Challenges Encountered by Scaling Up Recommendation Services at Gravity R&D
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This talk was given by Bottyan Németh (Gravity R&D Product Owner & Co-founder) in the industry session at ACM Recsys Conference 2015 in Vienna. Presentation describes the challenges and solution we encountered by scaling up the recommendation services provided by Gravity.
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EIA2017Italy - Danny Lange - Artificial Intelligence - A Game Changer in App ...
EIA2017Italy - Danny Lange - Artificial Intelligence - A Game Changer in App ...
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Big Data in Production: Lessons from Running in the Cloud
Big Data in Production: Lessons from Running in the Cloud
How Companies are Using Cloud-Based Data Visualization & Analytics to Transfo...
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AWS Webcast - Journey through the Cloud - Cost Optimization
AWS Webcast - Journey through the Cloud - Cost Optimization
Big Data Day LA 2015 - Building a Big Data Culture in the Entertainment Indus...
Big Data Day LA 2015 - Building a Big Data Culture in the Entertainment Indus...
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This talk was given at HTE Medianet 2015 conference on the business ecosystem and its impact and requirement on recommender solutions on user generated content (UGC) video sharing portals (like Youtube, Dailymotion, etc). Besides the intro on Gravity R&D and the business model, we present some information on a real-world case study. We show that larger view counts are directly influence the recommendation quality.
Gravity rd corporate introduction - nlp matiné 2014
Gravity developed recommendation systems that improved user engagement for several clients. They won the Netflix Prize competition in 2006 with the best recommendation model. Their system for LiveJasmin increased user engagement metrics by up to 125%. Gravity also provides personalization solutions for international classifieds clients in 7 countries and helped increase user satisfaction and ad revenues for media companies in 5 countries.
Xây dựng mạng lưới tài năng trẻ trong sáng tạo – khởi nghiệp
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The document discusses success stories from companies that have implemented recommendation technology solutions from Gravity R&D, highlighting increased revenue, engagement, and satisfaction metrics. Examples include LiveJasmin seeing a 5.71% direct revenue increase and Randivonal experiencing a 2.5x rise in conversations between users. The final case discusses Netflix's million dollar prize for improving their recommendation system, which Gravity & The Ensemble won by outperforming Netflix's model by 10.1%.
Entrepreneurship & Innovation: Dual-core Engine
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The rise of Recommendation Engines
Gravity R&D uses machine learning and big data analytics to create personalized recommendations across all digital touchpoints. They have a team of 4 PhDs and have published over 200 journal articles that have been cited over 2,000 times. Their recommendation systems help users find relevant content and products based on their interests. Gravity has seen success implementing recommendation systems for companies like Tiki.vn and Anthropic, increasing revenue, engagement, and conversions. They are exploring new areas like deep learning recommendations, omnichannel personalization, and making recommendations more accessible through self-service platforms.
Lessons learnt at building recommendation services at industry scale
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RecSys 2015: Large-scale real-time product recommendation at Criteo
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Understanding How CQL3 Maps to Cassandra's Internal Data Structure
CQL3 is the newly ordained, canonical, and best-practices means of interacting with Cassandra. Indeed, the Apache Cassandra documentation itself declares that the Thrift API as “legacy” and recommends that CQL3 be used instead. But I’ve heard several people express their concern over the added layer of abstraction. There seems to be an uncertainty about what’s really happening inside of Cassandra.
In this presentation we will open up the hood and take a look at exactly how Cassandra is treating CQL3 queries. Our first stop will be the Cassandra data structure itself. We will briefly review the concepts of keyspaces, columnfamilies, rows, and columns. And we will explain where this data structure excels and where it does not. Composite rowkeys and columnnames are heavily used with CQL3, so we'll cover their functionality as well.
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Dynamically Allocate Cluster Resources to your Spark Application
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At the sold-out Spark & Machine Learning Meetup in Brussels on October 27, 2016, Nick Pentreath of the Spark Technology Center teamed up with Jean-François Puget of IBM Analytics to deliver a talk called Creating an end-to-endRecommender System with Apache Spark and Elasticsearch.
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Similar to Challenges Encountered by Scaling Up Recommendation Services at Gravity R&D
There are 250 Database products, are you running the right one?
