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Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
Evolution of Search
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Evolution of Search

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Eachan Fletcher …

Eachan Fletcher
Christopher Lynch
Rob DeFeo
Expedia Affiliate Network

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  • intro talk
  • and by this I mean it has become the new lower bound everyone has reviews - ratings - tripadvisor - beautiful pictures - engaging maps it doesn ’ t take long for the bar on the web to be reset, and it doesn ’ t take long after that for a web business to haemorrhage customers when they don't have those things visitors expect
  • it is happening now, today, in some very big ways!
  • remember altavista when a search was “ keyword ” AND “ keyword ” you had to think about what you want and then try to figure out how that would be classified and organised in a machine and then approach it that way this is unnatural and a learned proficiency, not what intuitively comes to people
  • with google becoming more semantic, you can just type more comprehensive plain english sentences and get answers now we live in world where humans explore the web using the same terms that they use in casual conversation with each other this is more intuitive
  • then there ’ s stuff like this of course this is a very vegas centric search example, but we are where we are I guess so this stuff is happening - it is inevitable because it is how humans are wired, but it is already emerging in popular sites and apps
  • we like to think of ourselves as the platform for web travel businesses - and therefore I guess a good question is what are we doing to make sure that you, our partners, aren ’ t being left behind?
  • History of travel... So.. Anybody remember this? I ’ m not sure I do. This was your local friendly travel agent. You went to see him to help you plan your travel. Your next much needed getaway. He ’ s just a person, with specific expertise in the industry. He listened to you and then facilitated your booking based on his suggestions. Anyone notice one glaring omission from his desk? Anyone? Yeah.. There is no computer. He had a discussion with his clients. The conversation is what was important.
  • But, here ’ s what his interface looks like. Its all textual, but since he ’ s an expert, it makes sense. Our whole industry has grown up around the data in these mainframe system.
  • So, here we are. Fast forward to today. All the interfaces do look pretty much the same because that is the information that is available to us in the back end. You have your Destination , Arrival and Departure Dates , number of adults and a few options for Amenities . Few additional filtering options , but pretty much it and actually pretty restrictive. You have things like free wifi or breakfast , but what about things that are actually important to you? Things for me like a Tempurpedic Mattress? Where is that on the attribute list? There is no real insight! They ’ re just pretty buttons into the mainframe system. We haven ’ t put that agent ’ s expertise into the system. When you type that destination into the form interface, did the computer really listen do you?
  • No.. Not really. And that is just not natural. You don ’ t have the same conversation online as you would offline. You can ’ t just talk to your computer and expect to get the a conversation back. That ’ s why were resigned to those simple interfaces. More recently there are technologies such as Apple ’ s Siri that are very promising and have brought the possibility of computers understanding a conversation. But, still where is the expert? Where is the intelligence?
  • We ’ re missing the expert conversation. An exchange of ideas. Some requirements, some offers, some feedback then some counter offers. The dialog they have is key to making this work, because more then likely this couple has an idea of what they want, but that doesn ’ t mean they know exactly what they want. About 35% of travelers are undecided about where they want to travel, and we give them no easy way to do a ambiguous, Non destination specific search. Travel agents give you those options.
  • So online.... That ’ s why you put a phone number of on your website. It gives an opportunity to fall back on that conversation. And people who ring that number convert at a very healthy 30% on Average Take that in comparison to your typical online conversion of 2 - 3% if you are lucky. Is it because all Call center agents are super heros? No…. Its because they are people.. And people who listen and have a conversation with your clients.
  • Look.. Here are your super heros. The call center. With real people, having those real conversations with clients and making the sale.
  • So, we need to evolve. Lets teach the computer to allow that conversation with a person. Listen… Understand…. No, really understand that I just want a BEACH Holiday and that I LOVE my tempurpedic mattress. And provide the relevant suggestions that a user wants to see. We need an interface that can bring back a bit of that human agent experience. So, How can we do that???? Rob?
  • This a considerable problem, because firstly we need to get computers to understand how people express their intention, through a natural language to them, which its not something machines traditionally do very well. However there is a way , NLP, or Natural Language Processing (which is the field of computer science that deals with understanding human text, mainly through machine learning.) Lets go in to some detail of how the Natural Language Processing or NLP works by looking at what is actually does, concentrating on the parts most useful for our searching problem, the most relevant elements are it ’ s ability to identify semantic structure of sentences as this is the foundation for then recognizing entities plus can also help with identifying temporal terms.
  • The first part of NLP I will focus on is Part of Speech tagging, which means it is going to take some text, for example this simple phase, “ I want to go to Paris ” then break it down in to separate tokens (usually a word) and based on its definition and its context (i.e. relationship with near by words) mark each word with a part of speech definition . Once the phrase has been tagged, you can see “ I ” as a preposition, “ want ” is a verb, to is a “ TO ” (still not really sure what that is), “ visit ” is another verb and “ Paris ” a “ proper noun ” . If we extend out the phrase a little to add “ for a weekend ” . And tag it once again , you can see it makes “ for ” an different type of preposition, “ a ” determiner and “ weekend ” a noun. This is something similar to what they tried to teach most of us at school with the noun, adjective, verbs etc. However being dyslexic I never really had any clue what any of this meant. All very technical but its not obvious how we can use any of this apart from looking and seeing that Nouns, proper or not, are going more help in a search than the prepositions and determinators. But just using the nouns and attempting to build a search from them, will often lead to confusing results, especially with more complicated phrases, as there can be many nouns with no obvious relationships. Also if we made the phrase “ I want to visit Paris for a romantic weekend ” , “ romantic ” would be an adjective, which is actually something we are interested in because it sounds like a theme.
  • This is where Named entity recognition becomes important, as using this it gives us the possibility to pick out specific types of entities from the sentance and then work with them individually, so to give you an idea of what that could look like. We were able to get take the previous phrase “ I want to visit Paris for a Romantic weekend ” and identify that Paris is a location and that romantic is a theme, and that weekend is a temporal term. And that is the basis for a travel search, i.e. the when and the where and some amenities
