recipe recommendation reasoning on features: ensemble ... · core challenges in food/recipe...
TRANSCRIPT
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Recipe Recommendation Reasoning on Features: Ensemble Topic Modeling Based
Approach
Mansura A. Khan, Ellen Rushe, Barry Smyth and David Coyle University College Dublin
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Whyfoodrecommendationisinteresting?
Foodchoiceismultifaceted,multilevel,contextual,dynamic,integratedand
diverse-JefferySobal
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• Capturing user’s preference on food related aspects.
• Identifying coherent content and context information.
• Getting access to standardized food and nutrition data.
• Representation of Food Data
• Mapping health on recommendations.• Measuring healthiness of food item. • Measure user’s nutrient requirement.
• Diversity
• Coverage
• Pursue user to choose healthier options.
• Achieve continuous and regular user engagement.
CoreChallengesinFood/RecipeRecommendationDomain
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Whatisarecipe?
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RecipeisaStory:Document
Title:YummymashedpotatoesIngredients:butter,cream,potatoes,saltandpepper
Instruction:cookpotatoesinbowlingwateruntiltenderbutstillfirm,Inasmallsaucepanheatbutterandcreamatlowheat.Usingapotatomasherslowlyblendcreammixtureintopotatoes.Seasonwithsaltandpeppertotaste.
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FrequencyCount:
Occurrenceoftermsineachdocument
Terms Frequency
Potatoes 4
saucepan 1
heat 2
title 1
…..
in 2
salt 2
tender 1
a 2
HowRSreadsarecipe?
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Bagofwordrepresentation
Title:YummymashedpotatoesIngredients:potatoes,butter,cream,saltandpepper
Instruction:cookpotatoesinbowlingwateruntiltenderbutstillfirm,Inasmallsaucepanheatbutterandcreamatlowheat.Usingapotatomasherslowlyblendcreammixtureinto
potatoes.Seasonwithsaltandpeppertotaste.
a in potatoes salt low Saucepane but masher blend
2 2 4 2 1 1 1 1 1
Recipe 1
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HundredsofthousandsofDocuments:RecipeCorpus
Title:IrishstewIngredients:chucklambstewmeat,parsnips,broth,russetpotatoesInstruction
:cookpotatoesinbowlingwateruntiltenderbutstillfirm,Inaccsmallsaucepanheatbutterandcreamatlowheat.Usingapotatomasher
slowlyblendcreammixtureintopotatoes.Seasonwithsaltandpeppertotaste.Title:Gunniesbeefpie
Ingredients:flour,beef,gunnies,butter,salt,celery,beefstock,carrotInstruction:cookpotatoesinbowlingwateruntiltenderbutstillfirm,Ina
choppmall saucepanheatbutterandcreamatlowheat.Usingapotatomasherslowlyblendcreammixtureintopotatoes.Seasonwithsaltandpeppertotaste.
Title:YummymashedpotatoesIngredients:butter,cream,potatoessaltandpepper
Instruction:cookpotatoesinbowlingwateruntiltenderbutstillfirm,Inasmallsaucepanheatbutterandcreamatlowheat.Usingapotatomasherslowlyblendcreammixtureintopotatoes.Seasonwithsaltandpeppertotaste.
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Recipe1,2,3
Bagofwordrepresentation
a in potatoes salt low saucepan but beef lamb
R1 2 2 4 2 1 1 1 0 0
R2 9 5 0 4 0 4 0 2 0
R3 6 4 3 3 0 2 0 0 3
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Recommendingrecipes
a in potatoes salt low saucepan but beef lamb
R1 2 2 4 2 1 1 1 0 0
R2 9 5 0 4 0 4 0 2 0
R3 6 4 3 3 0 2 0 0 3
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Understandingtherelevanceweight
a in potatoes salt low saucepan but beef lamb
R1 2 2 4 2 1 1 1 0 0
R2 9 5 0 4 0 4 0 2 0
R3 6 4 3 3 0 2 0 0 3
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DocumentFrequency:Occurrenceoftermsoverentirecorpus
Terms Frequency
Potatoes 7
saucepan 7
heat 9
Title 3
…..
in 11
salt 9
tender 1
a 17
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a in potatoes salt low saucepan but beef lamb
R10.493 0.871 2.296 1.045 1.477 0.632 1.477 0 0 0 0 0 0
R22.220 2.178 0.574 2.091 0 2.528 0 0 0 2.352 0 0 0
R31.480 1.742 1.722 1.568 0 1.264 0 0 0 0 0 0 3
a in potatoes salt low saucepan but beef lamb
R1 2 2 4 2 1 1 1 0 0
R2 9 5 0 4 0 4 0 2 0
R3 6 4 3 3 0 2 0 0 3
Tf-Idf Matrix
FrequencyMatrix
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• 10,661 ingredients• 230,872 recipes
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ItFeelsLikeEverythinghasbeenFiguredOut!Butjustnotyet!
