Sahil Mhatre1 , Raghvendra Lola2 , Tejas Pathak3 , Harshal Dhake4, Prof. Deepti Lawand5 1,2,3,4UG Student, Dept. of Computer Engineering, Pillai College of Engineering, New Panvel, India 5Assistant Professor, Dept. of Computer Engineering, Pillai College of Engineering, New Panvel, India ***
1.INTRODUCTION
International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p ISSN: 2395 0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page1454
Foodmate is a social media application which enables to share,exchangeanddiscovertheinformation.Inourcaseit isthereviews/postswithpicturesoffood.Thesepostsare discoverableto the enduserina personalized waywhere each and every post is placed in the feed according to the geo spatialdistancebetweentheuserandtherestaurantto
Abstract Foodmate is a social media platform built for the community of foodies. It overcomes the drawbacks of food delivery platforms which is that they majorly focus on selling products and quality of food is neglected and solely depends on what the vendor sells. Foodmate provides a platform to the Food Vloggers to showcase their talent and content over the platform. This also helps the foodies to keep a check on what's yummy around themselves by watching the reviews posted by various food vloggers. This also provides the restaurant with a better tool to have insights of what foodies are interested in their place and various insights regarding them. The images uploaded by the food vloggers are tagged with the help of ML & Image based tagging, this will provide a better personalized feed for the user. The ML based recommendation will use content based recommendation implemented using cosine similarity between user’s previously liked posts to determine the user with similar likes and fetching similar posts with the help of Word Vectorization and TF IDF. The Image based tagging will be done using CNN and Deep Learning. Graph based structure will be designed to efficiently generate post insights data. A revenue model will be added where restaurants can do promotion and some part of the revenue will be contributed to an NGO for their social work
Now a daysorderingfoodonlinehasbecomeamajorpartof dailylifeforthe majorityof population.Millionsofpeople acrosstheglobeorderfoodonline,butinmostofthecases,it has been observed that the food items that the customers receiveinhandarewaydifferentthantheitemasperceived bythecustomerbasedonthethumbnailandadvertisement imagesshown in food delivery applications. Herethecustomerortheend userisinneedofactualimagesoftheitems they are going to order along with some genuine reviews abouttherestaurantorthefoodtheydecidedtoorder.Currently,thereisnosuchplatformavailableinthemarketthat givesthecustomersuchaclearview.Also,noseparateplatformexistsforfoodvloggerstoshowcasetheircontent.
2. LITERATURE SURVEY
A. TF IDF & Cosine Similarity: AuthorOneetal.[1]makes useofTF IDF(TermFrequency InverseDocumentFrequency)forimplementingContentBasedRecommendationsystem.Thistechniqueisameasureoforiginalityofawordby comparingthenumberoftimesawordappearsinagiven documenttothenumberofdocumentsinwhichthatparticular word appears. It is used to recommend content to the user based on the similarity with the content previously viewedbytheuser.Thesimilarityscorebetweenthecontentsiscalculatedusingcosinesimilaritywhichisameasure ofsimilaritybetweentwovectors.
Foodmate: A Social Networking Web Application for Foodies
whichthepostsbelongtoandalsotheuserpreferredcuisinesaregivenapriorityinthepostsfeedtakingintoconsideration their likes and dislikes. Foodmate focuses on providingaplatformforvloggerswheretheircontentiscateredtoonlythosewhoareinterestedinthenicheofthat particular post. We focus on making foodmate usable for everyone,henceFoodmatewillbedesignedtobeusablefor morethan90%ofdeviceswithdifferentscreensizes.Inadditiontothis,theplatformconsistsofaContentBasedRecommendationfortheuserstoprovidecontentwhichistailored based on their history of interest. The Vloggers can viewinsightfuldataoftheuserswhohaveinteractedwith theircontentwhichhelpstheminproducingtheircontentin anefficientway.Therestaurantownerswillbeabletohire vloggersfromtheplatformtopromotetheirfoodandservicesthroughsponsoredpostsandpaythevloggerbasedon theCPC(CostPerClick)rate.Afixedpercentageofthisrevenue will be donated towards NGO funds for helping their socialneedsanddeeds.
