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措施营造,设计和推动集团云存款和储蓄和云文

日期:2019-10-05编辑作者:必赢网上注册

合法推荐大家利用 Overlay 的措施来营造项目,能够说极度方便。

利用相当粗略,在布局文件中一直动用CorronisonView

1.1 概述

推介系统当下大约无处不在,主流的app都基本采纳到了引入系统。比方,旅游骑行,乐途、去哪个地方等都会给您推荐机票、商旅等等;点外卖,饿了么、美团等会给你推荐酒馆;购物的时候,京东、天猫商城、亚马逊(亚马逊(Amazon))等会给您推荐“恐怕喜欢”的物品;看资源信息,博客园、腾讯情报等都会给你推送你感兴趣的消息....大约具备的app应用或网址都存在推荐系统。

究其根本的原因,推荐系统的盛行是因为要去化解二个题目:货色越多,音信更是多,而人的肥力和时间是有限的,要求三个办法去更有作用地获取音信,链接人与新闻。

推荐系统正是为了消除这一标题而诞生的,在海量的物料和人以内,架起来一条桥梁。它就如三个私人的直属引导购物,依照你的野史行为、个人消息等等,为各种人diy进行推荐介绍,千人前面,帮衬大家越来越好、更加快地挑选自个儿感兴趣的、自身索要的东西。乐乎系的feed流在引入算法的加持下,短短几年的客户增速和采用时间长度数据令人咂舌,受到了市道的追捧和高价值评估。一夜之间,大致具有的app都开首上feed流、上各类推荐,首要性一叶报秋。

query_planner代码片段:

一家初创集团的技艺官员,难免要求照拂古板上应该由IT部门造成的办事。在这一篇中,咱们聊聊IT管理中最大旨的一项职业:建设云存款和储蓄和云文书档案系统,支撑公司初创期全部文本和知识的电子化保存和享受,为文书档案和材料的创办、管理、分享和应用,提供最优的减轻方案。

选好版本后,clone 或许 download 都行。

 <com.skateboard.corronisonview.CorronisonView app:duration="10" android: android:layout_width="match_parent" android:layout_height="match_parent" />

2.3 CF算法示例

为了学习那块的技艺知识,跟着插手了下里面设置的srtc推荐竞赛。重在参预,首假使上学整个核心流程,体会下推荐场景,理解Tencent里面做得好的团组织和成品是怎么体统。

2.3.1(内部敏感资料,删除)

2.3.2 CF算法

在web平台上点一点,大概错失了就学的意义。所以针对学习的姿态,作者在线下自身的机械上落到实处了部分常用的算法,比方CF等。

推荐介绍算法里CF算是相比较普及的,大旨还是很轻巧的。

  • user-cf基本原理

A.找到和对象客商兴趣相似的的顾客聚集; B.找到那么些集合中的客户喜爱的,且对象客商没听过的物料推荐给指标客户。

  • item-cf基本原理

A.总括货色之间的相似度; B.遵照货物的相似度和顾客的野史作为给客商生成推荐列表。

组合前边总括的,cf属于memory-base的算法,相当大学一年级个特征正是会用到相似度的函数。这么些user-cf须求总结顾客兴趣的相似度,item-cf供给计算货品间的相似度。基于相似度函数的挑三拣四、编程语言的挑三拣四、完成格局的选项、优化的不如,结果和全路运营时刻会十分的大分化。那时候就轻巧用python达成的,8个process跑满cpu同期管理,需求近十个小时跑完。前边通晓到有底层实行过优化的pandas、numpy等,基于那个工具来落到实处速度会快相当多。

2.3.3 收获

哈哈哈,第壹回到位这种竞技,就算战绩比较不好,但本身认为格外学到相当多东西,基本完成了参加比赛的目标。在真正的光景和数据下去思虑各样影响因素,体会各个算法从筹算、完毕、磨炼、评价等各阶段,非常多东西确实比看资料和书来得更尖锐。果真施行才是读书的最棒手段。借使想更深切去搞推荐算法这块,认为供给后续求学前段时间各样紧俏算法的法规、潜法规,kaggle上多练手,以及操练相关的平台及工程化能力。

收罗、斟酌了下网络一些推荐系统落地总计的篇章,能够开发视线,加深全体通晓。

以下只是有个别要害内容,风乐趣能够阅读原作:

