用数据方法进行简单商品推荐
背景介紹
當(dāng)顧客在購(gòu)買一件商品時(shí)，商家可以趁機(jī)了解他們還想買什么，以便把多數(shù)顧客愿意同時(shí)購(gòu)買的商品放到一起銷售以提升銷售額。當(dāng)商家收集到足夠多的數(shù)據(jù)時(shí)，就可以對(duì)其進(jìn)行親和性分析，以確定哪些商品適合放在一起出售。
什么是親和性呢，簡(jiǎn)單的說(shuō)就是物品之間的相似性或者說(shuō)是相關(guān)性。比如說(shuō)，一個(gè)去商場(chǎng)購(gòu)物，買了蘋果的同時(shí)也買了香蕉，如果又買蘋果又買香蕉的人比較多，那么我們把蘋果和香蕉擺放在一起來(lái)銷售，往往可以提高銷量。這背后的思想就是人們經(jīng)常購(gòu)買同一件商品，下次大概率還是會(huì)繼續(xù)購(gòu)買。看似簡(jiǎn)單的思想，的確是很多線上和線下商品推薦服務(wù)的基礎(chǔ)。
之前的商品推薦工作，常常是人工在線下來(lái)完成的，費(fèi)時(shí)費(fèi)力，也沒有很好地精準(zhǔn)度。現(xiàn)在我們可以用數(shù)據(jù)驅(qū)動(dòng)的方式來(lái)自動(dòng)完成。節(jié)約成本，也提高了效率，下面我們來(lái)看看如何來(lái)做。
數(shù)據(jù)準(zhǔn)備和介紹
import numpy as np dataset_filename = "affinity_dataset.txt" X = np.loadtxt(dataset_filename) n_samples, n_features = X.shape print("This dataset has {0} samples and {1} features".format(n_samples, n_features))結(jié)果是：This dataset has 100 samples and 5 features
我們來(lái)解釋下這個(gè)數(shù)據(jù)，看看顧客在前五次交易中都買了什么
print(X[:5]) [[ 0. 0. 1. 1. 1.][ 1. 1. 0. 1. 0.][ 1. 0. 1. 1. 0.][ 0. 0. 1. 1. 1.][ 0. 1. 0. 0. 1.]]豎著看，每一列分別表示一種商品的購(gòu)買情況。分別是面包、牛奶、奶酪、蘋果和香蕉。舉個(gè)例子，第一行表示一個(gè)顧客，買了奶酪、蘋果和香蕉。而沒有買別的商品。每一行表示的是一次顧客購(gòu)買行為。
數(shù)據(jù)處理
我們把數(shù)據(jù)特征打上標(biāo)簽，方便后面做處理：
# The names of the features, for your reference. features = ["bread", "milk", "cheese", "apples", "bananas"]我們下面來(lái)做一個(gè)顧客既買蘋果又買香蕉的支持度和置信度，這里支持度指的是，對(duì)于總體而言，有多少樣本符合這個(gè)規(guī)則。置信度是：支持度/總體，比如說(shuō)對(duì)于這個(gè)規(guī)則而言總是是買蘋果也買香蕉+買蘋果不買香蕉的總?cè)藬?shù)的和。即，只要他買蘋果，就算做是總體中的一員。
# How many of the cases that a person bought Apples involved the people purchasing Bananas too? # Record both cases where the rule is valid and is invalid. rule_valid = 0 rule_invalid = 0 for sample in X:if sample[3] == 1: # This person bought Applesif sample[4] == 1:# This person bought both Apples and Bananasrule_valid += 1else:# This person bought Apples, but not Bananasrule_invalid += 1 print("{0} cases of the rule being valid were discovered".format(rule_valid)) print("{0} cases of the rule being invalid were discovered".format(rule_invalid))輸出結(jié)果是
21 cases of the rule being valid were discovered 15 cases of the rule being invalid were discovered根據(jù)排列組合的知識(shí)，我們知道如果5種商品兩兩隨機(jī)組合的話，一共有10種組合方式（C25C52），我們計(jì)算所有組合的置信度，并把排名前三的打印出來(lái)：
import numpy as np dataset_filename = "affinity_dataset.txt" X = np.loadtxt(dataset_filename) n_samples, n_features = X.shape print("This dataset has {0} samples and {1} features".format(n_samples, n_features))# The names of the features, for your reference. features = ["bread", "milk", "cheese", "apples", "bananas"]from collections import defaultdict # Now compute for all possible rules valid_rules = defaultdict(int) invalid_rules = defaultdict(int) num_occurences = defaultdict(int) #num_occurances represents the same number of rulesfor sample in X: # (sample means record of buying fruit)for premise in range(n_features):if sample[premise] == 0: continue# Record that the premise was bought in another transactionnum_occurences[premise] += 1for conclusion in range(n_features):'''根據(jù)排列組合的規(guī)則，我這里希望按照1,2,3,4； 2，3,4； 3,4；4這樣的順序進(jìn)行比較。