推荐系统框架详解
1. 数据准备
本项目的数据是采集电商网站的用户行为数据,主要包含用户的4种行为:搜索、点击、下单和支付。
电商数据保存在log中,我们一般处理之后存入MySQL数据库中
使用spark 查看数据
2.召回与评估
import random
import numpy as np
import pandas as pd
from tqdm import tqdm
import collections
from collections import defaultdict
import string
import re
import gensim
from collections import Counter
import pickle
from nltk. corpus import stopwords
from sklearn. feature_extraction. text import TfidfVectorizer
from sklearn. decomposition import TruncatedSVD
from sklearn. preprocessing import StandardScaler
from sklearn. model_selection import train_test_split
from sklearn. metrics import roc_auc_score
from sklearn. model_selection import KFold
from keras. preprocessing import text, sequence
import warnings
warnings. filterwarnings( 'ignore' )
import lightgbm as lgb
from functools import partial
import math
import os
import gc
from scipy. sparse import vstack
import time
import datetime
import joblib
import multiprocessing as mp
import seaborn as sns
% matplotlib inline
Using TensorFlow backend.
读取数据
view_data = pd. read_csv( './data/nf_recommend_view_data_15d.csv' , sep= '\t' )
cart_data = pd. read_csv( './data/nf_recommend_cart_data_15d.csv' , sep= '\t' )
order_data = pd. read_csv( './data/nf_recommend_order_data_15d.csv' , sep= '\t' )
sku_info = pd. read_csv( './data/nf_recommend_sku_info.csv' , sep= '\t' )
user_info = pd. read_csv( './data/nf_recommend_user_info.csv' , sep= '\t' )
view_data. columns = [ 'user_log_acct' , 'item_sku_id' , 'request_tm' , 'stm_rt' ]
view_data = view_data[ [ 'user_log_acct' , 'item_sku_id' , 'request_tm' ] ]
view_data[ 'action_type' ] = 1
cart_data. columns = [ 'user_log_acct' , 'item_sku_id' , 'request_tm' ]
cart_data[ 'action_type' ] = 2
order_data = order_data[ [ 'user_log_acct' , 'item_sku_id' , 'ord_complete_tm' ] ]
order_data. columns = [ 'user_log_acct' , 'item_sku_id' , 'request_tm' ]
order_data[ 'action_type' ] = 3
数据集拼接
action_data = pd. concat( [ view_data, cart_data, order_data] , axis= 0 , ignore_index= True )
action_data = action_data[ ~ action_data[ 'user_log_acct' ] . isnull( ) ]
action_data = action_data. sort_values( [ 'user_log_acct' , 'request_tm' ] ) . reset_index( drop= True )
action_data[ 'date' ] = action_data[ 'request_tm' ] . apply ( lambda x: str ( x) [ : 10 ] )
数据预处理
sku_info = sku_info[ [ 'item_sku_id' , 'brand_code' , 'item_first_cate_cd' , 'item_second_cate_cd' , 'item_third_cate_cd' , 'my_prc' ] ]
for f in [ 'ulp_addr_province' , 'ulp_addr_city' , 'ulp_addr_county' ] :
user_info[ f] = user_info[ f] . fillna( - 999 )
user_info[ f] = user_info[ f] . map ( dict ( zip ( user_info[ f] . unique( ) , range ( 0 , user_info[ f] . nunique( ) ) ) ) )
action_data[ 'dd_len' ] = action_data[ 'request_tm' ] . apply ( lambda x: len ( str ( x) ) )
