Using an Ontological A-priori Score to Infer User

1
Using an Ontological A-priori Score to Infer
Users Preferences

• W17 Workshop on Recommender Systems ECAI 2006
• Advisor Prof Boi Faltings EPFL

2
Presentation Layout

• Introduction
• Introduce the problem and existing techniques
• Transferring Users Preference
• Introduce the assumptions behind our model
• Explain the transfer of preference
• Validation of the model
• Experiment on MovieLens
• Conclusion
• Remarks Future work

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Problem Definition

• Recommendation Problem (RP)
• Recommend a set of items I to the user from a
  set of all items O, based on his preferences P.
• Use a Recommender System, RS, to find the best
  items

• Examples
• NotebookReview.com (ONotebooks, P criteria
  (Processor Type, Screen Size))
• Amazon.com (OBooks, DVDs, , P grading)
• Google (OWeb Documents, P keywords)

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Recommendation Systems

• Three approaches to build a RS 12345
• Case-Based Filtering uses previous cases
• i.e. Collaborative Filtering (cases users
  ratings)
• Good performances low cognitive requirements
• Sparsity, latency, shilling attacks and cold
  start problem

• Content-Based Filtering uses items description
• i.e. Multi-Attribute Utility Theory
  (descriptions-attributes)
• Match users preferences very good precision
• Elicitation of weights and value function.

• Rule-Based Filtering uses association between
  items
• i.e. Data Mining (associations rules)
• Find hidden relationships good domain discovery
• Expensive and time consuming

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Central Problem of RS
6
Presentation Layout

• Introduction
• Introduce the problem and existing techniques
• Transferring Users Preference
• Introduce the assumption behind our model
• Explain the transfer of preference
• Validation of the model
• Experiment on MovieLens
• Conclusion
• Remarks Future work

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Ontology

• D1 Ontology ? is a graph (DAG) where
• nodes models concepts
• Instances being the items
• edges represents the relations (features).
• Sub-concepts are distinguished by certain
  features
• Feature are usually not made explicit

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The Score of Concept -S

• The RP viewed as predicting the score S assigned
  to a concept (group of items).

• The score can be seen as a lower bound function
  that models how much a user likes an item

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A-priori Score - APS

• The structure of the ontology contains
  information
• Use APS(c) to capture the knowledge of concept c

• If no information, assume S(c) uniform 0..1
• P(S(c)gtx)1-x

• Concepts can have n descendants
• Assumption A3 gt P(S(c)gtx)(1-x)n1

• APS uses no user information

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Inference Idea
Select the best Lowest Common Ancestor lca(SUV,
bus) AAAI06
Vehicle
Car
Bus
S(bus)???
SUV
S(SUV)0.8
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Upward Inference
A1 the score depends on the features of the item

vehicle
K levels
SUV

• Going up k levels ? remove k known features

• Removing features ? S? or S ? (S ?S)

• S( vehicle SUV) a( vehicle, SUV) S(SUV)
• a ?0..1 is the ratio of feature in common liked

• How to compute a?

• a feature(vehicle) / feature(SUV)
• Does not take into account the feature
  distribution

• a APS(vehicle) / APS(SUV)

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Downward Inference
A2 Features contributes independently to the
score
vehicle
l levels
bus

• Going down l levels ? adding l unknown features

• Adding features ? S? or S? (S ?S)

S(busvehicle)a S(vehicle) a 1
?

• S(busvehicle) S(vehicle) ß(vehicle, bus)
• ß ?0..1 is ?features in bus not present in
  vehicle

• How to compute ß?

• ß APS(bus) - APS(vehicle)

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Overall Inference

• There exist a chain between city and vehicle
  but not a path

Vehicle

• As for Bayesian Networks, we assume independence

Car
Bus

• S(BusSUV) aS(SUV) ß

SUV

• The score of a concept x knowing y is defined as
• S(yx) a(x,lcax,y)S(x) ß(y,lcax,y)

• The score function is asymmetric

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Presentation Layout

• Introduction
• Introduce the problem and existing techniques
• Transferring Users Preference
• Introduce the assumption behind our model
• Explain the transfer of preference
• Validation of the model
• WordNet (built best similarity metric see
  paper)
• Experiment on MovieLens
• Conclusion
• Remarks Future work

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Validation Transfer - I

• MovieLens database used by CF community
• 100,000 ratings on 1682 movies done by 943 users.

• MovieLens movies are modeled by 23 Attributes
• 19 themes, MPPA rating, duration, and released
  date.
• Extracted from IMDB.com

• Built an ontology modeling the 22 attributes of a
  movies
• Used definitions found in various online
  dictionaries

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Validation Transfer - II

• Experiment Setup for each 943 users
• Filtered users with less than 65 ratings
• Split users data into learning set and test set
• Computed utility functions from learning set
• Frequency count algorithm for only 10 attributes
• Our inference approach for other 12 attributes
• Predicted the grade of 15 movies from the test
  set
• Our approach HAPPL (LNAI 4198 WebKDD05)
• Item-Item based CF (using adjusted Cosine)
• Popularity ranking
• Computed the accuracy of predictions for Top 5
• Used the Mean Absolute Error (MAE)
• Back to 3 with a bigger training set
  5,10,20,,50

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Validation Transfer - III
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Validation Transfer - IV
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Conclusions

• We have introduced the idea that ontology could
  be used to transfer missing preferences.
• Ontology can be used to compute A-priori score
• Inference model - asymmetric property
• Outperforms CF without other people information

• Requirements Conditions
• A2 - Features contributes to preference
  independent.
• Need an ontology modeling all the domain

• Next steps Try to learn the ontology
• Preliminary results shows that we still
  outperform CF
• Learn ontology gives a more restricted search
  space

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Questions?
Thank-you Slides http//people.epfl.ch/vincent.sc
hickel-zuber
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References - I

• 1 Survey of Solving Multi-Attribute Decisions
  Problems
• Jiyong Zang, and Pearl Pu, EPFL Technical
  Report, 2004.
• 2 Improving Case-Based Recommendation A
  Collaborative Filtering Approach
• Derry OSullivan, David Wilson, and Barry Smyth,
  Lecture Notes In Computer Science, 2002.
• 3 An improved collaborative Filtering approach
  for predicting cross-category purchases based on
  binary market data.
• Andreas Mild, and Thomas Reutterer, Journal of
  Retailing and Consumer Services Special Issue on
  Model Building in Retailing consumer Service,
  2002.
• 4 Using Content-Based Filtering for
  Recommendation
• Robin van Meteren and Maarten van Someren,
  ECML2000 Workshop, 2000.
• 5 Content-Based Filetering and Personalization
  Using Structure Metadata
• A. Mufit Ferman, James H. Errico, Peter van
  Beek, and M Ibrahim Sezan, JCDL02, 2002.

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References - II

• AAAI06 Inferring Users Preferences Using
  Onotlogies
• Vincent Schickel and Boi Faltings, In Proc.
  AAAI06 pp 1413 1419, 2006.
• LNAI 4198 Overcoming Incomplete User Models In
  Recommendation Systems via an Ontology.
• Vincent Schickel and Boi Faltings, LNAI 4198, pp
  39 -57, 2006.