This webinar discusses choosing the right database for organizations. It will cover industry trends driving data and database evolution, real-world use cases where speed and scale are important, and an architecture overview. Speakers from Forrester and Aerospike will discuss how new applications are challenging traditional databases and how Aerospike's in-memory database provides extremely high performance for large-scale, data-intensive workloads. The agenda includes an industry overview, tips for choosing a database, how data has evolved, examples where low latency is critical, and a question and answer session.
How to run Real Time processing on Big Data / Ron Zavner (GigaSpaces)
This document discusses how GigaSpaces software provides real-time processing of big data through its in-memory data grid technology. It highlights challenges with current tier-based architectures in meeting demands for real-time analytics at scale. GigaSpaces' XAP platform is presented as a solution that scales the data, messaging, and application tiers in memory to enable real-time querying and analysis of large datasets across multiple sites. The document provides an overview of XAP features and capabilities as well as example use cases from customers in e-commerce and banking.
The Cloud - What's different
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Serverless Computing: Driving Innovation and Business Value
See webinar recording of this presentation at https://resource.alibabacloud.com/webinar/live.htm?&webinarId=68
In 2018, serverless computing dominated the headlines, tech shows, and radio waves, but why? In this webinar, we will explore the key concepts of serverless computing and introduce Function Compute, Alibaba Cloud’s event-driven and fully-managed compute service. Function Compute enables users to build and run applications with minimal effort on underlying compute infrastructure. We will also explore some of the basic patterns and leading practices on deploying serverless architecture. And learn how to get started with serverless computing by using Alibaba Cloud’s Function Compute.
Learn more about Alibaba Cloud’s Function Compute:
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Amazon Elastic Compute Cloud (Amazon EC2) provides a broad selection of instance types to accommodate a diverse mix of workloads. In this technical session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current-generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
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AGENDA
- Continuent Introduction
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- Continuent Tungsten Solutions & Benefits
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PRESENTER
Eero Teerikorpi - founder and CEO, Continuent - is a 7-time serial entrepreneur who has more than 30 years of high-tech management and enterprise software experience. Eero has been in the MySQL marketplace virtually since day one, from the early 2000s. Eero has held top management positions at various cross-Atlantic entities (CEO at Alcom Corporation, President at Capslock, Executive Board Member at Esker S.A.) Eero started his career as a Product Manager at Apple Computer in Finland in the mid-80s. Eero also owns and manages a boutique NOET Vineyards producing high-quality dry-farmed Cabernet Sauvignon.
Eero is a former Navy officer and still an avid sailor on San Francisco Bay and around the world. Eero is a very active sportsman: a 4+ tennis player, a rookie golfer, a very careful mountain biker, and an experienced (40+ years) skier, both slalom and cross-country.
Intro to AWS: Amazon EC2 and Compute Services
Amazon EC2 is a cloud computing service that provides virtual computing resources such as servers and storage. It allows users to launch virtual machine instances that can be used to build and host applications. EC2 has grown significantly since its launch in 2006 to include many instance types, operating systems, pricing options, and features to improve performance, security, and scalability. Customers use EC2 for its flexibility, low costs, global accessibility, security, and ability to easily scale resources to meet variable computing needs.
Scale Your Load Balancer from 0 to 1 million TPS on Azure
For years, enterprises have relied on appliance-based (hardware or virtual) load balancers. Unfortunately, these legacy ADCs are inflexible at scale, costly due to overprovisioning for peak traffic, and slow to respond to changes or security incidents.
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Watch this webinar to see how Avi can scale up and down quickly on the Microsoft Azure Cloud.
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AWS Summit Kuala Lumpur Keynote with Stephen Orban - Head of Enterprise Strategy
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Data Culture Series - Keynote - 3rd Dec
Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward?
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Hadoop and NoSQL databases have emerged as leading choices by bringing new capabilities to the field of data management and analysis. At the same time, the RDBMS, firmly entrenched in most enterprises, continues to advance in features and varieties to address new challenges.
Join us for a special roundtable webcast on April 7th to learn:
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Most of the time, infrastructure is under-utilised and it’s near impossible for organisations to forecast the amount of computing power they will need in the future to serve their customers and suppliers.