action_data = action_data[ action_data[ 'dd_len' ] != 3 ]
action_data[ 'request_tm' ] = action_data[ 'request_tm' ] . apply ( lambda x: x[ : 19 ] )
del action_data[ 'dd_len' ]
action_data[ 'len' ] = action_data[ 'item_sku_id' ] . apply ( lambda x: len ( str ( x) ) )
action_data = action_data[ action_data[ 'len' ] >= 6 ]
action_data = action_data[ action_data[ 'len' ] <= 16 ]
action_data = action_data[ action_data[ 'len' ] != 9 ]
action_data = action_data[ action_data[ 'len' ] != 15 ]
del action_data[ 'len' ]
action_data = action_data[ ~ action_data[ 'item_sku_id' ] . isnull( ) ]
sku_info = sku_info[ ~ sku_info[ 'item_sku_id' ] . isnull( ) ]
action_data. head( )
user_log_acct item_sku_id request_tm action_type date 0 *yx_196688565 100016727330.0 2020-11-24 18:57:45 1 2020-11-24 4 *yx_196688565 57112907461.0 2020-11-25 22:35:20 1 2020-11-25 5 *yx_196688565 57112907461.0 2020-11-25 22:35:42 1 2020-11-25 6 *yx_196688565 57112907461.0 2020-11-25 22:35:54 2 2020-11-25 7 *yx_196688565 57112907461.0 2020-11-25 22:36:12 1 2020-11-25
dic = dict ( zip ( action_data[ 'item_sku_id' ] . unique( ) , range ( 0 , action_data[ 'item_sku_id' ] . nunique( ) ) ) )
action_data[ 'item_sku_id' ] = action_data[ 'item_sku_id' ] . map ( dic)
sku_info[ 'item_sku_id' ] = sku_info[ 'item_sku_id' ] . astype( str )
sku_info[ 'item_sku_id' ] = sku_info[ 'item_sku_id' ] . map ( dic)
sku_info = sku_info[ ~ sku_info[ 'item_sku_id' ] . isnull( ) ]
action_data. head( )
user_log_acct item_sku_id request_tm action_type date 0 *yx_196688565 0 2020-11-24 18:57:45 1 2020-11-24 4 *yx_196688565 1 2020-11-25 22:35:20 1 2020-11-25 5 *yx_196688565 1 2020-11-25 22:35:42 1 2020-11-25 6 *yx_196688565 1 2020-11-25 22:35:54 2 2020-11-25 7 *yx_196688565 1 2020-11-25 22:36:12 1 2020-11-25
训练集切分
action_data[ 'request_tm' ] = action_data[ 'request_tm' ] . apply ( lambda x: datetime. datetime. strptime( x, "%Y-%m-%d %H:%M:%S" ) )
action_data = action_data[ action_data[ 'request_tm' ] < pd. to_datetime( '20201210' ) ]
train_data = action_data[ action_data[ 'request_tm' ] < pd. to_datetime( '20201209' ) ]
test_data = action_data[ action_data[ 'request_tm' ] >= pd. to_datetime( '20201209' ) ]
test_data = test_data. drop_duplicates( [ 'user_log_acct' , 'item_sku_id' ] , keep= 'first' )
test_data = test_data[ test_data[ 'action_type' ] == 1 ]
test_data = test_data. groupby( 'user_log_acct' ) [ 'item_sku_id' ] . agg( { list } ) . reset_index( )
test_data. columns = [ 'user_log_acct' , 'action_list' ]
train_data[ 'timestamp' ] = train_data[ 'request_tm' ] . apply ( lambda x: ( x- np. datetime64( '1970-01-01T00:00:00Z' ) ) / np. timedelta64( 1 , 's' ) )
test_data. to_csv( 'label_data.csv' , index= False )