  • 《博客园算法原理》,原来的文章链接
  • 《推荐算法在闲鱼小商品池的追究与执行》,原版的书文链接
  • 《饿了么推荐系统:从0到1》,原版的书文链接
  • 《爱奇艺天性化推荐排序推行》,最早的小说链接
  • 《乐途本性化推荐算法执行》,原来的文章链接
  • 《寸菇街推荐工程实行》,原作链接

二、源码解读

remove_useless_joins扫除无用的连年,比方以下的SQL语句:

select t1.dwbh from t_grxx t1 left join t_dwxx t2 on t1.dwbh = t2.dwbh;

左连接,而且t_dwxx.dwbh独一,这样的连天是无需的连日,直接查询t_grxx就能够.从施行安顿来看,PG只对t_grxx进行围观:

testdb=# explain verbose select t1.dwbh from t_grxx t1 left join t_dwxx t2 on t1.dwbh = t2.dwbh; QUERY PLAN -------------------------------------------------------------------- Seq Scan on public.t_grxx t1 (cost=0.00..14.00 rows=400 width=38) Output: t1.dwbh

源代码如下:

 /* * remove_useless_joins * Check for relations that don't actually need to be joined at all, * and remove them from the query. * * We are passed the current joinlist and return the updated list. Other * data structures that have to be updated are accessible via "root". */ List * remove_useless_joins(PlannerInfo *root, List *joinlist) { ListCell *lc; /* * We are only interested in relations that are left-joined to, so we can * scan the join_info_list to find them easily. */ restart: foreach(lc, root->join_info_list)//遍历连接信息链表 { SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) lfirst; int innerrelid; int nremoved; /* Skip if not removable */ if (!join_is_removable(root, sjinfo))//判断是否可以清除连接 continue; /* * Currently, join_is_removable can only succeed when the sjinfo's * righthand is a single baserel. Remove that rel from the query and * joinlist. */ innerrelid = bms_singleton_member(sjinfo->min_righthand); remove_rel_from_query(root, innerrelid, bms_union(sjinfo->min_lefthand, sjinfo->min_righthand));//从查询中删除相应的Rel /* We verify that exactly one reference gets removed from joinlist */ nremoved = 0; joinlist = remove_rel_from_joinlist(joinlist, innerrelid, &nremoved); if (nremoved != 1) elog(ERROR, "failed to find relation %d in joinlist", innerrelid); /* * We can delete this SpecialJoinInfo from the list too, since it's no * longer of interest. */ //更新连接链表信息 root->join_info_list = list_delete_ptr(root->join_info_list, sjinfo); /* * Restart the scan. This is necessary to ensure we find all * removable joins independently of ordering of the join_info_list * (note that removal of attr_needed bits may make a join appear * removable that did not before). Also, since we just deleted the * current list cell, we'd have to have some kluge to continue the * list scan anyway. */ goto restart; } return joinlist; }

reduce_unique_semijoins把能够简化的半连接转化为内连接.比方以下的SQL语句:

select t1.*from t_grxx t1 where dwbh IN (select t2.dwbh from t_dwxx t2);

是因为子查询"select t2.dwbh from t_dwxx t2"的dwbh是PK,子查询升高后,t_grxx的dwbh只对应t_dwxx独一的一条记下,因而得以把半连连调换为内延续,试行安排如下:

testdb=# explain verbose select t1.*from t_grxx t1 where dwbh IN (select t2.dwbh from t_dwxx t2); QUERY PLAN ----------------------------------------------------------------------------- Hash Join (cost=1.07..20.10 rows=6 width=176) Output: t1.dwbh, t1.grbh, t1.xm, t1.xb, t1.nl Inner Unique: true Hash Cond: ::text = ::text) -> Seq Scan on public.t_grxx t1 (cost=0.00..14.00 rows=400 width=176) Output: t1.dwbh, t1.grbh, t1.xm, t1.xb, t1.nl -> Hash (cost=1.03..1.03 rows=3 width=38) Output: t2.dwbh -> Seq Scan on public.t_dwxx t2 (cost=0.00..1.03 rows=3 width=38) Output: t2.dwbh

钉住剖析:

 n199 reduce_unique_semijoins; stepreduce_unique_semijoins (root=0x1702968) at analyzejoins.c:520520 for (lc = list_head(root->join_info_list); lc != NULL; lc = next) n522 SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) lfirst 

翻开SpecialJoinInfo内部存款和储蓄器结构:

528 next = lnext p *sjinfo$1 = {type = T_SpecialJoinInfo, min_lefthand = 0x1749818, min_righthand = 0x1749830, syn_lefthand = 0x1749570, syn_righthand = 0x17495d0, jointype = JOIN_SEMI, lhs_strict = true, delay_upper_joins = false, semi_can_btree = true, semi_can_hash = true, semi_operators = 0x17496c8, semi_rhs_exprs = 0x17497b8}

内表(innerrel,即t_dwxx)如援救独一性,则足以思量把半三番五次调换为内连接

550 if (!rel_supports_distinctness(root, innerrel))...575 root->join_info_list = list_delete_ptr(root->join_info_list, sjinfo);...

