In this presentation I will talk about the design of scalable recommender systems and its similarity with advertising systems. The problem of generating and delivering recommendations of content/products to appropriate audiences and ultimately to individual users at scale is largely similar to the matching problem in computational advertising, specially in the context of dealing with self and cross promotional content. In this analogy with online advertising a display opportunity triggers a recommendation. The actors are the publisher (website/medium/app owner) the advertiser (content owner or promoter), whereas the ads or creatives represent the items being recommended that compete for the display opportunity and may have different monetary value to the actors. To effectively control what is recommended to whom, targeting constraints need to be defined over an attribute space, typically grouped by type (Audience, Content, Context, etc.) where some associated values are not known until decisioning time. In addition to constraints, there are business objectives (e.g. delivery quota) defined by the actors. Both constraints and objectives can be encapsulated into and expressed as campaigns. Finally, there there is the concept of relevance, directly related to users' response prediction that is computed using the same attribute space used as signals.
As in advertising, recommendation systems require a serving platform where decisioning happens in real-time (few milliseconds) typically selecting an optimal set of items to display to the user from hundreds, sometimes thousands or millions of items. User actions are then taken as feedback and used to learn models that dynamically adjust order to meet business objectives.
This is a radical departure from the traditional item-based and user-based collaborative filtering approach to recommender systems, which fails to factor-in context, such as time-of-day, geo-location or category of the surrounding content to generate more accurate recommendations. Traditional approaches also fail to recognize that recommendations don't happen in a vacuum and as such may require the evaluation of business constraints and objectives. All this should be considered when designing and developing true commercial recommender/advertising systems.
Speaker Bio
Joaquin A. Delgado is currently Director of Advertising Technology at Intel Media (a wholly owned subsidiary of Intel Corp.), working on disruptive technologies in the Internet T.V. space. Previous to that he held CTO positions at AdBrite, Lending Club and TripleHop Technologies (acquired by Oracle). He was also Director of Engineering and Sr. Architect Principal at Yahoo! His expertise lies on distributed systems, advertising technology, machine learning, recommender systems and search. He holds a Ph.D in computer science and artificial intelligence from Nagoya Institute of Technology, Japan.
How to assess the company's readiness to intelligent automation of office pro...Alexandre Prozoroff
油
How to assess the company's readiness to intelligent automation of office processes?
舒从 仂亠仆亳 亞仂仂于仆仂 从仂仄仗舒仆亳亳 从 仂弍仂亳亰舒亳亳 仂亳仆 仗仂亠仂于?
http://cybersyn.ch/office
In this presentation I will talk about the design of scalable recommender systems and its similarity with advertising systems. The problem of generating and delivering recommendations of content/products to appropriate audiences and ultimately to individual users at scale is largely similar to the matching problem in computational advertising, specially in the context of dealing with self and cross promotional content. In this analogy with online advertising a display opportunity triggers a recommendation. The actors are the publisher (website/medium/app owner) the advertiser (content owner or promoter), whereas the ads or creatives represent the items being recommended that compete for the display opportunity and may have different monetary value to the actors. To effectively control what is recommended to whom, targeting constraints need to be defined over an attribute space, typically grouped by type (Audience, Content, Context, etc.) where some associated values are not known until decisioning time. In addition to constraints, there are business objectives (e.g. delivery quota) defined by the actors. Both constraints and objectives can be encapsulated into and expressed as campaigns. Finally, there there is the concept of relevance, directly related to users' response prediction that is computed using the same attribute space used as signals.
As in advertising, recommendation systems require a serving platform where decisioning happens in real-time (few milliseconds) typically selecting an optimal set of items to display to the user from hundreds, sometimes thousands or millions of items. User actions are then taken as feedback and used to learn models that dynamically adjust order to meet business objectives.
This is a radical departure from the traditional item-based and user-based collaborative filtering approach to recommender systems, which fails to factor-in context, such as time-of-day, geo-location or category of the surrounding content to generate more accurate recommendations. Traditional approaches also fail to recognize that recommendations don't happen in a vacuum and as such may require the evaluation of business constraints and objectives. All this should be considered when designing and developing true commercial recommender/advertising systems.
Speaker Bio
Joaquin A. Delgado is currently Director of Advertising Technology at Intel Media (a wholly owned subsidiary of Intel Corp.), working on disruptive technologies in the Internet T.V. space. Previous to that he held CTO positions at AdBrite, Lending Club and TripleHop Technologies (acquired by Oracle). He was also Director of Engineering and Sr. Architect Principal at Yahoo! His expertise lies on distributed systems, advertising technology, machine learning, recommender systems and search. He holds a Ph.D in computer science and artificial intelligence from Nagoya Institute of Technology, Japan.
How to assess the company's readiness to intelligent automation of office pro...Alexandre Prozoroff
油
How to assess the company's readiness to intelligent automation of office processes?
舒从 仂亠仆亳 亞仂仂于仆仂 从仂仄仗舒仆亳亳 从 仂弍仂亳亰舒亳亳 仂亳仆 仗仂亠仂于?
http://cybersyn.ch/office
仍亠从舒仆亟 亳亳仍仍仂于, Head of data monetization CleverDATA, 舒从舒亰舒仍 仆舒 亠仄亳仆舒亠 IAB Russia 于 舒仄从舒 从舒 Use Data 仂弍 仂仂弍亠仆仆仂 CRM Onboarding 亳 仆仂于 于亰仂于舒 仗亠亠亟 仆从仂仄 亟舒仆仆
CleverDATA is a leading Russian IT company with 43 branches in Russia and abroad and over 7,000 employees. It provides data management platforms, data marketing solutions, and predictive analytic models and big data processing solutions. It helps clients in various industries like online advertising, media, finance, retail, and public sector to make their businesses more effective with data-driven solutions like customer data enrichment, marketing automation, and operational analytics. The company aims to make data more intelligent and valuable for its customers through technologies related to the Fourth Industrial Revolution like the Internet of Things, artificial intelligence, and exponential organization models.
CleverDATA for Hadoop_Meetup_22052015_Spark_vs_HadoopCleverDATA
油
This document provides an overview of the CleverDATA company, including that it is one of the top 3 IT companies in Russia with over 5,500 employees. It also summarizes a presentation comparing Spark and Hadoop, noting that Spark is faster than Hadoop for interactive queries and real-time applications due to its ability to keep data in memory and support iterative algorithms. The presentation also discusses using Spark and Hadoop for batch processing, streaming data, and lambda architectures.