train_data. to_csv( 'train_data.csv' , index= False )
user_info. to_csv( 'user_info.csv' , index= False )
sku_info. to_csv( 'sku_info.csv' , index= False )
基于历史交互
% % time
beh_weight = { 1 : 1 , 2 : 1.2 , 3 : 0.8 }
train_data[ 'item_score' ] = train_data[ 'action_type' ] . map ( beh_weight)
last_timestamp = train_data[ 'timestamp' ] . max ( )
train_data[ 'date_diff' ] = last_timestamp - train_data[ 'timestamp' ] . values
use_time_decay = True
if use_time_decay:
train_data[ 'item_score' ] = train_data[ 'item_score' ] * np. log( train_data[ 'date_diff' ] + 1 )
user_item = train_data. groupby( [ 'user_log_acct' , 'item_sku_id' ] ) [ 'item_score' ] . sum ( ) . reset_index( )
user_item = user_item. sort_values( [ 'user_log_acct' , 'item_score' ] )
user_item = user_item. groupby( [ 'user_log_acct' ] ) [ 'item_sku_id' ] . agg( lambda x: list ( x) [ - 20 : ] ) . reset_index( )
user_item. columns = [ 'user_log_acct' , 'top_items' ]
CPU times: user 1.65 s, sys: 56 ms, total: 1.7 s
Wall time: 1.7 s
train_data = train_data. merge( user_info[ [ 'user_log_acct' , 'ulp_base_sex' , 'ulp_base_age' ] ] , on= 'user_log_acct' , how= 'left' )
test_data = test_data. merge( user_info[ [ 'user_log_acct' , 'ulp_base_sex' , 'ulp_base_age' ] ] , on= 'user_log_acct' , how= 'left' )
train_data[ 'user_label' ] = train_data[ 'ulp_base_sex' ] . fillna( 1 ) . astype( int ) . astype( str ) . values + \
train_data[ 'ulp_base_age' ] . fillna( 3 ) . astype( int ) . astype( str ) . values
test_data[ 'user_label' ] = test_data[ 'ulp_base_sex' ] . fillna( 1 ) . astype( int ) . astype( str ) . values + \
test_data[ 'ulp_base_age' ] . fillna( 3 ) . astype( int ) . astype( str ) . values
test_data. head( )
user_log_acct action_list ulp_base_sex ulp_base_age user_label 0 *yx_196688565 [18] 1 4 14 1 ---叶叶叶叶叶叶灵 [98, 99, 100, 101] 0 3 03 2 -huhongyang- [154, 166, 167] 1 3 13 3 -小静宝- [446, 447, 448, 174, 449, 450, 451, 452, 453, ... 0 3 03 4 0 [1259, 1260, 1261, 1262, 1263, 1264, 1265, 126... 0 4 04
test_data = test_data. merge( user_item, on= 'user_log_acct' , how= 'left' )
test_data[ 'items_len' ] = None
test_data[ 'top_items' ] = test_data[ 'top_items' ] . fillna( 0 )
test_data. loc[ test_data. top_items== 0 , 'items_len' ] = 0
test_data. loc[ test_data. items_len!= 0 , 'items_len' ] = test_data. loc[ test_data. items_len!= 0 , 'top_items' ] . apply ( lambda x: len ( x) )
user_item. head( )
user_log_acct top_items 0 *yx_196688565 [17, 16, 15, 14, 12, 10, 6, 5, 4, 0, 13, 11, 9... 1 ---叶叶叶叶叶叶灵 [95, 94, 72, 66, 61, 69, 71, 60, 51, 39, 27, 2... 2 -huhongyang- [119, 133, 132, 117, 114, 164, 149, 160, 127, ... 3 -小静宝- [428, 335, 395, 435, 185, 174, 230, 253, 188, ... 4 -相信- [474, 473, 472, 466, 465, 464, 463, 462, 461, ...
