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Vincent Schickel-Zuber - AI Lab 29th August 2006
Using an Ontological A-priori Score to Infer User’s Preferences
W17: Workshop on Recommender Systems – ECAI 2006
Advisor: Prof Boi Faltings – EPFL
Vincent Schickel-Zuber - AI Lab 2
Presentation Layout
1. Introduction Introduce the problem and existing techniques
2. Transferring User’s Preference Introduce the assumptions behind our model Explain the transfer of preference
3. Validation of the model Experiment on MovieLens
4. Conclusion Remarks & Future work
Vincent Schickel-Zuber - AI Lab 3
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 (O=Notebooks, P= criteria (Processor Type, Screen
Size)) Amazon.com (O=Books, DVDs,… , P= grading) Google (O=Web Documents, P= keywords)
Vincent Schickel-Zuber - AI Lab 4
Recommendation Systems
Three approaches to build a RS: [1][2][3][4][5]
1. Case-Based Filtering: uses previous casesi.e.: Collaborative Filtering (cases – user’s ratings)
Good performances – low cognitive requirementsSparsity, latency, shilling attacks and cold start problem
3. Rule-Based Filtering: uses association between itemsi.e.: Data Mining (associations – rules)
Find hidden relationships – good domain discoveryExpensive and time consuming
2. Content-Based Filtering: uses item’s descriptioni.e.: Multi-Attribute Utility Theory (descriptions-attributes)
Match user’s preferences – very good precision
Elicitation of weights and value function.
Vincent Schickel-Zuber - AI Lab 5
Central Problem of RS
A Major Problem in RS: The Elicitation Problem=> Incomplete user’s model
Collaborative Filtering
5I4
4I55
3I245
4I134
Multi-Attribute Utility Theory
Vincent Schickel-Zuber - AI Lab 6
Presentation Layout
1. Introduction Introduce the problem and existing techniques
2. Transferring User’s Preference Introduce the assumption behind our model Explain the transfer of preference
3. Validation of the model Experiment on MovieLens
4. Conclusion Remarks & Future work
Vincent Schickel-Zuber - AI Lab 7
OntologyD1 Ontology λ 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
Car
Vehicle
Transport
Boat
Bus
On-land On-sea
<7 >6
Compact SUV
City All_terrain
Vincent Schickel-Zuber - AI Lab 8
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
S is a function that satisfies the assumptions: A1: S depends on the features of the item
Items are models by a set of features A2: Each feature contributes independently to S
Eliminates the inter-dependence between features A3: unknown|disliked features make no contribution
Reflects the fact that users are risk-averse Liking a concept liking a sub-concept
Vincent Schickel-Zuber - AI Lab 9
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)>x)=1-x
Concepts can have n descendants Assumption A3 => P(S(c)>x)=(1-x)n+1
E(c)= ∫xfc(x)dx = 1n+2
APS(c)=n+2
1
#descendants
APS
0,5
leafs
root
APS uses no user information
Vincent Schickel-Zuber - AI Lab 10
Inference Idea
Car
Vehicle
Bus
S(SUV)=0.8
SUV
S(bus)=???