源代码如下:

 /* * reduce_unique_semijoins * Check for semijoins that can be simplified to plain inner joins * because the inner relation is provably unique for the join clauses. * * Ideally this would happen during reduce_outer_joins, but we don't have * enough information at that point. * * To perform the strength reduction when applicable, we need only delete * the semijoin's SpecialJoinInfo from root->join_info_list. (We don't * bother fixing the join type attributed to it in the query jointree, * since that won't be consulted again.) */ void reduce_unique_semijoins(PlannerInfo *root) { ListCell *lc; ListCell *next; /* * Scan the join_info_list to find semijoins. We can't use foreach * because we may delete the current cell. */ for (lc = list_head(root->join_info_list); lc != NULL; lc = next) { SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) lfirst;//特殊连接信息,先前通过deconstruct函数生成 int innerrelid; RelOptInfo *innerrel; Relids joinrelids; List *restrictlist; next = lnext; /* * Must be a non-delaying semijoin to a single baserel, else we aren't * going to be able to do anything with it. (It's probably not * possible for delay_upper_joins to be set on a semijoin, but we * might as well check.) */ if (sjinfo->jointype != JOIN_SEMI || sjinfo->delay_upper_joins) continue; if (!bms_get_singleton_member(sjinfo->min_righthand, &innerrelid)) continue; innerrel = find_base_rel(root, innerrelid); /* * Before we trouble to run generate_join_implied_equalities, make a * quick check to eliminate cases in which we will surely be unable to * prove uniqueness of the innerrel. */ if (!rel_supports_distinctness(root, innerrel)) continue; /* Compute the relid set for the join we are considering */ joinrelids = bms_union(sjinfo->min_lefthand, sjinfo->min_righthand); /* * Since we're only considering a single-rel RHS, any join clauses it * has must be clauses linking it to the semijoin's min_lefthand. We * can also consider EC-derived join clauses. */ restrictlist = list_concat(generate_join_implied_equalities(root, joinrelids, sjinfo->min_lefthand, innerrel), innerrel->joininfo); /* Test whether the innerrel is unique for those clauses. */ if (!innerrel_is_unique(root, joinrelids, sjinfo->min_lefthand, innerrel, JOIN_SEMI, restrictlist, true)) continue; /* OK, remove the SpecialJoinInfo from the list. */ root->join_info_list = list_delete_ptr(root->join_info_list, sjinfo);//删除特殊连接信息 } }

add_placeholders_to_base_rels把PHV分发到base rels中,代码较为轻松

 /* * add_placeholders_to_base_rels * Add any required PlaceHolderVars to base rels' targetlists. * * If any placeholder can be computed at a base rel and is needed above it, * add it to that rel's targetlist. This might look like it could be merged * with fix_placeholder_input_needed_levels, but it must be separate because * join removal happens in between, and can change the ph_eval_at sets. There * is essentially the same logic in add_placeholders_to_joinrel, but we can't * do that part until joinrels are formed. */ void add_placeholders_to_base_rels(PlannerInfo *root) { ListCell *lc; foreach(lc, root->placeholder_list)//遍历PH链表 { PlaceHolderInfo *phinfo = (PlaceHolderInfo *) lfirst; Relids eval_at = phinfo->ph_eval_at; int varno; if (bms_get_singleton_member(eval_at, &varno) && bms_nonempty_difference(phinfo->ph_needed, eval_at))//添加到需要的RelOptInfo中 { RelOptInfo *rel = find_base_rel(root, varno); rel->reltarget->exprs = lappend(rel->reltarget->exprs, copyObject(phinfo->ph_var)); /* reltarget's cost and width fields will be updated later */ } } }

在合营社内拉动应用一套新系统,总是供给投入额外对精力。日常的话,云存款和储蓄和云文书档案的放大全部相比较较轻松。究竟无论身处何种岗位,人人都亟待接触文书档案和知识,那样遍布的要求正是有协助利用的最佳技巧。大家只须求助推这一经过,就能够辅助公司从总体上升高效用和解决难点。