Considering only highly frequent ingredients ?
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CorecontentsaroundFoodWhatinformationarerelevanttoFoodRecommender?
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TopicModeling
Topicmodelsaimtodiscoverthelatentsemanticstructureorthemeswithinacorpusofdocuments,whichcanbederived
fromco-occurrencesofwordsacrossthedocuments.-MarkBelford
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Atopicmodeltypicallyconsistsofktopics,eachrepresentedbyarankedlistofstrongly-associatedterms(oftenreferredto
asa“topicdescriptor”).
TopicModel
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potatoes saucepan coco beef lambTopic1 6 .07 0 7 0
Topic2 6 1 0 0 4
Topic3 0 1 2 0 0
Topic1 Topic2 Topic3R1 .5 0 0
R2 0 0 0
R3 .7 0 0.002
R4 0 0 0
R5 0 0.3 0.9
R6 0 0 0
TopicModeling
Topic-TermMatrix
Recipe-TopicMatrix
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Eachdocument/recipeinthecorpuscanbeassociatedwithoneormoretopics.
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WHV
Non- negativematrixfactorization
𝑛×𝑚 𝑛×𝑟 r×𝑚
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Wa 𝑏
𝑉 ≈ 𝑊×𝐻
Itcanberewrittencolumnbycolumnas,
a~𝑊×𝑏
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potatoes saucepan coco beef lambTopic1 6 .07 0 7 0
Topic2 6 1 0 0 4
Topic3 0 1 2 0 0
Topic1 Topic2 Topic3R1 .5 0 0
R2 0 0 0
R3 .7 0 0.002
R4 0 0 0
R5 0 0.3 0.9
R6 0 0 0
TopicModeling
Topic-TermMatrix
Recipe-TopicMatrix
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Outcomeofdifferenttopicmodelingsessionisonlyapproximatelyequal
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Ensemble procedures seek to encourage diversity with a view to improving the quality of the information available in the integration phase
- AlexanderTopchy
EnsembleTopicModeling
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Recipe Recommendation Reasoning on Features : Ensemble Topic Modeling Based Approach
Extracting significant Features(words) using Ensemble Topic modeling
l Tokenize , Lemmatize, Stem , customWordl Domain and corpus specific Stopwords generation l Ensemble Topic modelingl Selecting first 15 words from top 30 topics l Calculating the weight for the Unique 288 words from the previous step
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Exampleofidentifiedtopics
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The final 288 Food Features
Which we later considered the basis of new data representation format and thereby recommendation algorithms
low-calorie
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ItFeelsLikeEverythinghasbeenFiguredOut!Butjustnotyet!
The final 288 Food Features
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The final 288 Food Features
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Our proposed n-by-288 data representation format
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Recommendation approaches
l ua is an userl rn is a new recipe l i is each feature in rn i=0,1,2....ml fi,ua is ua’s rating on i
Food Features based Recommendation (FFbR)
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Recommendation approaches
l ua is an userl rn is a new recipe l i is each ingredient in rn i=0,1,2....ml fi,ua is ua’s rating on il wi is significance score of each
feature
Weighted Food Features based Recommendation (WFFbR)
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Recommendation approaches
l ua is an userl i is each neighbor of ua i=0,1,2....nl fb,i is neighbor i’s rating on a feature fbl We calculate fr,ua using FFbR after learning ua’s rating on all unknown feature
fb using FFbCF
Food Features based Collaborative Filtering (FFbCF)
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Recommendation approaches
Content Based(CB)
l ua is an userl rn is a new recipe l i is each ingredient in rn i=0,1,2....ml Ingi,ua is ua’s rating on i
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User modeling and testing the recommendation algorithms
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UserStudy:4recommendersand48user
Users’spreadovergendercategory
FemaleMaleOther
Users’spreadoveractivitycategory
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UserStudy:4recommendersand48user
Users’spreadoveragerange
Participantnumber
Age
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UserStudy:4recommendersand48user
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UserStudy:4recommendersand48user
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UserStudy:4recommendersand48user
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UserStudy:4recommendersand48user
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UserStudy:4recommendersand48user
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UserStudy:4recommendersand48user
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UserStudy:4recommendersand48user
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UserStudy:
UserStudy:4recommendersand48user
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UserStudy:UserStudy:4recommendersand48user
https://www.youtube.com/watch?v=ujaB0FiqRwk
D
Demoonourdevelopedsystemcouldbefoundon
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Results
Coverage User preference
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Identifying user’s food requirement and restriction
Results
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RecommendingHealthyRecipe??
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RecommendingHealthyRecipe??
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Lookingforusergroupwithhealthierlifestyle??
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Users’spreadoverdifferentHealthinessscale
Lookingforusergroupwithhealthierlifestyle??
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Positivelycorrelatedfeaturestohealthierusergroups
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Negativelycorrelatedfeaturestohealthierusergroups
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Limitation
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Featurework
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Thank You