To overcome these challenges, organisations can instead utilise the cloud to support their real-time data analytics activities. Scalable, agile and secure, cloud-based infrastructure enables organisations to quickly spin up infrastructure to support their data analytics projects exactly when it is needed. Importantly, they can ‘switch off’ infrastructure when it is not.
BluePi Consulting and Amazon Web Services (AWS) are giving you the opportunity to discover how organisations are using real time data analytics to gain new insights from their information to improve the customer experience and drive competitive advantage.
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In this presentation, Intel presents their journey, starting small and growing Data Virtualization to an Enterprise IT enabling use cases such as samples management, cloud, and big data for sales and marketing.
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"This presentation will introduce Kinesis, the new AWS service for real-time streaming big data ingestion and processing.
We’ll provide an overview of the key scenarios and business use cases suitable for real-time processing, and discuss how AWS designed Amazon Kinesis to help customers shift from a traditional batch-oriented processing of data to a continual real-time processing model. We’ll provide an overview of the key concepts, attributes, APIs and features of the service, and discuss building a Kinesis-enabled application for real-time processing. We’ll also contrast with other approaches for streaming data ingestion and processing. Finally, we’ll also discuss how Kinesis fits as part of a larger big data infrastructure on AWS, including S3, DynamoDB, EMR, and Redshift."
Neha Narkhede | Kafka Summit London 2019 Keynote | Event Streaming: Our Cloud...
More info: https://cnfl.io/cloud-native-experience-for-kafka-in-cloud | Neha Narkhede is co-founder and CTO at Confluent, a company backing the popular Apache Kafka messaging system. Prior to founding Confluent, Neha led streams infrastructure at LinkedIn, where she was responsible for LinkedIn’s streaming infrastructure built on top of Apache Kafka and Apache Samza. She is one of the initial authors of Apache Kafka and a committer and PMC member on the project.
Vertica Analytics Database general overview
Vertica is an advanced analytics platform that combines high-performance query processing with advanced analytics and machine learning capabilities. It bridges the gap between high-cost legacy data warehouses and less powerful Hadoop data lakes. Vertica uses a massively parallel processing architecture to deliver fast analytics on large datasets regardless of where the data resides. It has been implemented by various companies across industries to drive customer experience management, operational analytics, and fraud detection through applications like predictive maintenance, customer churn analysis, and network optimization.
Migrating from Oracle to Postgres
This presentation reviews the key methodologies that all the member of the team should consider such as:
- How to prioritize the right application or project for your first Oracle
- Tips to execute a well-defined, phased migration process to minimize risk and increase time to value
- Handling the common concerns and pitfalls related to a migration project
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- Suggestions on how you can achieve independence from an Oracle database – without sacrificing performance.
Target audience: This presentation is intended for IT Decision-Makers and Leaders on the team involved in Database decisions and execution.
For more information, please email sales@enterprisedb.com
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Using real time big data analytics for competitive advantage
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This talk was given by István Pilászy, co-founder and head of core development at Gravity R&D, at LSRS workshop at Recsys 2015. Messages of the talk: (1) in industry item-2-item (i2i) recommendation is the dominant case, hardly researched by academia; (2) in industry you have typically implicit feedback data; (3) matrix factorization (MF) is good to optimize error metric, but less obvious for top-N and i2i recommendations. (4) item-kNN in most cases outperforms MF for i2i in terms of CTR; (5) Performance heavily depends on the domain and the recommendation scenario.
General factorization framework for context-aware recommendations
This document proposes a general factorization framework (GFF) for context-aware recommendation that takes a preference model as input and computes latent feature matrices for different context dimensions. GFF allows for easy experimentation with various linear models on both explicit and implicit feedback recommendation tasks involving multiple context dimensions. The document demonstrates GFF's potential by exploring different preference models on a 4-dimensional context problem using real-world implicit feedback datasets, showing that proper preference modeling significantly improves accuracy and previously unused models outperform traditional ones. GFF is also extended to incorporate additional information like item metadata, social networks and session data beyond just context.
