def recommend1(): j=0 csvfile = file('csv_result19.csv', 'wb') writer=csv.writer(csvfile) user_item,user_item_time,ignore_user=csv_to_list() user_latest_time=map_time() item_issue_time=map_item_issue_time() predict=list() print ignore_user print len(ignore_user) for user in user_item: if user not in ignore_user: recos=pearsSim(user,user_item,user_item_time)#求解相似用户 item_fre,local_user_item=create_feature(user,recos,user_item)#编号处理 item_score=interest_distribution(user,local_user_item,user_item_time,item_fre,user_latest_time,item_issue_time,h=0.4)#计算核密度 number=len(user_item[user]) if number>30: number=20 final_result=sorted(item_score, key=lambda jj:jj[1], reverse=True)[:(number/10)+1]#结果排序 print final_result for line in final_result: if line[1]>0.25: writer.writerow((user,line[0])) j=j+1 print j csvfile.close() return predict
def recommend1(): j = 0 csvfile = file('csv_result19.csv', 'wb') writer = csv.writer(csvfile) user_item, user_item_time, ignore_user = csv_to_list() user_latest_time = map_time() item_issue_time = map_item_issue_time() predict = list() print ignore_user print len(ignore_user) for user in user_item: if user not in ignore_user: recos = pearsSim(user, user_item, user_item_time) #求解相似用户 item_fre, local_user_item = create_feature(user, recos, user_item) #编号处理 item_score = interest_distribution(user, local_user_item, user_item_time, item_fre, user_latest_time, item_issue_time, h=0.4) #计算核密度 number = len(user_item[user]) if number > 30: number = 20 final_result = sorted(item_score, key=lambda jj: jj[1], reverse=True)[:(number / 10) + 1] #结果排序 print final_result for line in final_result: if line[1] > 0.25: writer.writerow((user, line[0])) j = j + 1 print j csvfile.close() return predict
day_late[day] = time early = day_early.get(day) if early is not None: if cmp_time(early,time) > 0: day_early[day] = time else: day_early[day] = time return day_news, day_late, day_early if __name__=='__main__': latest_time,news_time=map_time() day_news, day_late, day_early = draw_time(news_time) print 'every day\'s news:' print day_news print print 'every day\'s earliest issue time:' print day_early print print 'every day\'s latest issue time:' print day_late # print day_news.keys() # print day_late.keys() # print day_early.keys()