上边说过,CAS 提供三种办法:一种是引入可插拔的模块,另一种是用自身重写的 java 方法来覆盖基础版本中的 class 完结。

内部duration代表了销毁时间。然后在MainActivity里设置bitmap

2.2 推荐算法汇总

里头一个分享这么分类常用的引荐算法:

图片 1img

图5 推荐算法分类

这里涉及的Memory-based算法和Model-based算法的出入是如何?那也是本身事先关心的主题材料,找到个资料,解说得比较深透。

Memory-based techniques use the data (likes, votes, clicks, etc) that you have to establish correlations (similarities?) between either users (Collaborative Filtering) or items (Content-Based Recommendation) to recommend an item i to a user u who’s never seen it before. In the case of collaborative filtering, we get the recommendations from items seen by the user’s who are closest to u, hence the term collaborative. In contrast, content-based recommendation tries to compare items using their characteristics (movie genre, actors, book’s publisher or author… etc) to recommend similar new items.

In a nutshell, memory-based techniques rely heavily on simple similarity measures (Cosine similarity, Pearson correlation, Jaccard coefficient… etc) to match similar people or items together. If we have a huge matrix with users on one dimension and items on the other, with the cells containing votes or likes, then memory-based techniques use similarity measures on two vectors (rows or columns) of such a matrix to generate a number representing similarity.

Model-based techniques on the other hand try to further fill out this matrix. They tackle the task of “guessing” how much a user will like an item that they did not encounter before. For that they utilize several machine learning algorithms to train on the vector of items for a specific user, then they can build a model that can predict the user’s rating for a new item that has just been added to the system.

Since I’ll be working on news recommendations, the latter technique sounds much more interesting. Particularly since news items emerge very quickly (and disappear also very quickly), it makes sense that the system develops some smart way of detecting when a new piece of news will be interesting to the user even before other users see/rate it.

Popular model-based techniques are Bayesian Networks, Singular Value Decomposition, and Probabilistic Latent Semantic Analysis (or Probabilistic Latent Semantic Indexing). For some reason, all model-based techniques do not enjoy particularly happy-sounding names.

《游侠客个性化推荐算法实施》一文中梳理了工产业界应用的排序模型,大约经历八个级次:

图片 2img

图6 排序模型演进

正文不对上边包车型客车这几个算法举办详细的法规商讨,会比较复杂,风野趣能够再自动学习。

一、数据结构

PlaceHolderVar上一小节已介绍过PHInfo

 /* * Placeholder node for an expression to be evaluated below the top level * of a plan tree. This is used during planning to represent the contained * expression. At the end of the planning process it is replaced by either * the contained expression or a Var referring to a lower-level evaluation of * the contained expression. Typically the evaluation occurs below an outer * join, and Var references above the outer join might thereby yield NULL * instead of the expression value. * * Although the planner treats this as an expression node type, it is not * recognized by the parser or executor, so we declare it here rather than * in primnodes.h. */ typedef struct PlaceHolderVar { Expr xpr; Expr *phexpr; /* the represented expression */ Relids phrels; /* base relids syntactically within expr src */ Index phid; /* ID for PHV (unique within planner run) */ Index phlevelsup; /* > 0 if PHV belongs to outer query */ } PlaceHolderVar;