Key Words: Social Media Platform, Food, Recommendation System, Image Recognition, Web Application
B. Tags Based Recommendation: Thistechniqueisusedby AuthorTwoetal.[3]andprovestobeabetterapproachfor recommendationwhentheuserisnewtotheplatform.Itisa ContentBasedRecommendationtechnique.Itmakesuseof tags given to the food posts at the time of uploading the posts.These tagsand user preferred tags(selected by the user)areusedtorecommendpostsandrenderthecustomizedfeedfortheuser.
C. Geopositioning: Itisoneofthetechniquesexplainedby Author[4]elat.whichisusedtofetchdetailsaboutauser's location.Inthistechnique,user’slocationisfetchedbyusing theGlobalPositioningSystem(GPS)installedinuser’sdevice.WhileitachievesahighaccuracyindeviceshavingGPS,
Performancewith largedataset Fast Slow
ThediagrambelowdescribestheArchitectureofFoodmate involvingthevarioususersofthesystem.TheAppflowfor eachandeveryuserisdescribedfurtherinthissection.
E. Convolutional Neural Network (CNN): ItisadeeplearningtechniqueusedbytheAuthor[5]elat.torecognizethe foodimages.Afterrecognizingtheimage,itwillbedynamicallytaggedwhichwillbeusedlaterforrecommendation.
TheoverviewofcomparisonofdifferentparametersaregiveninTable2
Parameters Geopositioning Geocoding
its accuracy falls down drastically working with devices withoutGPS(Eg:Laptop,Desktop,etc)
Accuracy in deviceswithoutGPS Low High
International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p ISSN: 2395 0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page1455
Efficiency High Low
3.1 Architecture Overview
Fig. 3.1 Proposed system architecture
3. PROPOSED WORK
A. For Vloggers: Thebelowfigurerepresentsthepartsofthe architecture which works for the vloggers of Foodmate. Foodmatestoresandprocessesthepostsofthevloggersina wayitisshowntoonlytheaudience/foodieswhoareinterestedinthenicheofthatcontent.Followingthefigure,isthe detailedexplanationofeachblockintheaboverepresented architecture.
Parameter TF IDF & Cosine Similarity Tags Based Recommendation
TheFoodmatehasbeendesignedtoprovideaplatformfor theusertohaveaglanceatthefoodavailableinhis/hersurroundings.Thecontentpostedonthefoodmateplatformis providedbythelocalvloggerswhichisbrowsedbytheusers of foodmate. Foodmate has its own algorithm to serve its userswithdifferentfeedslistspersonalizedaccordingtothe geo spatialdistancebetweentheuserandtherestaurantto whichthepostsbelong.Oncetheplatformisfamiliarwith theuser’sinterests, they areserved with onlythose posts whicharemostsimilartotheirowninterests.Thedifferent modulesofFoodmatearediscussedbelow.
D. Geocoding: ItisoneofthetechniquesexplainedbyAuthor [4] el at. which is used to fetch details about a user's location.Itisdividedinto2types:ForwardGeocodingand Reverse Geocoding. Forward Geocoding is a technique of convertinganaddressintocoordinates(latitudeandlongitude)whereasinReverseGeocoding,coordinatesareconverted into a textual address. Foodmate uses forward geocodingtechniquetogetcoordinatesoftheaddressentered bytheuseranddisplaytherestaurantsandpostsofrestaurantsinthatlocation.
2.1 Comparison of techniques in Literature Survey
Accuracy in deviceswithGPS High High
Table -2: SummaryofRecommendationtechniques
Table -1: Summaryofliteraturesurvey
Requirementofa dataset of addresses Notrequired Required
Accuracy when a userisnew Low High
TheoverviewofthetwomajortechniquesexplainedbyAuthor[2]elat.tofetchlocationisgiveninthefollowingtable.
Thewholearchitectureisbeendividedintothreeseparate partsforthethreedifferent userroles Users,Vloggers & Restaurant.
Fig. 3.2 Vlogger Architecture of Foodmate
● Add Restaurant & Cuisine Details: The Vlogger addstherestaurantdetailswhichcanbeusedbytheuserto furtherorderfromthesamerestaurantandalsothevlogger addsthetagswhichdescribesthetypesofcuisinestheparticularfoodbelongsto.Thesetagsarelaterutilizedbythe systemforpersonalizingtheuserfeed.