test_data. head( )
user_log_acct action_list ulp_base_sex ulp_base_age user_label top_items items_len 0 *yx_196688565 [18] 1 4 14 [17, 16, 15, 14, 12, 10, 6, 5, 4, 0, 13, 11, 9... 18 1 ---叶叶叶叶叶叶灵 [98, 99, 100, 101] 0 3 03 [95, 94, 72, 66, 61, 69, 71, 60, 51, 39, 27, 2... 20 2 -huhongyang- [154, 166, 167] 1 3 13 [119, 133, 132, 117, 114, 164, 149, 160, 127, ... 20 3 -小静宝- [446, 447, 448, 174, 449, 450, 451, 452, 453, ... 0 3 03 [428, 335, 395, 435, 185, 174, 230, 253, 188, ... 20 4 0 [1259, 1260, 1261, 1262, 1263, 1264, 1265, 126... 0 4 04 [813, 483, 826, 812, 1104, 1037, 1003, 898, 93... 20
test_data. tail( )
user_log_acct action_list ulp_base_sex ulp_base_age user_label top_items items_len 14752 默楠默北 [75913, 644238, 930826] 0 3 03 [930828, 661591, 930821, 930818, 337984, 93081... 20 14753 默默地魏宁宁 [721221, 930905] 0 3 03 [682142, 25812, 24082, 930902, 60444, 930896, ... 20 14754 黯月飞雪 [7] 1 2 12 [388958, 369332, 12047, 930909, 930907, 143747... 15 14755 齐乐 [10241, 930920, 930926, 930927, 930928, 930929... 0 3 03 [930918, 930917, 930916, 200242, 200262, 93091... 20 14756 龍鑫燚 [931130, 931132, 931133, 931134, 931135, 93113... 1 3 13 [931060, 931009, 243199, 931093, 931055, 93098... 20
test_data[ test_data[ 'user_log_acct' ] == '*yx_196688565' ]
user_log_acct action_list ulp_base_sex ulp_base_age user_label top_items items_len 0 *yx_196688565 [18] 1 4 14 [17, 16, 15, 14, 12, 10, 6, 5, 4, 0, 13, 11, 9... 18
topN = 50
label_types = test_data[ 'user_label' ] . unique( )
for lbl in tqdm( label_types) :
hot_items = train_data. loc[ train_data[ 'user_label' ] == lbl] . groupby( 'item_sku_id' , as_index= False ) [ 'timestamp' ] . count( ) . sort_values( 'timestamp' , ascending= False ) [ : topN] [ 'item_sku_id' ] . tolist( )
test_data. loc[ ( test_data[ 'user_label' ] == lbl) & ( test_data[ 'items_len' ] < topN) , 'top_items' ] = test_data. loc[ ( test_data[ 'user_label' ] == lbl) & ( test_data[ 'items_len' ] < topN) , 'top_items' ] . map ( \
lambda x: ',' . join( [ str ( int ( i) ) for i in ( x + hot_items) [ : topN] ] ) if isinstance ( x, list ) else ',' . join( [ str ( int ( i) ) for i in ( hot_items) [ : topN] ] ) )
100%|██████████| 10/10 [00:01<00:00, 5.55it/s]
基于历史交互 召回率
def recall20 ( items) :
score = len ( list ( set ( items[ 0 ] ) . intersection( set ( items[ 1 ] ) ) ) ) / len ( items[ 0 ] )
return score
test_data[ 'top_items' ] = test_data[ 'top_items' ] . apply ( lambda x: [ int ( s) for s in x. split( ',' ) ] )
test_data[ 'score' ] = test_data[ [ 'action_list' , 'top_items' ] ] . apply ( recall20, axis= 1 )
test_data[ 'score' ] . mean( )
0.1602899645782721
基于关联商品
def get_user_item_time ( click_df) :
click_df = click_df. sort_values( 'timestamp' )
def make_item_time_pair ( df) :
return list ( zip ( df[ 'item_sku_id' ] , df[ 'timestamp' ] ) )
user_item_time_df = click_df. groupby( 'user_log_acct' ) [ 'item_sku_id' , 'timestamp' ] . apply ( lambda x: make_item_time_pair( x) ) \
. reset_index( ) . rename( columns= { 0 : 'item_time_list' } )
user_item_time_dict = dict ( zip ( user_item_time_df[ 'user_log_acct' ] , user_item_time_df[ 'item_time_list' ] ) )
return user_item_time_dict
def itemcf_sim ( df) :
"""
商品与商品之间的相似性矩阵计算
:param df: 数据表
:item_created_time_dict: 商品创建时间的字典
return : 商品与商品的相似性矩阵
思路: 基于物品的协同过滤 + 关联规则
"""