SpecialJoinInfo

 /* * "Special join" info. * * One-sided outer joins constrain the order of joining partially but not * completely. We flatten such joins into the planner's top-level list of * relations to join, but record information about each outer join in a * SpecialJoinInfo struct. These structs are kept in the PlannerInfo node's * join_info_list. * * Similarly, semijoins and antijoins created by flattening IN (subselect) * and EXISTS(subselect) clauses create partial constraints on join order. * These are likewise recorded in SpecialJoinInfo structs. * * We make SpecialJoinInfos for FULL JOINs even though there is no flexibility * of planning for them, because this simplifies make_join_rel()'s API. * * min_lefthand and min_righthand are the sets of base relids that must be * available on each side when performing the special join. lhs_strict is * true if the special join's condition cannot succeed when the LHS variables * are all NULL (this means that an outer join can commute with upper-level * outer joins even if it appears in their RHS). We don't bother to set * lhs_strict for FULL JOINs, however. * * It is not valid for either min_lefthand or min_righthand to be empty sets; * if they were, this would break the logic that enforces join order. * * syn_lefthand and syn_righthand are the sets of base relids that are * syntactically below this special join. (These are needed to help compute * min_lefthand and min_righthand for higher joins.) * * delay_upper_joins is set true if we detect a pushed-down clause that has * to be evaluated after this join is formed (because it references the RHS). * Any outer joins that have such a clause and this join in their RHS cannot * commute with this join, because that would leave noplace to check the * pushed-down clause. (We don't track this for FULL JOINs, either.) * * For a semijoin, we also extract the join operators and their RHS arguments * and set semi_operators, semi_rhs_exprs, semi_can_btree, and semi_can_hash. * This is done in support of possibly unique-ifying the RHS, so we don't * bother unless at least one of semi_can_btree and semi_can_hash can be set * true. (You might expect that this information would be computed during * join planning; but it's helpful to have it available during planning of * parameterized table scans, so we store it in the SpecialJoinInfo structs.) * * jointype is never JOIN_RIGHT; a RIGHT JOIN is handled by switching * the inputs to make it a LEFT JOIN. So the allowed values of jointype * in a join_info_list member are only LEFT, FULL, SEMI, or ANTI. * * For purposes of join selectivity estimation, we create transient * SpecialJoinInfo structures for regular inner joins; so it is possible * to have jointype == JOIN_INNER in such a structure, even though this is * not allowed within join_info_list. We also create transient * SpecialJoinInfos with jointype == JOIN_INNER for outer joins, since for * cost estimation purposes it is sometimes useful to know the join size under * plain innerjoin semantics. Note that lhs_strict, delay_upper_joins, and * of course the semi_xxx fields are not set meaningfully within such structs. */ typedef struct SpecialJoinInfo { NodeTag type; Relids min_lefthand; /* base relids in minimum LHS for join */ Relids min_righthand; /* base relids in minimum RHS for join */ Relids syn_lefthand; /* base relids syntactically within LHS */ Relids syn_righthand; /* base relids syntactically within RHS */ JoinType jointype; /* always INNER, LEFT, FULL, SEMI, or ANTI */ bool lhs_strict; /* joinclause is strict for some LHS rel */ bool delay_upper_joins; /* can't commute with upper RHS */ /* Remaining fields are set only for JOIN_SEMI jointype: */ bool semi_can_btree; /* true if semi_operators are all btree */ bool semi_can_hash; /* true if semi_operators are all hash */ List *semi_operators; /* OIDs of equality join operators */ List *semi_rhs_exprs; /* righthand-side expressions of these ops */ } SpecialJoinInfo;

差了一些全数部门都有保存和享受文书档案的必要:手艺单位要存款和储蓄设计素材,本事约定,公私钥,密码,还可能须要快速传输大文件;行政部门要发布手册、表格、文书档案,还要征集申请、报告;市集部门要力保整体门能每天访谈最新的产品和劳动质地;售前售后机关则有大气的顾客数据记录;设计部门要公布公司形象识别手册等。

看 maven overlay 的 官方文书档案(要夸一下,组织得一定雅观),轻易地说正是用来组合三个 WALacrosse项目标,就 CAS 项目以来,正是您会引进 CAS 项目组早就编写翻译好的某二个可运转的二进制基础版本,然后在此基础上照旧经过丰富配置插入自身必要的模块,恐怕重写一些兑现形式来定制特性化的作用,而基础版本中的代码会被遮住,进而项目会以你愿意的法子来执行。

#version 300 esprecision mediump float;in vec2 texCoord;out vec4 fragColor;uniform sampler2D sampler;uniform float percent;void main(){ vec4 samplerColor=texture(sampler,texCoord); float size=samplerColor.x+samplerColor.y+samplerColor.z+samplerColor.w; float curSize=4.0*percent; if(size<=curSize) discard; fragColor=samplerColor;}

3.1 天涯论坛推荐系统

明日头条算法框架结构师曹欢欢学士,做过贰遍《微博算法原理》的报告。首要涉嫌4部分:系统概览、内容深入分析、客商标签、评估分析。

  • 四类规范推荐特征

图片 3img

第一类是相关性特征,正是评估内容的习性和与顾客是还是不是相称。 第二类是境况特色,包涵地理地方、时间。那些既是bias特征,也能以此创设一些非凡特征。 第三类是热度特征。包罗全局热度、分类热度,宗旨热度,以及首要词热度等。 第四类是手拉手特征,它可以在一部分程度上帮忙解决所谓算法越推越窄的难点。