Tartalomgazdagítás (content enrichment)
This is the slides from conTEXT conference held by SPSS Consulting (Hungary) at 2014.11.21. Slides are in Hungarian
Tartalomgazdagítás szövegbányászattal Linked Open Data alapján és felhasználása ajánlórendszerekben
Ajánlórendszerek jellemzően két adatforrás alapján dolgoznak: a felhasználók tartalomfogyasztási szokásaik (collaborative filtering) és a tartalomak metaadatai alapján (content based filtering). A tartalomleíró adatoknak nagy előnye, hogy az ajánlások könnyen magyarázhatóak a felhasználóknak (azért ajánjuk, mert szereted Brad Pitt filmjeit, stb.), és már új tartalmak esetén is alkalmazhatók. Ezért nyilván nagyon fontos, hogy jó minőségűek és minél gazdagabbak legyenek a tartalmakat leíró metaadatok. Ezek a feltételek azonban gyakran nem teljesülnek, ezért szükség ún. tartalomgazdagításra (content enrichment), hogy jobban jellemezhetőek legyenek a tartalmak. A Gravity alkalmazott megoldás nyilvánosan elérhető adatbázisok (Linked Open Data, LOD) segítségével végzi el a tartalomgazdagítást, mint pl. a Freebase és a DBpedia. A probléma számos szövegbányászati részfeladatot tartalmaz, mint pl. névelemek felismerése és egyértelműsítése (színész, rendező neve; film címe), névelemek tulajdonságainak meghatározása (pl. színész fontosabb adatai) különböző forrásokból származó adatok egyesítése, átfedő, ill. esetleg inkonzisztens adatok kanonizálása. Az előadás során röviden ismertetésre kerül az általunk alkalmazott RDF alapú megoldás, a SPARQL lekérdezőnyelv és alkalmazása, és rámutatunk néhány lehetséges megoldására a szövegbányázati feladatoknak.
Idomaar crowd rec_reference_fw
Idomaar is an open-source benchmarking framework for recommender systems created by the CrowdRec EU-project. It enables impartial evaluation of recommenders solutions from different aspect: (1) recommendation quality (2) technical aspects (3) business aspects.
Main contributors of Idomaar: Moviri, Gravity RD, Technical University of Delft, Technical University Berlin
Big Data in Online Classifieds
The slideshow was presented at ICMA Conference in Helsinki at the "How to Turn Big Data into Dollars" Workshop organized by Gravity R&D,
The presentation reviews the heterogeneity of data sources at classified media, shows the massive size of data available, and give some insights how to use those data for personalization in various scenarios.
Context-aware similarities within the factorization framework - presented at ...
Talk deals with a practical recommendation sceaario - item-2-item recommendation (similar/related items) with implicit feedback and context. Solution is provided in the factorization framework.
Paper abstract (to appear in ACM digital Library) Item-to-item recommendation - when the most similar items sought to the actual item - is an important recommendation scenario in practical recommender systems. One way to solve this task is to use the similarity between item feature vectors of factorization models. By doing so, one may transfer the well-known accuracy of factorization models observed at the personalized recommendations to the item-to-item case. This paper introduces context-awareness to item similarities in the factorization framework. Two levels of context-aware similarities are defined and applied to two context-aware implicit feedback based factorization methods (iTALS and iTALSx). We investigate the advantages and drawbacks of the approaches on four real life implicit feedback data sets and we characterize the conditions for their application. The results suggest that it is worth using contextual information for item-to-item recommendations in the factorization framework, however, one should carefully select the appropriate method to achieve similar accuracy gain than in the case of the more general item-to-user recommendation scenario.
Slides from CARR 2012 WS - Enhancing Matrix Factorization Through Initializat...
Executive summary: The paper propose a new method to solve the cold start problem for matrix factorization using metadata. The method works with the realistic implicit feedback scenario. With a smart initialization of the feature matrices better performance values were achieved on several data sets.