user_item_time_dict = get_user_item_time( df)
i2i_sim = { }
item_cnt = defaultdict( int )
for user, item_time_list in tqdm( user_item_time_dict. items( ) ) :
for loc1, ( i, i_click_time) in enumerate ( item_time_list) :
item_cnt[ i] += 1
i2i_sim. setdefault( i, { } )
for loc2, ( j, j_click_time) in enumerate ( item_time_list) :
if ( i == j) :
continue
loc_alpha = 1.0 if loc2 > loc1 else 0.7
loc_weight = loc_alpha * ( 0.9 ** ( np. abs ( loc2 - loc1) - 1 ) )
i2i_sim[ i] . setdefault( j, 0 )
i2i_sim[ i] [ j] += loc_weight / math. log( len ( item_time_list) + 1 )
i2i_sim_ = i2i_sim. copy( )
for i, related_items in i2i_sim. items( ) :
for j, wij in related_items. items( ) :
i2i_sim_[ i] [ j] = wij / math. sqrt( item_cnt[ i] * item_cnt[ j] )
return i2i_sim_
def item_based_recommend ( user_id, user_item_time_dict, i2i_sim, sim_item_topk, recall_item_num, item_topk_click) :
"""
基于商品协同过滤的召回
:param user_id: 用户id
:param user_item_time_dict: 字典, 根据点击时间获取用户的点击商品序列 {user1: [(item1, time1), (item2, time2)..]...}
:param i2i_sim: 字典,商品相似性矩阵
:param sim_item_topk: 整数, 选择与当前文章最相似的前k个商品
:param recall_item_num: 整数, 最后的召回商品数量
:param item_topk_click: 列表,点击次数最多的商品列表,用户召回补全
return: 召回的商品列表 {item1:score1, item2: score2...}
"""
item_rank = { }
try :
user_hist_items = user_item_time_dict[ user_id] [ : 50 ]
user_hist_items_ = { user_id for user_id, _ in user_hist_items}
for loc, ( i, click_time) in enumerate ( user_hist_items) :
for j, wij in sorted ( i2i_sim[ i] . items( ) , key= lambda x: x[ 1 ] , reverse= True ) [ : sim_item_topk] :
if j in user_hist_items_:
continue
item_rank. setdefault( j, 0 )
item_rank[ j] += wij
except :
pass
if len ( item_rank) < recall_item_num:
for i, item in enumerate ( item_topk_click) :
if item in item_rank. items( ) :
continue
item_rank[ item] = - i - 100
if len ( item_rank) == recall_item_num:
break
item_rank = sorted ( item_rank. items( ) , key= lambda x: x[ 1 ] , reverse= True ) [ : recall_item_num]
return item_rank
def get_item_topk_click ( click_df, k) :
topk_click = click_df[ 'item_sku_id' ] . value_counts( ) . index[ : k]
return topk_click
item_topk_click = get_item_topk_click( train_data, k= 20 )
item_topk_click
Int64Index([ 7, 3, 1435, 2392, 1625, 7297, 77276, 148, 1836,
2068, 2086, 19454, 2441, 4486, 1716, 5011, 1604, 2501,
81970, 2058],
dtype='int64')
train_data = train_data. sort_values( 'timestamp' )
i2i_sim = itemcf_sim( train_data)
100%|██████████| 25930/25930 [46:28<00:00, 9.30it/s]
user_recall_items_dict = collections. defaultdict( dict )
user_item_time_dict = get_user_item_time( train_data)
sim_item_topk = 50
recall_item_num = 50
for user in tqdm( test_data[ 'user_log_acct' ] . unique( ) ) :
user_recall_items_dict[ user] = item_based_recommend( user, user_item_time_dict, i2i_sim, sim_item_topk, recall_item_num, item_topk_click)
100%|██████████| 14757/14757 [4:25:28<00:00, 1.08s/it]
user_item_score_list = [ ]
for user, items in tqdm( user_recall_items_dict. items( ) ) :
for item, score in items:
user_item_score_list. append( [ user, item, score] )
recall_df = pd. DataFrame( user_item_score_list, columns= [ 'user_log_acct' , 'item_sku_id' , 'pred_score' ] )
100%|██████████| 14757/14757 [00:01<00:00, 13204.08it/s]
recall_df = recall_df. groupby( [ 'user_log_acct' ] ) [ 'item_sku_id' ] . agg( list ) . reset_index( )