這樣的話，比較10次，就遍歷完所有的情況。基于此，有了最外層的if...else語(yǔ)句第一句話是為了讓他按照我前面說(shuō)的那個(gè)順序走，后面的判斷語(yǔ)句，保證不遍歷超出范圍'''conclusion = conclusion + premise if conclusion < n_features:if premise == conclusion: # It makes little sense to measure if X -> X.continueif sample[conclusion] == 1:# This person also bought the conclusion itemvalid_rules[(premise, conclusion)] += 1else:# This person bought the premise, but not the conclusioninvalid_rules[(premise, conclusion)] += 1else:continuesupport = valid_rules confidence = defaultdict(float) for premise, conclusion in valid_rules.keys():confidence[(premise, conclusion)] = valid_rules[(premise, conclusion)] / num_occurences[premise]最后我們來(lái)進(jìn)行排序操作，打印前三個(gè)結(jié)果。先來(lái)看一下我們處理之后的結(jié)果都是什么樣子的
# 用于打印 Python 數(shù)據(jù)結(jié)構(gòu). 當(dāng)你在命令行下打印特定數(shù)據(jù)結(jié)構(gòu)時(shí)你會(huì)發(fā)現(xiàn)它很有用(輸出格式比較整齊, 便于閱讀). from pprint import pprint pprint(list(support.items())) [((0, 1), 14),((1, 2), 7),((3, 2), 25),((1, 3), 9),((0, 2), 4),((3, 0), 5),((4, 1), 19),((3, 1), 9),((1, 4), 19),((2, 4), 27),((2, 0), 4),((2, 3), 25),((2, 1), 7),((4, 3), 21),((0, 4), 17),((4, 2), 27),((1, 0), 14),((3, 4), 21),((0, 3), 5),((4, 0), 17)]我們給輸出定義一個(gè)函數(shù)形式，方面后面進(jìn)行輸出：
因?yàn)槲覀冎皩懥艘粋€(gè)feature列表，這樣的話就很容易鎖定到具體產(chǎn)品信息，只用一個(gè)列表就可以搞定，不用定義字典（這是一個(gè)不錯(cuò)的思路）
示例輸出：
premise = 1 conclusion = 3 print_rule(premise, conclusion, support, confidence, features)Rule: If a person buys milk they will also buy apples
- Confidence: 0.196
- Support: 9
然后進(jìn)行排序操作，我們按照置信度大小進(jìn)行排序，降序：
# sort and print the first three resultfrom operator import itemgetter sorted_confidence = sorted(confidence.items(), key=itemgetter(1), reverse=True) for index in range(3):print("Rule #{0}".format(index + 1))(premise, conclusion) = sorted_confidence[index][0]print_rule(premise, conclusion, support, confidence, features)結(jié)果如下：
Rule #1
Rule: If a person buys cheese they will also buy bananas
- Confidence: 0.659
- Support: 27
Rule #2
Rule: If a person buys bread they will also buy bananas
- Confidence: 0.630
- Support: 17
Rule #3
Rule: If a person buys cheese they will also buy apples
- Confidence: 0.610
- Support: 25
從排序結(jié)果來(lái)看，“顧客買蘋果，也會(huì)買奶酪”和“顧客買奶酪，也會(huì)買香蕉”，這兩條規(guī) 則的支持度和置信度都很高。超市經(jīng)理可以根據(jù)這些規(guī)則來(lái)調(diào)整商品擺放位置。例如，如果本周蘋果促銷，就在旁邊擺上奶酪。或許可以提高超市銷量哦。
參考資料：
《python數(shù)據(jù)挖掘入門與實(shí)踐》
數(shù)據(jù)集
總結(jié)
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