Rex the foodbot docx

PROJECT​ ​2:​ ​REX THE​ ​CHATBOT Conversational​ ​UI

REX​ ​THE​ ​CHATBOT

a​ ​Facebook​ ​chat​ ​bot​ ​leveraging​ ​Yelp’s​ ​data​ ​and machine​ ​learning​ ​through​ ​account​ ​integrations across​ ​Yelp​ ​and​ ​BtownMenus.

Team​ ​C:​ ​Alison,​ ​Cameron,​ ​Hayden,​ ​Sanchit,​ ​Tosh Interaction​ ​Design​ ​Practice

Design​ ​Strategy Yelp,​ ​the​ ​current​ ​food/restaurant​ ​recommendation​ ​website​ ​has​ ​142​ ​million​ ​active​ ​users​ ​contributing​ ​to the​ ​food​ ​ratings​ ​and​ ​reviews​ ​system.​ ​This​ ​communal​ ​contribution​ ​has​ ​lead​ ​to​ ​a​ ​comprehensive database​ ​of​ ​food​ ​and​ ​restaurants​ ​to​ ​help​ ​users​ ​make​ ​food/restaurant​ ​decisions,​ ​as​ ​well​ ​as​ ​promoting local​ ​business.​ ​However,​ ​this​ ​has​ ​also​ ​caused​ ​problems: ●

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Food​ ​searching​ ​process​ ​is​ ​time-consuming.

People​ ​have​ ​to​ ​go​ ​through​ ​ratings​ ​and​ ​reviews​ ​on​ ​Yelp​ ​to make​ ​restaurant/food​ ​decisions​ ​when​ ​they​ ​are​ ​new​ ​to​ ​a place.

A​ ​large​ ​amount​ ​of​ ​ratings​ ​and​ ​reviews​ ​are​ ​not​ ​relevant​ ​to​ ​the​ ​user.

Food​ ​ratings​ ​and​ ​reviews​ ​are​ ​written​ ​by​ ​users​ ​who​ ​have various​ ​personal​ ​tastes,​ ​making​ ​the​ ​reviews​ ​less​ ​relevant and​ ​qualitative​ ​for​ ​visitors.

To​ ​address​ ​the​ ​above​ ​mentioned​ ​problems,​ ​we​ ​aim​ ​to Design​ ​a​ ​Facebook​ ​chat​ ​bot​ ​leveraging​ ​Yelp’s​ ​data​ ​and​ ​machine​ ​learning​ ​through​ ​account​ ​integrations across​ ​Yelp​ ​and​ ​BtownMenus.

The​ ​output​ ​of​ ​our​ ​system​ ​should​ ​be​ ​more​ ​qualitative,​ ​efficient​ ​and​ ​“human”​ ​than​ ​Yelp’s​ ​current​ ​reviews and​ ​ratings​ ​on​ ​their​ ​app​ ​and​ ​website.

Design​ ​Mantra

Fast Qualitative, Relevant.

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Key​ ​data​ ​and​ ​insights​ ​from​ ​primary​ ​and​ ​secondary​ ​research i.

Interview​ ​and​ ​findings To​ ​better​ ​understand​ ​how​ ​users​ ​make​ ​food​ ​decisions​ ​for​ ​online​ ​delivery,​ ​we​ ​conducted​ ​a semi-structured​ ​user​ ​interview​ ​on​ ​ten​ ​of​ ​our​ ​target​ ​users,​ ​IU​ ​students,​ ​who​ ​have​ ​had​ ​experience​ ​relying on​ ​BTownMenus​ ​or​ ​Yelp​ ​for​ ​getting​ ​food​ ​or​ ​making​ ​food​ ​decisions.​ ​Semi-structured​ ​interview techniques​ ​follow​ ​a​ ​framework​ ​rather​ ​than​ ​focusing​ ​on​ ​getting​ ​specific​ ​answers​ ​for​ ​specific​ ​questions.​ ​It allows​ ​for​ ​flexibility​ ​in​ ​responding​ ​to​ ​new​ ​questions​ ​resulting​ ​from​ ​the​ ​interviews​ ​themselves (​Macdonald​ ​&​ ​Headlam,​ ​2008)​.

Below​ ​are​ ​excerpted​ ​interview​ ​questions​ ​and​ ​feedback​ ​from​ ​the​ ​users​ ​that​ ​are​ ​particularly​ ​informative towards​ ​answering​ ​our​ ​questions.