  • After showing this to a bunch of people I got a lot of oohs and ahhs, but my boss, he said, ok, now make me something cool. Because that what you just saw is not really the travel agent, that is basically clever form filling in, it not much different to another layer on top of the green screen. We needed something with more intelligence to understand their intent and convert that to a booking. This is where we started to look at really what a travel agent does, and basically it can be described as saying how can I help you and listening, then adding some inferred knowledge are they on mayfair in london, or does the sign outside say “ discount holidays ” , build up some ideas probably combining a few different possibilities, present then and base on the feed back suggest alternatives
  • Lucky NLP is incredibily valuable because it removes one huge barrier. For example on a typical website you can search for say 12-15 popular attributes on a hotel. Now the UI guys and the marketing guys and I am sure a bunch of others sat down and decided these were the best and the best number to put on there. However we have about 1300 ways to describe a hotel, so we can look for hotels in many different ways, but it would be overwhelming, so this is where NLP has a real value, you tell us what you want, and we will go ahead and start matching it. No limits, no restriction, so ask for Beach sun loungers, Separate living room, Remote lighting, Billiards, Bidet, anything you like So we do the first thing an agent does , which is ask the question to start the conversation, and we hope to get something a little like this, and we do exactly what a travel agent does and attempt to figure out what that means. By detecting the concepts a little like how the named entity matching worked.
  • NLP is what we use for detection and and with it we get a few concepts, Romantic , weekend, Paris So we have a little bit of information from the sentence “ I want to visit Paris for a Romantic weekend ” but its not really enough to do a search, we need to use the extra information that we will get like, locale , and some basic geographical info to know where they are now, because that combined with our historical data can answer some other questions like the Departure date by looking at the booking windows of similar trips. Romantic suggests its 2 people, traveller to Paris from London Then also it can get more partner specific, such as how price sensitive are the customers. Once we build the profile with the detected and inferred concepts we will explain to them and demonstrate what they are, so the end user can adjust them if needed.
  • An example of how we search currently is can simply described as reducing with filters we start with 180000, filter to london get about 1000, fitness center gives 200, meeting rooms less than 100, the allows pets......1. What are the chances that although it matched actually your criteria is really the hotel you want. So people have been taught that giving too many filters give little or no results. It makes sense, but people cant really ask what they want, cus doing that will mean probably 0 results. So we limit ourselves to 1 or 2 extra filters, with no way of saying which are more important, then we have to page through loads of results to find other ways to distinguish them.
  • So what is the alternative, Discrete suggestions, Taking each concept, do a discrete search Location thats Paris in our example Amenities - romantic Price - from the profile we would have an idea Deals - and deals for the profile Trends - what do people normally do, i.e. the base rate, take a different example of someone from London wantting to travel to croatia for 1 week at the beech, the profile for this trip will probably for Spain, due to availabilty, deals etc, so if most poeple do that, then we should suggest it even its not explicitly asked for. After all our decisions making process could probably be defined as using Elastic mental filters , we dont use fixed bounds, if we say less than $150 a night for a 3 star we would most likely consider $175 for a 5 star and a better experience. We have some ideas and restrictions but we dont usually ignore other possibilties, we consider then on the merit of importance to us.
  • An agent will help us do this by walking us through some suggestions, obviously they dont just blindly walk you through a long list, google pretty much proved that we dont go much based the top 5 suggestions let alone look at page 3! So based on our feedback and expressions, they alter the suggests they give. That is where the grouping and the weighting comes together, you might of noticed that doing seperate searches like that, the same hotel will appear multiple times, well that is where we will score, group and weight them. This means we will take the results for each for the searches and combine them together, using their relavance score how close did it match the discrete search. Then use the general weight, i.e. how important is this aspect, this will alter the order and as we get feed back about which ones they are viewing we can suggest different ordering or alternatives. To give the user an intelligent experience, close to what we naturally would do if talking in a travel agent.
  • We will attempt to give absolutly as much information back as we can, what we detected and curicially why, what assumed and why, and what we suggested with detailed reasons for it. The reason is simple we are here to make a service but it is up to you to use it to make it truely stunning, we hope by giving you this intelligent and inutive service with as much information as possible it can help you achive this. I ’ ll get Chris to explain a little about the program and how we are running it.
  • So..... To Summarize People communicate in human language People like to describe their wants and needs in words Computers should “ understand ” and respond with intelligent and meaningful results Current travel interfaces are limiting Computers are dumb, but exceptionally obedient Computers need to understand the human interface Offline sales agents convert lookers into bookers exceptionally well What happens when online has some of the same capabilities that are so well proven to convert offline?
  • EAN Innovation Labs is a program and EAN ’ s R and D. is about exploring the boundaries of web travel. We ’ re investing in R and D for your benefit so you don ’ t have to. We need your feedback to make these products successful
  • So.. One of the innovations in Innovation Labs is Natural language processing We call it EAN Intelligent Query
  • EAN IQ is fundamentally an XML product allowing our parnters to dream up the best way to present all this information to users. But, here is just one way you could do it. Demo YourVisit.com
  • what are the applications of this kind of technology in travel? search is something we have working beta product today and that ’ s what we ’ ve talked about, but where else? mobile - input is a pain, and being able to standardise an experience across all platforms SEM - preserving meaning across transitions; imagine 1000 ’ s of campaigns, what about the 1000 ’ s of unique landing pages? discovery - the ideation of holidays and experiences which typically require lots of reading today with things like blogs and reviews and other content support - virtual agent to help you when you ’ re stuck - that ’ s the next place we ’ re going to take this, and let me give you a quick conceptual demo now before we wrap this up
  • Transcript

