [1, 0, 1, 0, 0],    [0, 1, 0, 1, 1],    [1, 1, 1, 0, 1],]df = pd.DataFrame(datasets, columns=items, index=users)

2.计算物品类似度

items_similar = 1 - pairwise_distances(df.values.T, metric='jaccard')items_similar = pd.DataFrame(items_similar, columns=items, index=items)

3. 获取每个物品对应的类似物品

topN_items = {}for i in items_similar.index:    _df = items_similar.loc[i].drop([i])    _df_sorted = _df.sort_values(ascending=False)    topN_items[i] = list(_df_sorted[:2].index)print(topN_items)

运行后果

{'ItemA': ['ItemC', 'ItemE'], 'ItemB': ['ItemE', 'ItemD'], 'ItemC': ['ItemA', 'ItemB'], 'ItemD': ['ItemE', 'ItemA'], 'ItemE': ['ItemB', 'ItemD']}

4.获取最初的举荐数据

rs_results = {}for user in df.index:    user_item = df.loc[user].replace(0, np.nan).dropna().index    rs_result = set()    for item in user_item:        rs_result=rs_result.union(topN_items[item])    rs_result -= set(df.loc[user].replace(0, np.nan).dropna().index)    rs_results[user] = rs_resultprint(rs_results)

举荐后果

{'User1': {'ItemB', 'ItemE'},  'User2': {'ItemB', 'ItemC'},  'User3': {'ItemB', 'ItemE'},  'User4': {'ItemA'},  'User5': {'ItemD'}}

残缺代码

import pandas as pdfrom sklearn.metrics import jaccard_scorefrom sklearn.metrics.pairwise import pairwise_distancesimport numpy as npusers = ['User1', 'User2', 'User3', 'User4', 'User5', ]items = ['ItemA', 'ItemB', 'ItemC', 'ItemD', 'ItemE']datasets = [    [1, 0, 1, 1, 0],    [1, 0, 0, 1, 1],    [1, 0, 1, 0, 0],    [0, 1, 0, 1, 1],    [1, 1, 1, 0, 1],]df = pd.DataFrame(datasets, columns=items, index=users)# print(df)items_similar = 1 - pairwise_distances(df.values.T, metric='jaccard')items_similar = pd.DataFrame(items_similar, columns=items, index=items)topN_items = {}for i in items_similar.index:    _df = items_similar.loc[i].drop([i])    _df_sorted = _df.sort_values(ascending=False)    topN_items[i] = list(_df_sorted[:2].index)print(topN_items)rs_results = {}for user in df.index:    user_item = df.loc[user].replace(0, np.nan).dropna().index    rs_result = set()    for item in user_item:        rs_result=rs_result.union(topN_items[item])    rs_result -= set(df.loc[user].replace(0, np.nan).dropna().index)    rs_results[user] = rs_resultprint(rs_results){'User1': {'ItemB', 'ItemE'}, 'User2': {'ItemB', 'ItemC'}, 'User3': {'ItemB', 'ItemE'}, 'User4': {'ItemA'}, 'User5': {'ItemD'}}