How​ ​do​ ​you​ ​typically​ ​order​ ​food? ● Most​ ​people​ ​responded​ ​that​ ​they​ ​would​ ​go​ ​by​ ​cuisine​ ​or​ ​food​ ​category​ ​if​ ​they​ ​do​ ​not​ ​know​ ​for sure​ ​what​ ​they​ ​want. ● Others​ ​said​ ​they​ ​will​ ​order​ ​pizza​ ​and​ ​know​ ​exactly​ ​where​ ​to​ ​get​ ​it. ● Respondents​ ​suggested​ ​they​ ​would​ ​want​ ​to​ ​see​ ​pictures​ ​and​ ​food​ ​descriptions​ ​to​ ​help​ ​with​ ​food decision​ ​making. ● Respondents​ ​also​ ​said​ ​they​ ​order​ ​similar​ ​kinds​ ​of​ ​food​ ​regularly.

Number​ ​of​ ​times​ ​a​ ​user​ ​mentioned​ ​a​ ​particular​ ​motive​ ​when​ ​making​ ​food​ ​related​ ​decisions

Have​ ​you​ ​ever​ ​had​ ​trouble​ ​ordering​ ​food​ ​online?​ ​How​ ​so?

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All​ ​interviewees​ ​we​ ​talked​ ​to​ ​do​ ​not​ ​have​ ​problems​ ​in​ ​terms​ ​of​ ​placing​ ​orders​ ​online.

Two​ ​users​ ​mentioned​ ​the​ ​technical​ ​problems​ ​they​ ​experienced​ ​when​ ​ordering​ ​food​ ​online.

Sometimes​ ​they​ ​received​ ​odd​ ​error​ ​prompts​ ​with​ ​no​ ​idea​ ​what​ ​they​ ​meant.

One​ ​heavy​ ​user​ ​of​ ​online​ ​food​ ​delivery​ ​services​ ​said​ ​he​ ​once​ ​ordered​ ​food​ ​online​ ​only​ ​to​ ​find out​ ​that​ ​the​ ​restaurant​ ​was​ ​closed​ ​and​ ​not​ ​making​ ​the​ ​food​ ​he​ ​wanted.​ ​He​ ​didn’t​ ​find​ ​this​ ​out until​ ​he​ ​had​ ​already​ ​paid​ ​and​ ​waited​ ​for​ ​his​ ​food.​ ​He​ ​called​ ​the​ ​online​ ​food​ ​ordering​ ​app​ ​and they​ ​told​ ​him​ ​of​ ​the​ ​problem.

Respondents​ ​had​ ​problems​ ​with​ ​some​ ​coupon​ ​codes​ ​listed​ ​on​ ​the​ ​site​ ​being​ ​invalid​ ​when checking​ ​out.

How​ ​do​ ​you​ ​use​ ​Yelp​ ​to​ ​make​ ​food​ ​decisions? ● They​ ​only​ ​check​ ​reviews​ ​when​ ​they​ ​are​ ​unfamiliar​ ​with​ ​a​ ​restaurant. ● They​ ​would​ ​use​ ​the​ ​filters​ ​on​ ​yelp​ ​to​ ​customize​ ​their​ ​needs.​ ​What​ ​is​ ​usually​ ​done​ ​at​ ​first​ ​when​ ​in a​ ​new​ ​place​ ​is​ ​to​ ​do​ ​the​ ​top​ ​10​ ​rated​ ​restaurants,​ ​as​ ​they​ ​want​ ​to​ ​explore​ ​options​ ​and​ ​they​ ​do not​ ​know​ ​what​ ​to​ ​get. ● They​ ​do​ ​not​ ​mind​ ​going​ ​through​ ​reviews​ ​a​ ​bit,​ ​as​ ​this​ ​information​ ​greatly​ ​maximizes​ ​the possibility​ ​that​ ​they​ ​will​ ​get​ ​what​ ​they​ ​like,​ ​even​ ​in​ ​a​ ​new​ ​place. ii.

Secondary​ ​Research​ ​-​ ​how​ ​to​ ​best​ ​utilize​ ​Conversational​ ​UI​ ​to​ ​address​ ​the​ ​problem According​ ​to​ ​Tomaz​ ​Stolfa,​ ​the​ ​co-founder​ ​at​ ​Layer,​ ​the​ ​history​ ​of​ ​Conversational​ ​UI​ ​can​ ​be​ ​dated​ ​back to​ ​1986​ ​when​ ​people​ ​would​ ​interact​ ​with​ ​computers​ ​by​ ​inputting​ ​text​ ​commands.​ ​Today,​ ​the​ ​definition of​ ​Conversational​ ​UI​ ​has​ ​gone​ ​far​ ​beyond​ ​text​ ​interaction.​ ​In​ ​2016,​ ​there​ ​has​ ​been​ ​an​ ​explosive​ ​use​ ​of Conversational​ ​UI​ ​in​ ​commerce.​ ​Many​ ​of​ ​them​ ​adopted​ ​various​ ​forms​ ​of​ ​media​ ​in​ ​interactions,​ ​showing more​ ​possibilities​ ​other​ ​than​ ​pure​ ​text​ ​interaction​ ​that​ ​can​ ​happen​ ​in​ ​Conversational​ ​UI​ ​design.