  • 模型的教练上,头条系超越百分之五十引进产品使用实时练习

图片 4img

模型的练习上,头条系一大半推荐产品应用实时陶冶。实时锻炼省能源况兼反馈快,这对音信产后出血品特别关键。客商需求展现信息能够被模型快捷捕捉并上报至下一刷的引荐效果。大家线上脚下基于storm集群实时管理样本数量,包蕴点击、表现、收藏、分享等动作类型。模型参数服务器是在那之中支出的一套高质量的序列,因为头条数据规模提升太快,类似的开源系统稳固和品质不可能满足,而笔者辈自研的系统底层做了众多针对的优化,提供了完美运营工具,更适配现成的事体场景。

此时此刻,头条的推荐介绍算法模型在世界范围内也是十分大的,包含几百亿本来特征和数十亿向量特征。全体的教练进程是线上服务器记录实时特征,导入到卡夫卡文件队列中,然后一发导入Storm集群开支卡夫卡数据,客户端回传推荐的label构造磨练样本,随后依据最新样本举办在线磨炼更新模型参数,最后线上模型得到更新。那个进度中至关心重视要的推移在顾客的动作反馈延时,因为文章引用后客商不必然立即看,不考虑那有的时刻,整个系统是差非常的少实时的。

图片 5img

但因为头条最近的内容积相当的大,加上小录制内容有相对等第,推荐系统不容许拥有内容总体由模型预估。所以须求统一筹算有个别召回政策,每回推荐时从海量内容中筛选出千等级的内容库。召回政策最珍视的需要是性质要非常,日常超时无法赶上50皮秒。

  • 顾客标签工程挑战更加大

图片 6img

内容剖析和顾客标签是援用系统的两大基本。内容分析涉及到机械学习的源委多一些,比较来讲,客户标签工程挑战越来越大。 微博常用的客商标签包涵客商感兴趣的项目和主旨、关键词、来源、基于兴趣的客商聚类以及种种垂直兴趣特征(车的型号,体育球队,股票等)。还会有性别、年龄、地点等音信。性别音信通过顾客第三方打交道账号登录获得。年龄新闻平日由模型预测,通过机型、阅读时间布满等预估。常驻地点来自客商授权访谈地方音讯,在地点音信的根基上经过守旧聚类的诀窍得到常驻点。常驻点组成别的消息,能够推断顾客的劳作地方、出差地方、旅游地方。这个客商标签特别有帮忙推荐。

图片 7img

自然最简便的顾客标签是浏览过的内容标签。但此间提到到一些数目处理政策。重要不外乎:一、过滤噪声。通过停留时间短的点击,过滤标题党。二、火爆惩罚。对客商在一部分吃香文章(如这段日子PG One的信息)上的动作做降权管理。理论上,传播范围异常的大的内容,置信度会减少。三、时间衰减。客商兴趣会发生偏移,由此计策更偏侧新的顾客作为。因而,随着客商动作的充实,老的特征权重会随时间衰减,新动作进献的特征权重会越来越大。四、惩罚表现。假使一篇推荐给顾客的小说未有被点击,相关特征(连串,关键词,来源)权重会被查办。当然相同的时候,也要考虑全局背景,是还是不是连锁内容推送相当多,以及相关的关门和dislike复信号等。

  • 措施营造,设计和推动集团云存款和储蓄和云文档。Hadoop集群压力过大,上线 Storm集群流式总计体系

图片 8img

直面那个挑衅。2015年初天涯论坛上线了顾客标签Storm集群流式总计连串。改成流式之后,只要有客户动作更新就更新标签,CPU代价非常的小,能够节省十分之八的CPU时间,大大收缩了总结财富开采。同期,只需几十台机械就足以支撑每日数千万客商的志趣模型更新,何况特征更新速度特别快,基本得以做到准实时。那套系统从上线一直选用到现在。

  • 重重商店算法做的不得了,而不是是程序猿工夫缺乏,而是必要三个强大的推行平台,还会有便捷的试验深入分析工具

图片 9img

A/B test系统原理

图片 10img

那是头条A/B Test实验系统的基本原理。首先我们会做在离线状态下做好客商分桶,然后线上抽成实验流量,将桶里顾客打上标签,分给实验组。比如,开多少个一成流量的尝试,多少个实验组各5%,二个5%是基线,计谋和线上大盘同样,别的三个是新的战术。