Select the best Lowest Common Ancestor lca(SUV, bus) – AAAI’06
Vincent Schickel-Zuber - AI Lab 11
Upward Inference
Going up k levels ⇒ remove k known features
A1 the score depends on the features of the item K levels
SUV
vehicle
Removing features ⇒ S↘ or S ↔ (S =∑S) S( vehicle | SUV)= α( vehicle, SUV) * S(SUV)
α ∈[0..1] is the ratio of feature in common liked
How to compute α?α =#feature(vehicle) / #feature(SUV) Does not take into account the feature distribution
α =APS(vehicle) / APS(SUV)
Vincent Schickel-Zuber - AI Lab 12
Downward Inference
Going down l levels ⇒ adding l unknown features
l levelsbus
vehicle
Adding features ⇒ S↗ or S↔ (S =∑S)
S(bus|vehicle)=α S(vehicle) α ≥ 1
How to compute β? β = APS(bus) - APS(vehicle)
⇏ S(bus|vehicle)= S(vehicle) + β(vehicle, bus)
β ∈[0..1] is ∑features in bus not present in vehicle
A3 Users are pessimistic liking some features liking othersA2 Features contributes independently to the score
Vincent Schickel-Zuber - AI Lab 13
Overall Inference
Car
Vehicle
SUV
Bus
There exist a chain between “city” and vehicle but not a path
As for Bayesian Networks, we assume independence
S(Bus|SUV)= αS(SUV) + β
The score of a concept x knowing y is defined as:
S(y|x)= α(x,lcax,y)S(x) + β(y,lcax,y)
Use APS The score function is asymmetric
Elicited from the user
Vincent Schickel-Zuber - AI Lab 14
Presentation Layout
1. Introduction Introduce the problem and existing techniques
2. Transferring User’s Preference Introduce the assumption behind our model Explain the transfer of preference
3. Validation of the model WordNet (built best similarity metric – see paper) Experiment on MovieLens
4. Conclusion Remarks & Future work
Vincent Schickel-Zuber - AI Lab 15
Validation – Transfer - I
MovieLens – movies are modeled by 23 Attributes 19 themes, MPPA rating, duration, and released date. Extracted from IMDB.com
MovieLens database used by CF community: 100,000 ratings on 1682 movies done by 943 users.
Built an ontology modeling the 22 attributes of a movies Used definitions found in various online dictionaries
-name
Themes
Unknown
Animation Adventure DocumentaryDrama
Musical
Comedy
Romance
Scifi
Action
Mystery
Western War
Horror
Thriller
Crime
Film-Noir
Children Fantasy
null
1 *
Vincent Schickel-Zuber - AI Lab 16
Validation – Transfer - II
Experiment Setup – for each 943 users1. Filtered users with less than 65 ratings2. Split user’s data into learning set and test set 3. Computed utility functions from learning set
1. Frequency count algorithm for only 10 attributes2. Our inference approach for other 12 attributes
4. Predicted the grade of 15 movies from the test set Our approach – HAPPL (LNAI 4198 – WebKDD’05) Item-Item based CF (using adjusted Cosine) Popularity ranking
5. Computed the accuracy of predictions for Top 5 Used the Mean Absolute Error (MAE)
6. Back to 3 with a bigger training set {5,10,20,…,50}
Vincent Schickel-Zuber - AI Lab 17
Validation – Transfer - III
Top 5 Strategy
0.73
0.78
0.83
0.88
0.93
0.98
5 10 20 30 40 50
#learning ratings in learning set
MA
E
Popularity
Hybrid
HAPPL
CF
Vincent Schickel-Zuber - AI Lab 18
Validation – Transfer - IV
Top 5 Strategy
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
5 10 20 30 40 50
#learning ratings in learning set LS
No
velt
y
HAPPL| Popularity
Hybrid | Popularity
CF| Popularity
Vincent Schickel-Zuber - AI Lab 19
Conclusions
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
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
n+21
Vincent Schickel-Zuber - AI Lab 20
Questions?
Thank-youSlides: http://people.epfl.ch/vincent.schickel-zuber
Vincent Schickel-Zuber - AI Lab 21
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 ApproachDerry O’Sullivan, 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 RecommendationRobin van Meteren and Maarten van Someren, ECML2000 Workshop, 2000.
[5] Content-Based Filetering and Personalization Using Structure MetadataA. Mufit Ferman, James H. Errico, Peter van Beek, and M Ibrahim Sezan, JCDL02, 2002.
Vincent Schickel-Zuber - AI Lab 22
References - II
[AAAI’06] Inferring User’s Preferences Using OnotlogiesVincent Schickel and Boi Faltings, In Proc. AAAI’06 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.