Here​ ​are​ ​the​ ​three​ ​takeaways​ ​during​ ​our​ ​secondary​ ​research​ ​about​ ​Conversational​ ​Commerce. ● Messenger​ ​service​ ​providers​ ​attempt​ ​to​ ​be​ ​the​ ​core​ ​of​ ​e-commerce​ ​and​ ​customer​ ​service (Abdel-Rahman,​ ​2016).​ ​Major​ ​chat​ ​apps​ ​including​ ​Facebook​ ​messenger,​ ​Slack,​ ​Whatsapp, 4

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WeChat​ ​have​ ​taken​ ​measures​ ​to​ ​integrate​ ​mini-chatbots​ ​to​ ​provide​ ​users​ ​with​ ​services​ ​such​ ​as Uber-​ing​ ​a​ ​car​ ​without​ ​switching​ ​to​ ​third-party​ ​platforms.

Dynamic​ ​cards​ ​is​ ​considered​ ​to​ ​be​ ​“the​ ​best​ ​design​ ​pattern​ ​for​ ​mobile​ ​devices”​ ​(Adams,​ ​2016).

Google,​ ​Apple​ ​iOS​ ​10,​ ​and​ ​Twitter​ ​all​ ​moved​ ​to​ ​cards​ ​to​ ​personalize​ ​information​ ​presented​ ​to users.​ ​Many​ ​Conversational​ ​UI​ ​services​ ​also​ ​provide​ ​integrated​ ​cards​ ​as​ ​they​ ​can​ ​give​ ​bursts​ ​of information​ ​and​ ​are​ ​easy​ ​to​ ​manipulate​ ​for​ ​users.​ ​Tomaz(2016),​ ​in​ ​his​ ​article,​ ​explicitly expressed​ ​that​ ​hybrid​ ​interfaces​ ​in​ ​Conversational​ ​UI​ ​design​ ​will​ ​be​ ​the​ ​future,​ ​as​ ​it​ ​provides convenience,​ ​clarity​ ​and​ ​more​ ​than​ ​one​ ​option​ ​for​ ​users.

Creating​ ​a​ ​good​ ​flow​ ​of​ ​conversation​ ​relies​ ​heavily​ ​on​ ​building​ ​a​ ​good​ ​information​ ​architecture and​ ​semantic​ ​meaning​ ​as​ ​opposed​ ​to​ ​the​ ​traditional​ ​principles​ ​of​ ​GUI​ ​design.​ ​Essential​ ​things​ ​in designing​ ​a​ ​Conversational​ ​UI,​ ​include​ ​suggestive​ ​hints​ ​that​ ​set​ ​expectations​ ​for​ ​users​ ​to​ ​avoid leaving​ ​them​ ​clueless​ ​as​ ​to​ ​how​ ​to​ ​start​ ​and​ ​continue​ ​with​ ​a​ ​given​ ​task.​ ​Giving​ ​personality​ ​to​ ​the bot​ ​and​ ​setting​ ​limits​ ​to​ ​avoid​ ​impairing​ ​productivity​ ​are​ ​also​ ​important​ ​points​ ​to​ ​guide​ ​a conversational​ ​design​ ​(Mariansky,​ ​2016).

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Personas Hannah​ ​Watson

Image​ ​source:​ ​https://randomuser.me/photos @​ ​9.14.2016​ ​used​ ​here​ ​under​ ​educational​ ​fair​ ​use​ ​only Age:​ ​20 Occupation:​ ​IU​ ​student Location:​ ​Indiana Gender:​ ​Female Bio Hannah​ ​is​ ​a​ ​sophomore​ ​at​ ​IU​ ​majoring​ ​in​ ​Elementary​ ​Education.​ ​She​ ​lives​ ​in​ ​an​ ​off​ ​campus​ ​apartment with​ ​her​ ​best​ ​friend.​ ​She​ ​typically​ ​orders​ ​from​ ​BTownMenus​ ​twice​ ​a​ ​month​ ​when​ ​she​ ​gets​ ​busy.​ ​She​ ​is familiar​ ​with​ ​the​ ​area​ ​around​ ​Bloomington​ ​and​ ​she​ ​knows​ ​different​ ​restaurants​ ​she​ ​likes,​ ​but​ ​has​ ​not tried​ ​a​ ​lot​ ​of​ ​places.​ ​She​ ​is​ ​willing​ ​to​ ​explore​ ​sometimes.​ ​When​ ​ordering​ ​food​ ​online​ ​for​ ​delivery,​ ​she likes​ ​to​ ​see​ ​pictures,​ ​accurate​ ​descriptions of​ ​food,​ ​and​ ​up-to-date​ ​deals​ ​if​ ​there​ ​are​ ​any,​ ​as​ ​she​ ​is​ ​very​ ​budget​ ​conscious.

