信息流推荐算法高级研究员简历模板

简历模板特点适合投递信息流推荐算法高级研究员职位，有专业信息流推荐算法高级研究员工作经历描述供参考，帮您制作专业信息流推荐算法高级研究员简历

.resume_main[data_color] .skill_item .skill_slider span::before{background-color:${color};} .resume_main[data_color] .skill_slider s i{background-color:${relative_skill_color};} .resume_main[data_color] .skill_style_01.skill_item .skill_slider s {border-color:${relative_skill_color};} .resume_main[data_color] .skill_style_01.skill_item .skill_slider s i{background-color:${relative_skill_color};} .resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="average"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="average"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 #ccc, 72px 0 0 #ccc, 96px 0 0 #ccc, 120px 0 0 #ccc;} .resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="good"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="good"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 #ccc, 96px 0 0 #ccc, 120px 0 0 #ccc;} .resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="advanced"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="advanced"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 ${relative_skill_color}, 96px 0 0 #ccc, 120px 0 0 #ccc;} .resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="expert"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="expert"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 ${relative_skill_color}, 96px 0 0 ${relative_skill_color}, 120px 0 0 #ccc;} .resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="average"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 #ccc,63px 0 0 #ccc,72px 0 0 #ccc,81px 0 0 #ccc;} .resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="good"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 #ccc,72px 0 0 #ccc,81px 0 0 #ccc;} .resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="advanced"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 ${relative_skill_color},72px 0 0 #ccc,81px 0 0 #ccc;} .resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="expert"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 ${relative_skill_color},72px 0 0 ${relative_skill_color},81px 0 0 #ccc;} .resume_main[data_color] .hobby_item .hobby_item_con .hobby_item_list a.alifont{border-color:${relative_hobby_color};color:${relative_hobby_color}; } /* �� */ .resume_main[data_color] .resume_cover .cover_html svg [data-svg="fill"] {fill:${color};} .resume_main[data_color] .resume_cover .cover_html svg [data-svg="stroke"] {stroke:${color};} .resume_main[data_color] .resume_letter .letter_html svg [data-svg="fill"] {fill:${color};} .resume_main[data_color] .resume_letter .letter_html svg [data-svg="stroke"] {stroke:${color};} .resume_main[data_color] .resume_letter .letter_html svg [data-fill="fill"] {fill:${color};} .resume_main[data_color] .resume_cover[data-type="07"] .resume_cover_avatar{border-color: ${color};} .resume_main[data_color] .resume_cover[data-type="07"] .resume_cover_content{background:${color}} .resume_main[data_color] .resume_cover[data-type="07"] .cover_item_list a.alifont{color: ${color};} .resume_main[data_color] .resume_cover[data-type="08"] .resume_cover_content::after{background:${color}} .resume_main[data_color] .resume_cover[data-type="09"] .resume_cover_content{background:${color}} .resume_main[data_color] .resume_cover[data-type="09"] .cover_item_list a.alifont{color: ${color};} .resume_main[data_color] .resume_cover[data-type="10"]{background-color:${color}} .resume_main[data_color] .resume_cover[data-type="11"] .resume_cover_content{background-color:${color}} .resume_main[data_color] .resume_cover[data-type="14"]{background-color:${color}} .resume_main[data_color] .resume_cover[data-type="15"]{background-color:${color}} .resume_main[data_color] .resume_cover[data-type="19"] .resume_cover_word::before{background-color:${color}} .resume_main[data_color] .resume_cover[data-type="20"]{background-color:${color}} .resume_main[data_color] .resume_letter[data-type="06"]{background-color:${color}} .resume_main[data_color] .resume_letter[data-type="12"]{background-color:${color}}

 头像: 隐藏

编辑

 基本信息: 

26岁









广东深圳



13800138000

BD@100chui.com

 姓名: 锤子简历

梦想每个人都有，但不是每个人都有勇气去坚信，我有!

 求职意向: 
信息流推荐算法高级研究员


广东深圳


8K-10K


随时到岗

 教育背景: 2012.09-2016.06

锤子简历大学

软件工程（本科）

 工作经验

2018.01-2019.11

锤子简历公司

信息流推荐算法高级研究员

负责优化和挖掘新的特征，提升线上推荐效果；
负责优化现有排序算法，提升线上点击率和用户体验；
负责探索深度学习，强化学习在个性化推荐中的应用；
负责大数据量的用户行为处理、清洗和挖掘，并持续迭代优化推荐策略；
从数据中发现现有系统和算法的不足，提出改进的算法并推动实现；
负责追踪业内前沿技术，结合业务特点，探索将前沿的算法技术应用于实际业务；

2016.06-2017.12

锤子简历公司

信息流推荐算法高级研究员

负责信息流推荐中召回、排序、多样性策略的开发和优化；负责用户画像的精准化；
通过算法持续性优化云音乐信息流的生态结构；
深入理解业务，和产品人员一起挖掘潜在的改进点，持续提升信息流的用户体验；
负责信息流广告的算法优化，包括广告推荐算法、广告排序、广告机制设计、大规模机器学习平台优化；
负责广告文本处理，意图识别，文本分类，图文相关性等自然语言处理任务；

 其他

技能： 熟悉Linux 操作，熟练使用 shell 脚本处理问题；熟悉 Python, Scala, C/C++、Java 语言；熟悉 MapReduce, Hbase, Hive, Storm, Spark, ZooKeeper 等Hadoop 大数据处理和分布式开发；熟练使用 MapReduce 进行数据统计，数据过滤，同类汇聚和全局排序；熟练使用 TFIDF 算法提取关键词和运用LCS算法计算文本相似度；熟悉Mysql, Hbase, MongoDB, 会爬虫爬取网站，app，微信公众号数据；熟悉推荐算法和相关技术，常用机器学习模型和 KNN, SVM, LR, Kmeans算法；熟悉Flume, Kafka, Spark sql, Spark streaming 等相关组件；

语言能力： CET-4，掌握基本的听说读写能力；

 项目经历

2017.01-2017.03

算法产品（标签库、策略系统）优化

负责信息流推荐算法中标签系统迭代优化，并且对线上下发推荐端进行评测、分析产出评测分析结果，与优化需求，并且对接技术同学进行优化迭代，且进行日常运营维护；
负责对标签库进行结构化优化，追踪新增热点进行标签提取，协助产品专家对信息流进行策略生产、管控，参与 PRD 编写对竞品的算法、Ctr、PV、Uv 进行测评分析产出报告；
负责对分类系统、标签系统抓取准确度进行评测，产出报告及优化需求，对国内外敏感节日进行预警，及时调整机器抓取下发策略保证线上安全；