Paper abstract: The implicit feedback based recommendation problem—
when only the user history is available but there are no ratings—is a much harder task than the explicit feedback based recommendation problem, due to the inherent uncertainty of the interpretation of such user feedbacks. Still, this practically important recommendation task received less attention and therefore there are only a few common implicit feedback based algorithms and benchmark datasets. This paper focuses on a common matrix factorization method for the implicit problem and investigates if recommendation performance can be improved by appropriate initialization of the feature vectors before training. We present a general initialization framework that preserves the similarity between entities (users/items) when creating the initial feature vectors,
where similarity is defined using e.g. context or metadata information. We demonstrate how the proposed initialization framework can be coupled with MF algorithms. The efficiency of the initialization is evaluated using various context and metadata based similarity concepts on two implicit variants of the MovieLens 10M
dataset and one real life implicit database. It is shown that performance gain can attain 10% improvement in recall@50 and in AUC@50.
Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Imp...
Executive summary: The paper deals with the implicit feedback problem for recommender systems, and propose a fast context-aware tensor factorization method that can integrate any kind of contextual information.
Paper abstract: Albeit the implicit feedback based recommendation problem—when only the user history is available but there are no ratings—is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark datasets, therefore new ideas
are usually experimented on explicit benchmarks. In this paper, we pro-
pose a generic context-aware implicit feedback recommender algorithm,
coined iTALS. iTALSapplies a fast, ALS-basedtensor factorization learn-
ing method that scales linearly with the number of non-zero elements
in the tensor. The method also allows us to incorporate various contex-
tual information into the model while maintaining its computational effi-
ciency. We present two context-aware implementation variants of iTALS.
The first incorporates seasonality and enables to distinguish user behav-
ior in different time intervals. The other views the user history as sequen-
tial information and has the ability to recognize usage pattern typical
to certain group of items, e.g. to automatically tell apart product types
that are typically purchased repetitively or once. Experiments performed
on five implicit datasets (LastFM 1K, Grocery, VoD, and “implicitized”
Netflix and MovieLens 10M) show that by integrating context-aware
information with our factorization framework into the state-of-the-art
implicit recommender algorithm the recommendation quality improves
significantly.
Recommender Systems Evaluation: A 3D Benchmark - presented at RUE 2012 worksh...
Recommender systems add value to vast content resources by matching users with items of interest. In recent years, immense progress has been made in recommendation techniques. The evaluation of these has however not been matched and is threatening to impede the further development of recommender systems. In this paper we propose an approach that addresses this impasse by formulating a novel evaluation concept adopting aspects from recommender systems research and industry. Our model can express the quality of a recommender algorithm from three perspectives, the end consumer (user), the service provider and the vendor (business and technique for both). We review current benchmarking activities and point out their shortcomings, which are addressed by our model. We also explain how our 3D benchmarking framework would apply to a specific use case.
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◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
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一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
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三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