Goals - Make​ ​accurate​ ​food​ ​decisions​ ​when​ ​ordering​ ​online - Have​ u ​ p-to-date​ ​deals​ ​on​ ​food​ ​if​ ​there​ ​are​ ​any 6

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Wants​ ​to​ ​explore​ ​food​ ​choices

Frustrations - The​ ​online​ ​menus​ ​sometimes​ ​do​ ​not​ ​have​ ​detailed​ ​food​ ​descriptions - Coupon​ ​code​ ​is​ ​found​ ​to​ ​be​ ​invalid​ ​when​ ​checking​ ​out - Does​ ​not​ ​know​ ​what​ ​is​ ​good​ ​and​ ​is​ ​too​ ​lazy​ ​to​ ​read​ ​into​ ​reviews​ ​to​ ​explore

Jared​ ​Bradbury

Image​ ​source:​ ​https://randomuser.me/photos @​ ​9.14.2016​ ​used​ ​here​ ​under​ ​educational​ ​fair​ ​use​ ​only Age:​ ​18 Occupation:​ ​IU​ ​Student Location:​ ​California Gender:​ ​Male Bio:​ ​Jared​ ​is​ ​a​ ​Junior​ ​at​ ​IU​ ​studying​ ​business.​ ​He​ ​is​ ​a​ ​member​ ​of​ ​a​ ​fraternity.​ ​He​ ​is​ ​originally​ ​from​ ​an upper​ ​middle​ ​class​ ​family​ ​in​ ​California.​ ​As​ ​an​ ​out-of-state​ ​student,​ ​he​ ​does​ ​not​ ​care​ ​about​ ​his​ ​budget.

He​ ​has​ ​an​ ​Audi​ ​and​ ​likes​ ​to​ ​try​ ​out​ ​nice​ ​food​ ​with​ ​friends​ ​during​ ​the​ ​weekend.​ ​He​ ​is​ ​very​ ​picky​ ​about the​ ​food​ ​and​ ​service​ ​quality​ ​but​ ​still​ ​does​ ​not​ ​like​ ​to​ ​sift​ ​through​ ​review​ ​after​ ​review​ ​to​ ​find​ ​something​ ​he would​ ​like​ ​to​ ​eat.​ ​He​ ​orders​ ​food​ ​online​ ​very​ ​frequently,​ ​sometimes​ ​four​ ​or​ ​five​ ​times​ ​a​ ​week​ ​and​ ​almost always​ ​orders​ ​just​ ​for​ ​himself.

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Goals ·​ ​Wants​ ​good​ ​food​ ​experiences ·​ ​Be​ ​knowledgeable​ ​about​ ​the​ ​food​ ​quality​ ​in​ ​local​ ​restaurants Frustrations ·​ ​Yelp​ ​reviews​ ​take​ ​too​ ​long​ ​to​ ​navigate ·​ ​Information​ ​on​ ​food​ ​ordering​ ​websites​ ​is​ ​sometimes​ ​not​ ​up​ ​to​ ​date.

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First​ ​Prototype​ ​–​ ​Foodie

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Testing​ ​Procedures​ ​and​ ​Results In​ ​testing​ ​our​ ​first​ ​prototype,​ ​we​ ​made​ ​the​ ​decision​ ​to​ ​use​ ​a​ ​Facebook​ ​messenger​ ​bot.​ ​The​ ​users​ ​were given​ ​a​ ​prescribed​ ​scenario​ ​in​ ​which​ ​they​ ​had​ ​finished​ ​a​ ​meal,​ ​wanted​ ​to​ ​share​ ​their​ ​experience,​ ​and used​ ​the​ ​foodie​ ​bot​ ​to​ ​accomplish​ ​this​ ​goal.​ ​The​ ​scenario​ ​was​ ​as​ ​follows: Scenario You​ ​have​ ​just​ ​finished​ ​a​ ​meal​ ​at​ ​a​ ​new​ ​restaurant​ ​and​ ​found​ ​the​ ​experience​ ​deeply​ ​unsatisfying.​ ​You want​ ​to​ ​share​ ​your​ ​experience​ ​with​ ​your​ ​friend​ ​so​ ​they​ ​know​ ​how​ ​much​ ​you​ ​hated​ ​it.​ ​You​ ​had​ ​taken 10

some​ ​pictures​ ​of​ ​the​ ​food​ ​during​ ​your​ ​meal​ ​and​ ​want​ ​to​ ​send​ ​them​ ​those,​ ​but​ ​don’t​ ​want​ ​to​ ​go​ ​through the​ ​hassle​ ​of​ ​sharing​ ​each​ ​one​ ​through​ ​text.​ ​Instead,​ ​you​ ​open​ ​up​ ​the​ ​Foodie​ ​messenger​ ​bot​ ​and​ ​try the​ ​new​ ​sharing​ ​feature​ ​using​ ​that.