图片 11img

尝试进程中顾客动作会被收罗,基本上是准实时,每时辰都足以见见。但因为时辰数据有动乱,平常是以天为时间节点来看。动作采摘后会有日记管理、遍布式计算、写入数据库,特别轻易。

三、参照他事他说加以考察资料

planmain.crelation.h

平时来讲,一套集团网盘,加上一套知识处理种类,丰裕消除初创公司全员的云存款和储蓄和云文书档案须要。那样的产品市道上选拔比很多。譬喻,云存款和储蓄能够思考开源的店堂民用云盘如OwnCloudNextCloud,也足以选拔软硬结合的NAS系统如Synology;文书档案和文化合作能够依照须求,思量ConfluenceWikiMedia等知识库、维基系统、和内容管理连串。仍能设想买入可供个人安顿的每一项云文书档案产品。

在 IDE 构建的经过中会引进四个 overlay 文件夹,里面放置从 maven 饭店取下来的法定创设好的功底版本。上海体育场面中并未呈现。

图片 12image

2.1 推荐算法演化

2.1.1 人工作运动营

本条阶段是跋扈的,人工依据运维目标,手工业给一定类型的客商推送特定的源委。

优点是:

  • 便利推广特定的原委;
  • 推荐的内轻巧解释;

缺点是:

  • 千人一方面,推送的内容一样;
  • 人为筛选,推送,成本人力巨大;
  • 运行遵照自身的学识,主观性相当的大;

2.1.2 基于总括的推荐

会基于部分简练的总结学知识做推荐,比方有个别内别卖得最棒的火爆榜;再精心一些,将客户按个人特质划分,再求各类热度榜等。

优点是:

  • 人心向背正是大多数顾客垂怜的拟合,效果好;
  • 推荐的内轻便解释;

缺点是:

  • 千人四头,推送的开始和结果完全一样;
  • 马太效应,火爆的会愈发吃香,冷门的愈加冷门;
  • 作用很轻易实现天花板;

2.1.3 本性化推荐

此时此刻阶段的推荐介绍,会基于联合过滤算法、基于模型的算法、基于社交关系等,机器学习、深度学习慢慢引进,升高了推荐介绍效果。

优点是:

  • 效果要相对于事先,要好非常多;
  • 千人眼下,每一种人都有友好特其余推荐列表;

缺点是:

  • 秘诀较高,推荐系统搭建、算法设计、调优等等,都对开采者有较高的渴求;
  • 财力较高,何况是个漫长迭代优化的长河,人力物力投入极高;
 //... /* * Remove any useless outer joins. Ideally this would be done during * jointree preprocessing, but the necessary information isn't available * until we've built baserel data structures and classified qual clauses. */ joinlist = remove_useless_joins(root, joinlist);//清除无用的外连接 /* * Also, reduce any semijoins with unique inner rels to plain inner joins. * Likewise, this can't be done until now for lack of needed info. */ reduce_unique_semijoins;//消除半连接 /* * Now distribute "placeholders" to base rels as needed. This has to be * done after join removal because removal could change whether a * placeholder is evaluable at a base rel. */ add_placeholders_to_base_rels;//在"base rels"中添加PH //...
  • 出品应协助个体布署,私有域名;
  • 出品应辅助统一账号登入(详见本专栏统一账号一章)。

什么是 Overlay ?

近年一直在研究opengl,在android应用中可以很好的开展细粒度的对突显的机能开展调节,明天这么些腐蚀面具的功效正是基于opengl es来做的。话相当少说,先看效率图

1.2 基本架构

小编们先把引入系统轻易来看,那么它能够简化为如下的架构。

图片 13img

图1 推荐系统平时代时尚程

不论是目眩神摇只怕简单的引荐系统,基本都包括流程:

  • 1)结果显示部分。不管是app依然网页上,会有ui分界面用于呈现推荐介绍列表。
  • 2)行为日志部分。顾客的各类行为会被时刻记录并被上传出后台的日记系统,比如点击行为、购买行为、地理地点等等。那一个多少持续日常会被开展ETL(extract收取、transform转变、load加载),供迭代生成新模型实行预测。
  • 3)特征工程部分。获得客户的行为数据、物品的风味、场景数据等等,须求人工或自动地去从原来数据中抽出出特色。这个特点作为输入,为后边各个引入算法提供数据。特征选择很重视,错的特点必定带来错误的结果。
  • 4)召回部分。 有了客户的画像,然后使用多少工程和算法的艺术,从相对级的制品中锁定特定的候选集结,完毕对推荐列表的开首筛选,其在一定水平上主宰了排序阶段的频率和推举结果的上下。
  • 5)排序部分。针对上一步的候选集合,会实行更加精细化地打分、排序,同不时候思量新颖性、惊奇度、商业利润等的一名目好些个指标,得到一份最后的引入列表并拓宽体现。