● Request Feed:Heretheregisteredusersarelogged inthroughtheiraccountsandwhileregisteringonFoodmate itrequirestheUsertoprovideUser’sCity&Locality.When the user is new to the platform and has interacted with enoughpoststhentheuserisservedwithcontentsimilarto itspreviouslylikedpostsusingCBRorelseiftheuserisnew totheplatformhasn’tinteractedwithenoughposts,thenthe followingblocksareexecuted.
● Sort based on location: Here the posts with the samenumberofmatchedtagsarefurthersortedbasedon restaurantsbeingthenearesttothefarthestfromtheuser.
● Generate Zomato Link:Iftherestaurantispresent onZomatothentheZomatoProfileLinkisalsoGenerated andsavedwiththepostwhichcanbeaccessedbytheuser viewingtheposttoenablethemaswelltoorderfromthat specificrestaurant.
Fig. 3.3 User Architecture of Foodmate
International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p ISSN: 2395 0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page1456
● Server & Post Index:Theserverwheneverreceives therequestsforfeedstheserverusesthefetcheduserdetails fromthepreviousblockandlooksintotheindexforrelevant postsandfetchesonlythosepostswhicharelocatedwithin 10kmsrangefromtheuserandonlythosepostspostedin thelast24hours.
● Create Posts & Add Pictures and Caption:Here the vlogger uploads the pictures of the food/places they haveexperiencedrecently.Theyalsoaddacaptiontothe postwhichdescribesthefoodinthepictureand/orabout therestauranttowhichthepostbelongs.
C. For Restaurants: TheabovefigurerepresentsthearchitecturefortheRestaurants.restaurantsareprovidedwith the feature to promote their food and services through Foodmatebyhiringvloggersontheplatform.
● Display the final personalized list:Thisstepinvolvesfetchingthepostsdetailsandbuildingtheactualfeed listwithpostpicturesanddetailsalongwithpostcomments. Thisfeedlististhefinalfeedrenderedinfrontoftheuser.
B. For Users/Foodies: ThebelowfigurerepresentsthearchitectureoftheproposedsystemforUsersofthefoodmate. Theeachandeveryblockofthebelowdiagramisexplained further.
● Process Posts Feed using Foodmate Ranking Algorithm:TheFoodmate’sRankingAlgorithmProcessesthe feedlistsinsuchawaythatonlythepostswhicharecloser and are latest in time are visible. The FRA provides users withalistoffeedpostswhichareclosertotheuserandalso latestintime
● Fetch Restaurant Location:OncetheRestaurant details are added the location coordinates are generated withthehelpofZomatoAPI.TheRestaurantcoordinates canbegeneratedusingZomatoAPIonlyiftherestaurantis availableonZomatoorelseTheRestaurantcoordinatesare generatedusingGoogleMapsAPI.
● Upload:ThepostisthenfinallystoredintheDatabase and simultaneously it is added into the Posts Index whichwillbeelaboratedinthefurthertopics.Atthesame timethepostisalsoconvertedintoastringwhichcontains thepostdetailssuchas,postownerdetails,postrestaurant details,postcuisinedetails,postcaption&posttags.These stringsarestoredseparatelyontheservertolaterutilize forWordVectorization&TF IDF.
4.1.2 Revenue Model for Vloggers
Fig. 4.1 Implemented Formula for Cosine Similarity
The restaurants are provided with an opportunity to promote their food and services over the Platform by hiring vloggers for creation of Sponsored content. A Sponsored contentisaPostwheretheVloggerdescribesabouttheRestaurantand/orit’sfoodand/orit’sservicesandthispostwill bepromotedall overtheplatformandtheVloggerwill be paidbytheRestaurantbasedontheInteractionshappening on their Posts on the basis of CPC (Cost Per Click) rate. A fixedpercentofthisamountwillbedonatedtowardsNGOs whichtheycanutilizefortheirsocialservices.
● Promote Post:TheSponsoredpostisthenpromoted by Foodmate all over the platform and served to only thosewhoactuallymightbeinterestedinseeingtheposts.
● Donation towards NGOs:Fromtherevenuegenerated for the vloggers the fixed percentage is donated towardsNGOfundswhichcanbeleveragedbyNGOstoutilize itfortheirsocialworks.