Procedure ● The​ ​user​ ​would​ ​first​ ​select​ ​some​ ​recently​ ​taken​ ​pictures​ ​or​ ​videos​ ​of​ ​the​ ​food​ ​they​ ​wanted​ ​to share,​ ​which​ ​were​ ​presented​ ​to​ ​the​ ​user​ ​within​ ​the​ ​messenger​ ​window. ● The​ ​user​ ​then​ ​had​ ​the​ ​option​ ​of​ ​adding​ ​a​ ​text​ ​review​ ​in​ ​addition​ ​to​ ​the​ ​photos​ ​or​ ​videos. ● The​ ​bot​ ​would​ ​present​ ​the​ ​user​ ​with​ ​a​ ​preview​ ​of​ ​their​ ​organized​ ​“media​ ​package”,​ ​which consisted​ ​of​ ​the​ ​items​ ​they​ ​selected​ ​to​ ​share,​ ​formatted​ ​with​ ​a​ ​focus​ ​towards​ ​making​ ​the​ ​items conducive​ ​to​ ​sharing​ ​with​ ​friends. ● The​ ​user​ ​would​ ​then​ ​choose​ ​who​ ​they​ ​wanted​ ​to​ ​share​ ​the​ ​“media​ ​package”​ ​with​ ​using​ ​a​ ​list​ ​of Facebook​ ​contacts,​ ​presented​ ​within​ ​the​ ​messenger​ ​window. ● The​ ​user​ ​would​ ​then​ ​be​ ​prompted​ ​to​ ​share​ ​on​ ​Yelp. ● Feedback​ ​is​ ​given​ ​to​ ​the​ ​user,​ ​informing​ ​them​ ​that​ ​the​ ​information​ ​they​ ​share​ ​will​ ​ultimately​ ​help them​ ​get​ ​more​ ​accurate​ ​recommendations​ ​themselves. ● The​ ​user​ ​would​ ​then​ ​be​ ​shown​ ​three​ ​recommendations​ ​based​ ​on​ ​the​ ​media​ ​they​ ​had​ ​just shared.​ ​The​ ​user​ ​would​ ​have​ ​the​ ​option​ ​to​ ​save​ ​any​ ​of​ ​these​ ​recommendations​ ​as​ ​a​ ​Yelp specific​ ​bookmark​ ​so​ ​they​ ​could​ ​return​ ​to​ ​them​ ​later​ ​when​ ​they​ ​were​ ​interested​ ​in​ ​ordering​ ​food.

Findings ● Users​ ​were​ ​confused​ ​as​ ​to​ ​why​ ​they​ ​would​ ​use​ ​the​ ​messenger​ ​bot​ ​to​ ​share​ ​any​ ​of​ ​this.​ ​They figured​ ​if​ ​they​ ​were​ ​already​ ​on​ ​Facebook,​ ​then​ ​they​ ​would​ ​just​ ​share​ ​through​ ​Facebook​ ​directly. ● Users​ ​typically​ ​did​ ​not​ ​seem​ ​to​ ​use​ ​the​ ​feature​ ​to​ ​add​ ​some​ ​supplementary​ ​text​ ​to​ ​the​ ​pictures or​ ​videos​ ​they​ ​were​ ​sharing.​ ​They​ ​figured​ ​if​ ​this​ ​was​ ​supposed​ ​to​ ​be​ ​a​ ​quick​ ​way​ ​to​ ​share,​ ​then they​ ​wouldn’t​ ​waste​ ​time​ ​typing​ ​additional​ ​comments,​ ​especially​ ​when​ ​sending​ ​to​ ​a​ ​close​ ​friend with​ ​whom​ ​they​ ​have​ ​frequent​ ​interactions. ● Users​ ​suggested​ ​that​ ​they​ ​may​ ​get​ ​overwhelmed​ ​by​ ​recommendations​ ​from​ ​friends,​ ​which would​ ​lead​ ​them​ ​to​ ​either​ ​a)​ ​ending​ ​usage​ ​of​ ​the​ ​bot​ ​and​ ​removing​ ​it​ ​from​ ​their​ ​chat​ ​list​ ​or​ ​b) turning​ ​off​ ​notifications​ ​for​ ​that​ ​messenger​ ​bot​ ​specifically.​ ​If​ ​they​ ​no​ ​longer​ ​received 11

notifications,​ ​users​ ​suggested​ ​they​ ​would​ ​be​ ​unlikely​ ​to​ ​return​ ​to​ ​the​ ​bot​ ​to​ ​check​ ​for​ ​recent recommendations.