recall_df. columns = [ 'user_log_acct' , 'top_items2' ]
test_data = test_data. merge( recall_df, on= 'user_log_acct' , how= 'left' )
test_data[ 'score' ] = test_data[ [ 'action_list' , 'top_items2' ] ] . apply ( recall20, axis= 1 )
test_data[ 'score' ] . mean( )
0.029094841710824874
Item2vec & Deepwalk
def get_w2v ( sentences, f1, f2, values, emb_size) :
model = Word2Vec( sentences, size= emb_size, sg= 1 , window= 10 , seed= 2021 , workers= 24 , min_count= 1 , iter = 10 )
w2v= [ ]
for v in values:
try :
a = [ int ( v) ]
a. extend( model[ str ( v) ] )
w2v. append( a)
except :
a = [ int ( v) ]
a. extend( [ 0 ] * emb_size)
w2v. append( a)
out_df = [ ]
for li in w2v:
out_df. append( [ li[ 0 ] , li[ 1 : ] ] )
out_df = pd. DataFrame( out_df)
out_df. columns = [ f2] + [ 'vector' ]
return out_df
def item2vec ( df_, f1, f2, emb_size) :
df = df_. copy( )
df = df. sort_values( 'timestamp' , ascending= True )
sentences = df. groupby( [ f1] ) [ f2] . agg( { list } ) . reset_index( ) [ 'list' ] . values. tolist( )
for i in range ( len ( sentences) ) :
sentences[ i] = [ str ( x) for x in sentences[ i] ]
values = df[ f2] . unique( )
out_df = get_w2v( sentences, f1, f2, values, emb_size)
return out_df
def deepwalk_walk ( walk_length, start_node) :
walk = [ start_node]
while len ( walk) <= walk_length:
cur = walk[ - 1 ]
try :
cur_nbrs = item_dict_new[ cur]
walk. append( random. choice( cur_nbrs) )
except :
break
return walk
def _simulate_walks ( nodes, num_walks, walk_length, ) :
walks = [ ]
for _ in tqdm( range ( num_walks) ) :
random. shuffle( nodes)
for v in nodes:
walks. append( deepwalk_walk( walk_length= walk_length, start_node= v) )
return walks
def deepwalk ( df_, f1, f2, emb_size) :
df = df_. copy( )
item_dict_new = { }
all_item = [ ]
sentences = df. groupby( [ f1] ) [ f2] . agg( { list } ) . reset_index( ) [ 'list' ] . values. tolist( )
for i in range ( len ( sentences) ) :
sentences[ i] = [ str ( x) for x in sentences[ i] ]
for sentence in sentences:
length = len ( sentence)
for position, itemId in enumerate ( sentence) :
for i in range ( position- 1 , position+ 2 ) :
if ( i < 0 ) | ( i >= length) | ( i == position) :
continue
try :
item_dict_new[ itemId] . append( sentence[ i] )
except :
item_dict_new[ itemId] = [ sentence[ i] ]
nodes = [ k for k in item_dict_new]
num_walks = 5
walk_length = 20
results = _simulate_walks( nodes, num_walks, walk_length)
len ( results)
values = df[ f2] . unique( )
out_df = get_w2v( results, f1, f2, values, emb_size)
return out_df
train_data[ 'time_diff' ] = train_data. groupby( [ 'user_log_acct' , 'item_sku_id' ] ) [ 'timestamp' ] . diff( )
tmp_data = train_data[ train_data[ 'time_diff' ] > 5 ]
from gensim. models import Word2Vec
emb_df = item2vec( tmp_data, 'user_log_acct' , 'item_sku_id' , 16 )
emb_dic = dict ( zip ( emb_df[ 'item_sku_id' ] . tolist( ) , emb_df[ 'vector' ] . tolist( ) ) )
( emb_dic)
{89740: [1.1308107,
0.15618902,
0.6002973,
-0.76109654,
-1.0868034,
0.5030788,
-0.50718504,
-0.40077063,
-0.59533364,
-1.3262757,
1.0814211,
-0.6232442,
-0.9521757,
-0.09484385,
0.5181297,
-1.6260711],
340914: [0.525045,
-0.14909405,
-0.97197306,
-0.2586312,
-0.506575,
0.9409744,
0.43536264,
-0.0741029,
-0.10424126,
-0.84103554,
0.8669736,
-0.572473,
-1.2950504,
0.5039337,
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