4. IMPLEMENTATION
The whole system of Foodmate is being designed and implementedusingthefollowingmodules.Thesemoduleshelp Foodmatetoachieveitsobjectivesandprovideasolutionto theproblemstatement.TheFoodmateisdividedintofollowingmodules.
4.1 Modules
1. Pre Processing:Inthisstepthepostsuploadedby the vlogger are converted into a string. This string is obtainedbyconcatenatingthepostcaption,postownerdetails, restaurantdetailsandtags.
TheimplementationofFoodmateisdiscussedinthefollowingsection.
2. Word Vectorization & TF-IDF:Thestringconverted post details are processed through word vectorization andthepostisconvertedintoavectorofwords.Thisvector is then processed through TF IDF for calculating the importanceofeachwordinthepoststringwithrespecttothe wholecorpus,i.e,alltheotherposts.
4.1.1 Content Based Recommendation
4. Finalizing Feed List: once the similar posts are fetchedthetoppostswithhighestsimilarityarefetchedand processedasalistoffeedpostsandservedtotheuser
TheContentBasedRecommendation(CBR)isusedforthe predictionofpoststhatmightinteresttheuser.Thewhole processisdividedintoafewstepsasfollows:
3. Similar content using Cosine Similarity:wheneverafeedisrequestedtheimportantwordsfromtheuser’s previouslylikedpostsarefetched.Thenthecontentsimilar totheuser’spreviouslylikedpostsarefetchedwheresimilarity is determined by the number of common important wordsbetweenthepostsandhigherthenumberofcommon importantwordshigheristhesimilaritybetweenthecontent.Thefollowingexpressionmathematicallyexplainsthe workingoftheCosineSimilarityusedinourproject.
● Hire Vloggers:Therestaurantscanhirevloggerson Foodmateforcreatingspecialpostscoveringtherestaurant food&services.Thisisdonecollaborativelybythevlogger andtherestauranttomakesureeachandeveryaspectofthe restaurantiscovered.Thevloggercreatesandproducesthe contentasapostontheplatform.
Post Insights:Therestaurantaswellasthevloggercanaccesstheinsightoftheirpromotedpostssothattheycanhave abetterideaofthekindofaudiencetheyareserving.This insight contains details of the users who have interacted withtheirpostsandthedetailsareclassifiedbasedonuser’s age, gender, locality, and time when they have interacted withthepost
4.1.3 Posts/Restaurants Insights
The various blocks of the whole architecture can be explainedasfollows:
Thepostsinsightsaregenerallytheinformationregarding theuserswhohaveinteractedwithaparticularpost.These detailsoftheusersarefurthermoreclassifiedonthebasisof fewmetrics,i.e.,gender,age,timeandlocationoftheusers
● Revenue based on User Interaction:Thepromotedpostiswhenservedtotheusertheinterestedoneslike thepostandbasedonthatinteractionthevloggerispaidby therestaurantonthebasisofapredecidedCPCrate.
International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p ISSN: 2395 0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page1457 Fig. 3.4 Restaurant Architecture of Foodmate
Heretheindexislookedupforpostslistswhichareinthe 10kmsrangefromtheuserandonlythosepostswhichhave beenpostedlessthan24hoursago.
FollowingisanexampleusingdifferentPhasesofFRA:
1. Fetching:
Themainaimbehindbuildingthisindexistominimizethe numberofrecordssearchedinthetablebynarrowingdown thecolumnsandfilteringoutwithconditions.sothatafter theprocessingisdoneonlythoserecordsarefetchedfrom thedatabasewhicharerelevanttotheuser’sfeed.Thisreducesthememoryconsumptionandincreasesthespeedof fetchingthepostsfromthedatabase.
Inthis phase thedata fetched fromthe index, needsto be processed in a 2 step operation. These operations are requiredtoprocessandrearrangethelistofpostsinapersonalizedmanner.Thestepsarelistedasfollows
2. Processing:
Fig. 4.4 Index Structure
Here,from the postsfetchedfromthe previous phase,the posts which are having similar tags are partially sorted basedonthegeospatialdistancebetweentheuserandpost restaurant and for posts with similar tags and geo spatial distance between user and restaurant is less than 0.3kms (300meters)aresortedbasedontimewhenthepostwas postedintheascendingorder.