Second​ ​Prototype​ ​-​ ​James​ ​the​ ​Recommendation​ ​Chat​ ​Bot

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Because​ ​of​ ​the​ ​user​ ​tests​ ​and​ ​feedback​ ​from​ ​our​ ​first​ ​prototype,​ ​specifically​ ​the​ ​confusion​ ​with​ ​the social/sharing​ ​feature,​ ​we​ ​decided​ ​to​ ​pivot​ ​to​ ​another​ ​design​ ​that​ ​recommends​ ​users​ ​restaraunts​ ​based off​ ​their​ ​inputs​ ​in​ ​the​ ​chat​ ​app.

Additionally,​ ​we​ ​realized​ ​the​ ​service​ ​was​ ​not​ ​taking​ ​advantage​ ​of​ ​Yelp’s​ ​data​ ​in​ ​order​ ​to​ ​provide​ ​more relevant​ ​recommendations​ ​to​ ​the​ ​user.​ ​We​ ​also​ ​found​ ​the​ ​use​ ​of​ ​a​ ​hi-fidelity​ ​prototype​ ​to​ ​stifle​ ​the feedback​ ​users​ ​were​ ​willing​ ​to​ ​offer.

User​ ​Testing​​ ​Scenario You’re​ ​on​ ​the​ ​bus​ ​to​ ​your​ ​home​ ​and​ ​want​ ​to​ ​order​ ​food​ ​for​ ​dinner.​ ​You​ ​remember​ ​someone​ ​mentioning ‘James​ ​Bot’​ ​restaurant​ ​recommender​ ​bot​ ​that​ ​works​ ​with​ ​BTownMenus.​ ​It​ ​runs​ ​on​ ​Facebook Messenger​ ​so​ ​there’s​ ​no​ ​installation.​ ​You​ ​try​ ​it.​ ​Assume​ ​that​ ​it​ ​knows​ ​your​ ​order​ ​history​ ​from BTownMenus​ ​and​ ​your​ ​favourites​ ​from​ ​Yelp.

Procedure ● The​ ​bot​ ​introduces​ ​itself​ ​and​ ​asks​ ​if​ ​the​ ​user​ ​is​ ​on​ ​BTownMenus. ● User​ ​selects​ ​certain​ ​food​ ​categories​ ​and​ ​enters​ ​any​ ​dietary​ ​recommendations. ● The​ ​bot​ ​prompts​ ​that​ ​the​ ​user​ ​can​ ​type​ ​‘Suggest’​ ​to​ ​receive​ ​a​ ​recommendation​ ​which​ ​is​ ​also presented​ ​as​ ​a​ ​direct​ ​button​ ​at​ ​that​ ​point​ ​in​ ​the​ ​conversation. ● User​ ​is​ ​presented​ ​one​ ​restaurant​ ​recommendation​ ​with​ ​photographs​ ​and​ ​details​ ​of​ ​the establishment​ ​along​ ​with​ ​a​ ​review​ ​from​ ​the​ ​user’s​ ​friend​ ​if​ ​they​ ​have​ ​reviewed​ ​the​ ​place. ● User​ ​can​ ​then​ ​select​ ​from​ ​‘I​ ​like​ ​it’​ ​or​ ​‘Show​ ​me​ ​more’ ● If​ ​the​ ​user​ ​selects​ ​“Show​ ​me​ ​more”​ ​the​ ​bot​ ​presents​ ​another​ ​restaurant​ ​recommendation. ● If​ ​the​ ​user​ ​selects​ ​“I​ ​like​ ​it”​ ​then​ ​the​ ​bot​ ​notifies​ ​the​ ​user​ ​that​ ​it​ ​has​ ​learned​ ​more​ ​about​ ​them.

Findings ● Users​ ​found​ ​single​ ​option​ ​responses​ ​for​ ​‘Alright’/’Sure’/’Cool’​ ​unnecessary. ● Users​ ​wanted​ ​to​ ​know​ ​what​ ​happened​ ​if​ ​they​ ​didn’t​ ​like​ ​the​ ​restaurant​ ​recommendations:​ ​There was​ ​no​ ​exit​ ​point. ● Users​ ​did​ ​not​ ​find​ ​one​ ​recommendation​ ​enough.​ ​They​ ​expected​ ​more​ ​options​ ​to​ ​choose​ ​from.

After​ ​every​ ​two​ ​recommendations,​ ​the​ ​bot​ ​asked​ ​for​ ​more​ ​input​ ​keywords​ ​that​ ​fetched​ ​two​ ​more 16

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results​ ​but​ ​displayed​ ​just​ ​one.​ ​The​ ​second​ ​result​ ​was​ ​displayed​ ​after​ ​the​ ​user​ ​clicked​ ​on​ ​‘Show me​ ​more’.

Users​ ​found​ ​it​ ​amusing​ ​that​ ​the​ ​bot​ ​was​ ​named​ ​'James​ ​Bot’​ ​but​ ​expected​ ​it​ ​to​ ​talk​ ​like​ ​James Bond​ ​or​ ​a​ ​British​ ​spy​ ​in​ ​general.

