recnl

2nd RecSys:NL Meetup

The 2nd RecSys:NL Meetup in Amsterdam will be hosted by Sanoma on April 16th at 18:00.The meetup will start with a few talks and afterwards there will be time to mingle and have drinks.

Sanoma is located at Capellalaan 65, Hoofddorp, close to Hoofddorp train station. Location info.

If you are coming by car, you will be able to park your car in the underground garage at Sanoma for free.

Please note that you need to sign up for a free ticket to participate. Tickets are available at our Eventbrite page.

Talk 1: Recommendations in SDL Fredhopper – Julian Ortega

SDL Fredhopper is a world leading e-commerce solution designed to help marketers target the right customers with the right items in the right context, optimize the online shopping experience and increase conversion rates, revenue and customer lifetime value. It powers over 300 web sites, in more than 40 languages, more than 50 countries and 12 of the top 100 online business – including the world’s 2nd largest online retailer. In this talk, I will bring SDL Fredhopper into context before looking at what happens in the recommendation space our business point of view as a service provider, while drilling down on a short study case of the events revolving around bringing a new recommender system to life – from idea to production. Finally, I will discuss some external influences beyond the technical scope.

Speaker bio:

Since 2011, Julian Ortega has been working as a software engineer for SDL Fredhopper, where he designs and develops processes to deliver product recommendations through Fredhopper’s merchandizing engine, using different techniques with work focused on developing pragmatic software engineering approaches to address the limitations of predictive models and recommender systems in industrial application domains. Julian is also currently half-way through his master in Computer Science at the VU Amsterdam. Find him on Twitter @jortegac

Talk 2: current.ly – Nico Schoonderwoerd

The current.ly app is PeerReach’s latest product. It recommends the best tweets for news trends, virals and memes. How current.ly gets those trends? From a selected group of recommenders!

Speaker bio:

Nico Schoonderwoerd is a physicist, worked at a hedge fund, disrupted real estate search on the web with miljoenhuizen.nl, and now applies his love for data at PeerReach. PeerReach identifies influential and relevant people and analyses 160 million twitter accounts

Talk 3: Scalable recommender systems and its similarity with advertising systems – Joaquin Delgado

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. Most of the targeting and real-time decisioning capability on scalable ad systems have been inspired by information retrieval (IR) techniques, which can be directly applied to recommender systems.

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 commercial recommender/advertising systems.

Speaker Bio:

Joaquin A. Delgado is currently Director of Advertising Technology at Intel Media – OnCue (an Intel Corp subsidiary recently acquired by Verizon Communications), 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.