Fig. 4.5 Queue fetched from index
Step 1: Sort Posts Based on Geo-Spatial Distance:Herewe canobservethatinthequeuefetchedfromtheindexthere are a few posts with the same number of match count. In such cases, the list is partially sorted based on the geo spatial distance between the user and the restaurant to whichthepostbelongs.Consideringtheoutputofthefetchingphaseasaninputtothisstep:
International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p ISSN: 2395 0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page1458
Inthisphasethepostsarefetchedfromtheindex.Indexisa specialplacewherewestorethemetadataoftheposts.The fetchingisdoneinsuchawaythatonlythepostswhichare published before 24 hours in time and are located in the 10kmsrangeoftheuser.Also,thecontentwithsimilarnumberoftagsisgroupedtogetherkeepingthepostswithhigher numberofcommontagsatthetopofthelist.
whohaveinteractedwiththeirposts.Thisgivesthemabetterunderstandingofthekindofaudiencetheyareserving andcontributewithbettercontenttowardstheiraudience. Restaurantscanleveragethisfeaturetoexaminethereachof thesponsoredpostsandhaveabetterlookofeachandevery pennyspentontheinteractionoftheposts.
4.1.4 Foodmate Ranking Algorithm (FRA)
Theindexisusedtostorethemetadataofallthepoststhat arepostedandstoredinthedatabase.Whilethepostsare addedintothedatabasesimultaneously,somemetadataof thepostsareaddedintotheindex.
● Processing
● Fetching
Theindexpreparedbythesystemhasthestructureasfollows:
Fig. 4.2 Posts Fetched from the Index
Fig. 4.3 Post Queue after processing using FRA
FRAorFoodmateRankingAlgorithmisspeciallydesigned forthoseuserswhoarenewtotheplatformandhaven’tinteractedwithanyofthepostsontheplatform.TheFRAutilizesthegeospatialdataofuseraswellasoftherestaurants ofthepoststopushthepostsfromtheclosestrestaurants andusesthetimewhentheposthasbeenpublishedtoagain minutelypersonalizethefeed.Thewholeprocessisdivided into2phasesasfollows:
andthematchcountsareasfollows: P3=3,P5=3,P2=2,P4=2,P7=2,P1=1,P6=1,P8=None
(IRJET) e
WehaveusedMongoDBdatabasetostoretheuserdetails and the database server is hosted on MongoDB Atlas. For userauthenticationwhileloggingintotheplatform,wehave usedhashingalongwithsaltingtoprovideanextralayerof securitytothedatabaseandthus,preventitfromdictionary attack.
IRJET |
P3=1hr ago, P5=23m ago, P7=45m ago, P4=50m ago, P2=38mago,P1=55mago,P6=34mago,P8=50mago
[3]Cantador,Iván&Bellogín,Alejandro&Vallet,David. (2010). Content based recommendation in social taggingsystems.RecSys'10 Proceedingsofthe4thACM Conference on Recommender Systems. 237 240.
and
|
Research Journal of
International Engineering Technology ISSN: 2395 0056
www.irjet.net p
Certified Journal | Page
We would also like to expand our deepest gratitude to all thosewhohavedirectlyandindirectlyguidedusincompletingthisproject.
as we can see P4 and P2 are close to each other so these postswillbesortedbasedonthetimetheywereposted.And hencehereisthefinallistofposts.
Considering the output of the previous step. Here the distanceevaluatedinthepreviousstepsareasfollows, P3=2.3, P5=2.7, P7=1.8, P4=2.5, P2=2.6, P1=1.3, P6=1.7,
ACKNOWLEDGEMENT
Fig. 4.6 Final output of FRA
SoP8=0accordingtoourlogic,thefollowingshouldbethenew sequence
Step 2:Ifthealgorithmencountersanysuchpostswherethe matchcountissameaswellasthedifferencebetweengeo spatialdistanceofthepostsandtheuserisminimum,then thealgorithmpartiallysortsthepostsbasedontheposted timekeepingthelatestpostsatthetop.