Users​ ​had​ ​a​ ​hard​ ​time​ ​understanding​ ​that​ ​the​ ​more​ ​information​ ​they​ ​gave​ ​to​ ​the​ ​bot​ ​the​ ​better the​ ​bots​ ​recommendations​ ​would​ ​be.

Users​ ​were​ ​not​ ​sure​ ​how​ ​their​ ​actions​ ​influenced​ ​the​ ​bot’s​ ​decision​ ​making​ ​and​ ​how​ ​those​ ​lead to​ ​improved​ ​recommendations.

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Final​ ​Design​ ​-​ ​Rex​ ​the​ ​Recommendation​ ​Bot Learning​ ​from​ ​the​ ​user​ ​feedback​ ​received​ ​during​ ​usability​ ​tests​ ​of​ ​the​ ​second​ ​prototype,​ ​we​ ​designed the​ ​conversation​ ​to​ ​be​ ​quicker​ ​and​ ​more​ ​relevant​ ​to​ ​the​ ​user’s​ ​goal​ ​(getting​ ​a​ ​recommendation).

The​ ​scenario​ ​is​ ​the​ ​same​ ​as​ ​the​ ​second​ ​prototype​ ​where​ ​the​ ​user​ ​is​ ​conversing​ ​with​ ​the​ ​bot​ ​for​ ​the​ ​first time.

Key​ ​changes​ ​in​ ​this​ ​iteration ● Changed​ ​bot​ ​name​ ​to​ ​Rex​ ​(short​ ​for​ ​RECommendationS),​ ​since​ ​conversation​ ​style​ ​did​ ​not match​ ​with​ ​a​ ​British​ ​spy. ● Unnecessary​ ​single​ ​choice​ ​responses​ ​like,​ ​“Cool”​ ​and​ ​“Alright”​ ​were​ ​omitted. ● Three​ ​restaurant​ ​suggestions​ ​in​ ​a​ ​carousel​ ​in​ ​one​ ​round. ● Prompt​ ​the​ ​user​ ​that​ ​an​ ​action​ ​performed​ ​on​ ​the​ ​suggested​ ​restaurant​ ​will​ ​train​ ​the​ ​bot​ ​and​ ​in turn​ ​help​ ​them​ ​get​ ​better​ ​recommendations. ● Removed​ ​‘I​ ​like​ ​it’​ ​button​ ​that​ ​comes​ ​with​ ​each​ ​suggested​ ​restaurant​ ​because​ ​it​ ​is​ ​too ambiguous. ● Included​ ​more​ ​direct​ ​key​ ​call-to-action​ ​buttons​ ​such​ ​as​ ​“Place​ ​Order” ● If​ ​the​ ​user​ ​doesn’t​ ​like​ ​any​ ​of​ ​the​ ​three​ ​restaurant​ ​suggestions,​ ​then​ ​another​ ​set​ ​of​ ​three restaurant​ ​suggestions​ ​is​ ​presented​ ​in​ ​a​ ​carousel. ● If​ ​the​ ​user​ ​doesn’t​ ​like​ ​any​ ​restaurant​ ​from​ ​the​ ​second​ ​set,​ ​they​ ​are​ ​provided​ ​a​ ​link​ ​to​ ​Yelp.com for​ ​further​ ​browsing.

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Design​ ​Rationale The​ ​goal​ ​of​ ​this​ ​final​ ​prototype​ ​was​ ​to​ ​provide​ ​users​ ​with​ ​quick​ ​and​ ​easy​ ​access​ ​to​ ​relevant​ ​food recommendations​ ​without​ ​the​ ​need​ ​to​ ​install​ ​a​ ​new​ ​app​ ​or​ ​use​ ​multiple​ ​different​ ​services​ ​or​ ​websites.

Firstly,​ ​to​ ​quicken​ ​the​ ​interaction​ ​between​ ​user​ ​and​ ​service,​ ​we​ ​decided​ ​to​ ​use​ ​a​ ​platform​ ​that​ ​is​ ​nearly ubiquitous​ ​on​ ​every​ ​smartphone;​ ​Facebook​ ​Messenger.​ ​This​ ​platform​ ​has​ ​the​ ​obvious​ ​benefit​ ​of​ ​being more​ ​mobile​ ​friendly​ ​than​ ​navigating​ ​Yelp’s​ ​mobile​ ​website​ ​from​ ​a​ ​smartphone,​ ​but​ ​also​ ​afforded​ ​us​ ​the ability​ ​to​ ​offer​ ​photo​ ​carousels​ ​and​ ​other​ ​“mini-app”​ ​experiences​ ​within​ ​our​ ​conversation.​ ​We​ ​used​ ​this functionality​ ​to​ ​format​ ​our​ ​“media​ ​package”​ ​that​ ​organizes​ ​the​ ​user’s​ ​submitted​ ​information/media​ ​into​ ​a consolidated,​ ​easy-to-understand​ ​package.