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◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
学历顾问：微信：176555708
Should Repositories Participate in the Fediverse?
Presentation for OR2024 making the case that repositories could play a part in the "fediverse" of distributed social applications
假文凭国外(Adelaide毕业证)澳大利亚国立大学毕业证成绩单办理
购买澳大利亚国立大学毕业证>【微信176555708】办理澳大利亚国立大学毕业证成绩单【微信176555708】Adelaide毕业证成绩单Adelaide学历证书Adelaide文凭【Adelaide毕业套号文凭网认证澳大利亚国立大学毕业证成绩单】【哪里买澳大利亚国立大学毕业证文凭Adelaide成绩学校快递邮寄信封】【开版澳大利亚国立大学文凭】Adelaide留信认证本科硕士学历认证(诚招代理)微信：176555708办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
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办理澳大利亚国立大学澳大利亚国立大学毕业证成绩单流程：
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2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
-办理真实使馆公证（即留学回国人员证明）
-办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度）
-全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！
（详情请加一下 文凭顾问+微信:176555708）欢迎咨询！
可查真实(Monash毕业证)西澳大学毕业证成绩单退学买
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一整套留学文凭证件服务：
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二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
1客户提供办理西澳大学西澳大学毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信176555708
留学挂科(UofM毕业证)明尼苏达大学毕业证成绩单复刻办理
精仿办理明尼苏达大学学历证书＜176555708微信＞【毕业证明信-推荐信做学费单＞【微信176555708】【制作UofM毕业证文凭认证明尼苏达大学毕业证成绩单购买】【UofM毕业证书】{明尼苏达大学文凭购买}】成绩单，录取通知书，Offer，在读证明，雅思托福成绩单，真实大使馆教育部认证，回国人员证明，＞【制作UofM毕业证文凭认证明尼苏达大学毕业证成绩单购买】【UofM毕业证书】{明尼苏达大学文凭购买}留信网认证。
明尼苏达大学学历证书＜微信176555708（一对一服务包括毕业院长签字,专业课程,学位类型,专业或教育领域,以及毕业日期.不要忽视这些细节.这两份文件同样重要!毕业证成绩单文凭留信网学历认证！）（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
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◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
学历顾问：微信：176555708
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制作原版1:1(Monash毕业证)莫纳什大学毕业证成绩单办理假
退学买莫纳什大学毕业证>【微信176555708】办理莫纳什大学毕业证成绩单【微信176555708】Monash毕业证成绩单Monash学历证书Monash文凭【Monash毕业套号文凭网认证莫纳什大学毕业证成绩单】【哪里买莫纳什大学毕业证文凭Monash成绩学校快递邮寄信封】【开版莫纳什大学文凭】Monash留信认证本科硕士学历认证1:1完美还原海外各大学毕业材料上的工艺：水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪。
可办理以下真实莫纳什大学存档留学生信息存档认证：
1莫纳什大学真实留信网认证（网上可查永久存档无风险百分百成功入库）；
2真实教育部认证（留服）等一切高仿或者真实可查认证服务（暂时不可办理）；
3购买英美真实学籍（不用正常就读直接出学历）；
4英美一年硕士保毕业证项目（保录取学校挂名不用正常就读保毕业）
留学本科/硕士毕业证书成绩单制作流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询莫纳什大学莫纳什大学毕业证offer）；
2开始安排制作莫纳什大学毕业证成绩单电子图；
3莫纳什大学毕业证成绩单电子版做好以后发送给您确认；
4莫纳什大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5莫纳什大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — — — — — — — — 《文凭顾问微信：176555708》
一比一原版(USYD毕业证)悉尼大学毕业证如何办理
USYD硕士毕业证成绩单【微信95270640】《如何办理悉尼大学毕业证认证》【办证Q微信95270640】《悉尼大学文凭毕业证制作》《USYD学历学位证书哪里买》办理悉尼大学学位证书扫描件、办理悉尼大学雅思证书！
国际留学归国服务中心《如何办悉尼大学毕业证认证》《USYD学位证书扫描件哪里买》实体公司，注册经营，行业标杆，精益求精！
如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证+微信95270640
2、面对父母的压力，希望尽快拿到悉尼大学悉尼大学毕业证offer；
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4、回国时间很长，忘记办理；
5、回国马上就要找工作，办给用人单位看；
6、企事业单位必须要求办理的；
面向美国乔治城大学毕业留学生提供以下服务:
【★悉尼大学悉尼大学毕业证offer毕业证、成绩单等全套材料，从防伪到印刷，从水印到钢印烫金，与学校100%相同】
【★真实使馆认证（留学人员回国证明），使馆存档可通过大使馆查询确认】
【★真实教育部认证，教育部存档，教育部留服网站可查】
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我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
专业服务，请勿犹豫联系我
如果您真实毕业回国，对于学历认证无从下手，请联系我，我们免费帮您递交
诚招代理：本公司诚聘当地代理人员，如果你有业余时间，或者你有同学朋友需要，有兴趣就请联系我
你赢我赢，共创双赢
你做代理，可以帮助悉尼大学同学朋友
你做代理，可以拯救悉尼大学失足青年
你做代理，可以挽救悉尼大学一个个人才
你做代理，你将是别人人生悉尼大学的转折点
你做代理，可以改变自己，改变他人，给他人和自己一个机会过暑假的小学生快乐的日子总是过得飞快山娃尚未完全认清那几位小朋友时他们却一个接一个地回家了山娃这时才恍然发现二个月的暑假已转到了尽头他的城市生活也将划上一个不很圆满的句号了值得庆幸的是山娃早记下了他们的学校和联系方式说也奇怪在山娃离城的头一天父亲居然请假陪山娃耍了一天那一天父亲陪着山娃辗转长隆水上乐园疯了一整天水上漂流高空冲浪看大马戏大凡里面有的父亲都带着他去疯一把山娃算了算这一次足足花了老爸元很
Gen Z and the marketplaces - let's translate their needs
The product workshop focused on exploring the requirements of Generation Z in relation to marketplace dynamics. We delved into their specific needs, examined the specifics in their shopping preferences, and analyzed their preferred methods for accessing information and making purchases within a marketplace. Through the study of real-life cases , we tried to gain valuable insights into enhancing the marketplace experience for Generation Z.