一体化的引荐系统还有恐怕会席卷过多帮助模块,例如线下磨炼模块,让算法探究人口运用真实的野史数据,测量试验各种区别算法,初始评释算法优劣。线下测量检验效果不错的算法就能够被放到线上测量试验,即常用的A/B test系统。它使用流量分发系统筛选特定的客商呈现待测量试验算法生成的推荐列表,然后搜集那批特定顾客作为数据举行线上评测。

图片 14img

图2 厚菇街推荐系统架构

推荐系统每种部分可大可小,从图2可见,各部分涉及的工夫栈也非常多。终端app每时每刻都在相连反映种种日志,点击、体现、时间、地理地点等等新闻,那一个海量消息须要借助大数额相关软件栈援助,举个例子卡夫卡、spark、HDFS、Hive等,个中Kafka常被用于拍卖海量日志上报的开销难点。将数据开展ETL后存入Hive数据仓库,就可实行各个线上、线下测验使用。线下的算法会上线到线上境遇开展ABtest,ABtest涉及完整的测量试验回路打通,不然拿不到结果,也力不能支连忙支付迭代算法。线上引进系统还要关心实时特征、离线特征,在品质和各式指标、商业目的间取均衡。

以前的章节已介绍了函数query_planner中子函数query_planner中qp_callback和fix_placeholder_input_needed_levels的重要完结逻辑,本节继续介绍remove_useless_joins、reduce_unique_semijoins和add_placeholders_to_base_rels的兑现逻辑。

思想的变动

如若要深度推动云存款和储蓄和云文档在小卖部内任何的应用,主旨绪路则应是促进理念的改变————这一意见正是“集团云上办公室”:公司的具备文档资料都应该在云端发生、云端存取,全体的设施都以云文书档案资料创立端恐怕费用端,而非存款和储蓄端,不在云端的文档资料应视为未成功或不设有。

为了确定保证这种观念的完成,应协理每一个人有着易采访的客户端,具体情势能够是三个随手可访问、易回忆的营业所网盘地址,一块随系统运维的网络驱动器(使用WebDAV,FTP,Samba等合计),或是三个桌面或手提式有线电话机版的云盘顾客端等。

近年来急需大家依照官方基础版本的目录结构来调动本地品种的目录结构了。先看一下法定 overlay 基础版本的目录结构:

乘势时光的延期不停的设置percent,然后总结当前颜色的具备颜色分量的和固然低于总分量*percent的值的话,就抛弃该有的,不实行渲染。在CorronisonView中设置时间动画,不停修改CorronisonViewRender中percent的值

3.3 饿了么推荐系统:从0到1

对此别的一个外表要求, 系统都会创设二个QueryInfo, 同时从各类数据源提取UserInfo、ShopInfo、FoodInfo以及ABTest配置音讯等, 然后调用Ranker排序。以下是排序的主导流程:

#调取RankerManager, 起始化排序器Ranker:

  1. 依附ABTest配置新闻, 创设排序器Ranker;
  2. 调取ScorerManger, 内定所需打分器Scorer; 同期, Scorer会从ModelManager获取相应Model, 并校验;
  3. 调取FeatureManager, 钦点及校验Scorer所需特征Features。

#调取InstanceBuilder, 汇总全数打分器Scorer的性状, 总结对应排序项EntityInfo排序所需特征Features;

#对EntityInfo进行打分, 并按需对Records进行排序。

图片 15img

此处需求表明的是:任何一个模子Model都必需以打分器Scorer情势展现可能被调用。首如果依据以下几点思虑:

  • 模型迭代:举个例子同贰个Model,根据时间、地点、数据抽样等衍生出多少个版本Version;
  • 模型参数:比方结合格局时的权重与轮次设定,模型是或不是协助并行化等;
  • 特征参数:特征Feature计算参数,比如距离在分歧城市具有不相同的分支参数。

作为云存款和储蓄和云文书档案的框架结构者,大家应接纳合适的工具统一筹算这么些材质的蕴藏保管。在此之外,还应当从市肆全局角度,剖判思考到以下相关主题素材的化解:

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