    • 1. Eachan FletcherChristopher Lynch Rob DeFeo Expedia Affiliate Network
    • 2. what does a robotuprising have to dowith how I plan my holidays?
    • 3. [content]the end of the^ world is here
    • 4. machines are secretly training you
    • 5. “cats” AND “hats”
    • 6. show me funny pictures of cats wearing hats
    • 7. site_____
    • 8. People who ring that number you put on your website convert at about 30% on Average
    • 9. NaturalLanguageProcessing
    • 10. “I (PRP) want (VBP) to (TO) visit (VB) Paris ” (NNP) “for (IN) a (DT) weekend” (NN)
    • 11. I want to visit Paris for a romantic weekend.Paris Locationromantic Themeweekend Temporal
    • 12. Build Discrete CombinedDetection profile hypothesis theory
    • 13. “I want to visit Paris for a Romantic weekend”
    • 14. Theme romanticTemporal weekendDestination ParisLocation LondonDeparture 2 weeks timeOccupancy 2 adults
    • 15. More filters = less results
    • 16. LocationAmenities Price Deals Trends
    • 17. Scoring, grouping and weighting
    • 18. oPeople communicate in human languageo People like to describe their wants and needs in wordso Computers should “understand” and respond with intelligent and meaningful resultsoCurrent travel interfaces are limitingo Computers are dumb, but exceptionally obediento Computers need to understand the human interfaceoOffline sales agents convert lookers into bookersexceptionally wello What happens when online has some of the same capabilities that are so well proven to convert offline?
    • 19. EAN (I)ntelligent (Q)uery
    • 20. searchmobilediscoverySEMsupport

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