The workshop was held on the DMA Conference in Vienna June 2024.
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HijackLoader Evolution: Interactive Process Hollowing
CrowdStrike researchers have identified a HijackLoader (aka IDAT Loader) sample that employs sophisticated evasion techniques to enhance the complexity of the threat. HijackLoader, an increasingly popular tool among adversaries for deploying additional payloads and tooling, continues to evolve as its developers experiment and enhance its capabilities.
In their analysis of a recent HijackLoader sample, CrowdStrike researchers discovered new techniques designed to increase the defense evasion capabilities of the loader. The malware developer used a standard process hollowing technique coupled with an additional trigger that was activated by the parent process writing to a pipe. This new approach, called "Interactive Process Hollowing", has the potential to make defense evasion stealthier.
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Gen Z and the marketplaces - let's translate their needs
Gen Z and the marketplaces - let's translate their needs
HijackLoader Evolution: Interactive Process Hollowing
HijackLoader Evolution: Interactive Process Hollowing
Challenges Encountered by Scaling Up Recommendation Services at Gravity R&D
- 1. Scaling UP Challenges Encountered Scaling Up Recommendation Services @Gravity R&D Bottyán Németh
- 2. Who we are and what we do Gravity R&D is a recommender system vendor company. We provide recommendation as a service since 2009 for our customers all around the globe. 2
- 3. How we imagine growth? 3 ?
- 4. How we imagine growth? 4
- 5. How it actually happens? 5 ?
- 6. How it actually happens? 6
- 7. # of requests 7 Vatera.hu largest online marketplace in Hungary served by one “server” Alexa TOP100 video chat webpage (~40M recommendation requests / day): Served by 5 application servers and 1 DB Too many events to store in MySQL using Cassandra (v0.6) Training time for IALS too long speedup by IALS1 Max. 5 sec latency in “product” availability
- 8. Using new/beta technologies 8 Cassandra (v0.6) Nginx (v0.5) (22% of top 1M sites) Kafka (v0.8) MySQL auto. failover
- 9. Reaching the limits 9 Even if the technology is widely used if you reach it’s limits the optimization is very costly / time consuming. Java GC – service collapsed because increased minor GC times due to a JVM bug (26th of January 2013) Maintaining MySQL with lots of data (optimize table, slave replication lag, faster storage device)
- 10. Complexity increases 10 There is always a business request or an algorithmic development which requires more resources.
- 11. Optimizations 11
- 12. Infrastructure 12 Currently 200+ hosts and 3500+ services monitored 0 50 100 150 200 250 2008 2009 2010 2011 2012 2013 2014 2015 2016 Number of servers
- 13. # of items 13 How to store item model / metadata in memory to serve requests fast?
- 14. # of items 14 How to store item model / metadata in memory to serve requests fast? VS. Auto increment IDs for the items? 231 not enough
- 15. Preconceptions 15 More data better results. If the CTR of a new algorithm is low than the old algorithm is better. Daily retrain is enough.
- 16. Training frequency 16 CTR decreased in the morning
- 17. 100+ Algorithms 17 0 10 20 30 40 50 60 0 20 40 60 80 100 120 Number of times an algorithm is used
- 18. Now 18 • Performance: Gravity’s performance oriented architecture enables real-time response to the always changing environment and user behavior • Algorithms: more than 100 different recommendation algorithm enables true personalization and to reach the highest KPIs in different domains • Infrastructure: fast response times all around the globe and data security thanks to the private cloud infrastructure located in 4 different data centers • Flexibility: the advanced business rule engine with intuitive user interface allows to satisfy various business requirements Performance 140M requests served daily Algorithms 30 man-years invested Infrastructure 4 data centers globally Flexibility 100s of logics configurable
- 19. Cross the river when you come to it 19
- 20. Thank you! 20