REFERENCES
TheP8=0time
[1]BhatiaGresha,ShedgeSaloni,SahetiaSimran,Mhatre Disha,andGangwaniSahil.2020.FoodDicted:ARestaurantFoodRecommendationSystem.InternationalJournalofFutureGenerationCommunicationandNetworking13,1(2020),284 290.
[2]Luhur,H.S.&Widjaja,N.D..(2014).Location based socialnetworkingmediaforrestaurantpromotionand foodreviewusingmobileapplication.EPJWebofConferences.68.10.1051/epjconf/20146800022.
©
solet’scalculatethegeo spatialdistance: P3=2.3, P5=2.7, P2=2.6, P4=2.5, P7=1.8, P1=1.3, P6=1.7,
Afterperformingalltheoperationsofthealgorithm,thefinal outputisalistoffeedwhichhasbeenpersonalizedforthe locationandisensuredthat freshcontentisservedtothe user
4.1.5 Image Recognition
Fig. 4.6 Output of step 1
Forrecommendingthetagsbasedontheimagesofthefood uploaded by the vlogger, Convolutional Neural Network (CNN) is used. We have opted for CNN as it isa preferred techniqueforfood basedimagetagging[5].TheimagesuploadedbytheVloggerwillbeprocessedusingCNNtopredict thefoodtagsdynamicallyandalsoallowingtheVloggerto particularlyaddoredittagsaspertheirconvenience.
Volume: 09 Issue: 03 | Mar 2022 ISSN: 2395 0072 2022, Impact Factor value: 7.529 ISO 9001:2008 1459
We haveputin a lotof effortandworkedwithdedication towards completing this paper. However, this would not havebeenpossiblewereitnotfortheconstantmotivation andencouragementthatwereceivedfromtheteachingas wellasnon teachingfacultyofourcollege.Wewouldliketo express our heartfelt gratitude to our Principal Prof. Dr. Sandeep Joshi for providing a platform for us students to showcaseourskillsandknowledge.
Wearethankful.TheDepartmentofComputerEngineering andourHeadofDepartmentProf.Dr.SharvariGovilkarfor providinguswiththenecessaryresourcesandinformation. WeextendourthankstoourminiprojectcoordinatorProf. PayelThakurandProf.RupaliNikhareforconductingthis activitysmoothlyandhelpingusoutwheneverwehadany doubts.Wewouldalsoliketothankourminiprojectguide Prof.DeeptiLawandfortheconstantsupervisionandguidanceduringtheprocessofimplementationanddocumentationoftheprojectandkeepingusmotivatedallthetime.
[4]10.1145/1864708.1864756.Rahman,Hafiz.(2019).AReviewoftheUsableFood DeliveryApps.InternationalJournalofEngineeringResearchand.V8.10.17577/IJERTV8IS120052.
4.2 Database and Authentication
whenthepostswerepostedareasfollows,
of
[7]Neeach thesocialnetworkingappforfoodlovers (2016) David Carter Shinn https://www.indiegogo.com/projects/neeach the social networking app for food lovers#/
International Journal Engineering Technology (IRJET) ISSN: 2395 0056 09 Issue: 03 | Mar 2022 www.irjet.net ISSN: 2395 0072
HarshalSunilDhake
p
RaghvendraMhatreRamesh
Volume:
BIOGRAPHIES
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page1460
[8]Yummi,asocialnetworkexclusivelydevotedtoall things food related (2016) Hillary K. design(2017)[9]ishttps://www.digitaltrends.com/photography/yummyGrigonisthesocialmediaforfood/UXDesign|FoodAdventuresAppACaseStudyJessicaChanghttps://blog.prototypr.io/uxfoodadventuresapp2e4a4ee8068d
SahilAtul Lola
Research
[5]V.H.Reddy,S.Kumari,V.Muralidharan,K.Gigooand B.S.Thakare,"FoodRecognitionandCalorieMeasurementusingImageProcessingandConvolutionalNeural Network,"20194thInternationalConferenceonRecent TrendsonElectronics,Information,Communication& Technology (RTEICT), 2019, pp. 109 115, doi: [6]10.1109/RTEICT46194.2019.9016694.BiteShareafoodsocialmediaapp(2019),AlineG. Rocha.https://medium.com/@agrdesigner/biteshare a food social media app bd92c7865afa
e
and
TejasSuhasPathak