Initial​ ​research​ ​from​ ​users​ ​suggested​ ​that​ ​the​ ​vast​ ​majority​ ​start​ ​their​ ​online​ ​food​ ​ordering​ ​experience with​ ​a​ ​category​ ​in​ ​mind.​ ​Because​ ​they​ ​have​ ​an​ ​idea​ ​of​ ​what​ ​they​ ​want,​ ​many​ ​claimed​ ​they​ ​were unlikely​ ​to​ ​look​ ​for​ ​reviews.​ ​In​ ​an​ ​effort​ ​to​ ​make​ ​this​ ​service​ ​and​ ​the​ ​resulting​ ​recommendations relevant​ ​to​ ​the​ ​user,​ ​we​ ​decided​ ​to​ ​start​ ​with​ ​where​ ​the​ ​user​ ​starts;​ ​the​ ​food​ ​category.

From​ ​our​ ​usability​ ​tests,​ ​it​ ​was​ ​clear​ ​users​ ​appreciated​ ​the​ ​“media​ ​package”​ ​specifically​ ​and​ ​cited​ ​it​ ​as a​ ​main​ ​reason​ ​for​ ​using​ ​the​ ​service.​ ​Based​ ​on​ ​the​ ​feedback​ ​from​ ​users​ ​that​ ​asking​ ​for​ ​suggestions multiple​ ​times​ ​was​ ​cumbersome,​ ​we​ ​decided​ ​to​ ​show​ ​users​ ​three​ ​recommendations​ ​at​ ​a​ ​time​ ​within this​ ​“media​ ​package”,​ ​to​ ​align​ ​with​ ​the​ ​three​ ​food​ ​categories​ ​they​ ​chose​ ​near​ ​the​ ​beginning​ ​of​ ​the interaction.​ ​We​ ​believe​ ​this​ ​service​ ​offers​ ​a​ ​quicker​ ​way​ ​to​ ​get​ ​relevant​ ​food​ ​recommendations​ ​at​ ​the moment​ ​the​ ​user​ ​wants​ ​them,​ ​thus​ ​providing​ ​an​ ​alternative​ ​to​ ​the​ ​tasking​ ​action​ ​of​ ​sifting​ ​through review​ ​after​ ​review​ ​on​ ​the​ ​Yelp​ ​website.

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Sources Adams,​ ​Paul.​ ​(2016).​ ​Why​ ​cards​ ​are​ ​the​ ​future​ ​of​ ​the​ ​web​ ​-​ ​Inside​ ​Intercom.​ ​Retrieved​ ​September​ ​14, 2016,​ ​from​ ​https://blog.intercom.com/why-cards-are-the-future-of-the-web/ El-Zohairy,​ ​Abdel-Rahman.​ ​(2016).​ ​Messenger​ ​Platform​ ​could​ ​be​ ​more​ ​powerful​ ​than​ ​you​ ​think.

Retrieved​ ​September​ ​14,​ ​2016,​ ​from https://medium.com/@azohairy/messenger-platform-could-be-more-powerful-than-you-think-4ecc08fb5 a5a#.wg2v714xn Macdonald,​ ​S.,​ ​&​ ​Headlam,​ ​N.​ ​(2008).​ ​Research​ ​methods​ ​handbook:​ ​Introductory​ ​guide​ ​to​ ​research methods​ ​for​ ​social​ ​research​.​ ​Manchester:​ ​Centre​ ​for​ ​Local​ ​Economic​ ​Strategies.

Mariansky,​ ​Matty.​ ​(2016).​ ​Cheating​ ​on​ ​the​ ​Turing​ ​test.​ ​Retrieved​ ​September​ ​14,​ ​2016,​ ​from https://medium.com/building-the-robot-assistant/cheating-on-the-turing-test-bc23a36db10#.j5b2u6vzs Messina,​ ​Chris.​ ​(2016).​ ​2016​ ​will​ ​be​ ​the​ ​year​ ​of​ ​conversational​ ​commerce.​ ​Retrieved​ ​September​ ​14, 2016,​ ​from https://medium.com/chris-messina/2016-will-be-the-year-of-conversational-commerce-1586e85e3991#. uht88wrto Stolfa,​ ​Tomaz.​ ​(2016).​ ​The​ ​Future​ ​of​ ​Conversational​ ​UI​ ​Belongs​ ​to​ ​Hybrid​ ​Interfaces.​ ​Retrieved September​ ​14,​ ​2016,​ ​from https://medium.com/the-layer/the-future-of-conversational-ui-belongs-to-hybrid-interfaces-8a228de0bdb 5#.f6beme6gv

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