EPFL’s AI Laboratory

1. EPFL’s AI Laboratory Meeting at the University Nancy 2 – 11 Oct 2006

2. EPFL in some numbers… • Ecole Polytechnique Fédérale de Lausanne • Founded in 1879 as part of university • Since 1969, one of the two federally funded university in Switzerland • In total: ~10’000 people • Annual budget from Swiss government ~380M€ • >5000 bachelor and master students in 13 domains • >1000 doctoral students • >250 research faculties • >100 nationalities

3. Computing at EPFL • The School of Computer and Communication Sciences at EPFL is one of the major European centers of teaching and research in information technology • 34 professors • 200 Ph.D students • >1000 bachelor and master students • > 8M€ research external funding.

4. The AI lab at EPFL • Director: Prof Boi Faltings • Software agent • Case based reasoning • Constraint-based reasoning • Recommender Sytems • 3 Teaching Professors • Development of a research and teaching activity in the domain of Natural Language. • Numerical constraint satisfaction problems  • 2 Post-docs • In charge of European project: CASCOM, DIP, Knowledge web • 12 Phd students • http://liawww.epfl.ch/

5. Agent 1 Agent i Agent k Constraint Solver x1 x2 x3 x4 x5 Artificial Intelligence • Definition 1: • Software to mimic human behavior • Definition 2: • Software to make people more intelligent • In particular, for artificial domains: accounting, design, planning, coordination… • Our vision of AI: • Combine expertise and concerns of many parties to solve problems that no individual can combine

6. Open Issues • Network leads to unbounded, dynamic problems => distributed problem-solving • Make agent incentives compatible to discourage manipulation • Model and reason with people’s preferences

7. Preference Elicitation – P. Viappiani • Objective: Build interactive tools that help users search for their most preferred item in a large collection of options (Preference-based search). • Consider example-critiquing, a technique for enabling users toincrementally construct preference models by critiquing exampleoptions that are presented to them. • Investigate techniques for generating automatic suggestions considering the uncertainty of the preference model and heuristics

8. Preference Elicitation – V. Schickel • Objective: Try to estimate missing preferences from an incomplete elicited user model and see how much preferences can be transferred. • Study how ontology can be used to model user’s preference model and model e-catlog product. • Investigate inference technique to “guess” missing preferences. • Build more robust similarity metric for hierarchical ontologies.

9. Reputation Mechanism – R. Jurca • Objective: build of reputation mechanisms for online environments where agents do not a priory trust each other. • The reputation of an agent is obtained by aggregating feedback • While most of the previous results assume that agents report feedback honestly, we explicitly consider rational reporting incentives and guarantee that truth-telling is in the best interest of the reporters. Carefully designed payment schemes (agents get paid when reporting feedback) insure that truth-telling is a Nash equilibrium: as long as other agents report honestly, no reporter can gain by lying.

10. Reputation Mechanism – Q. Ngyen • Objective: find local search algorithms that achieve good performance while satisfying the incentive compatibility for bounded-rational agents. • Studying randomized algorithms and local search algorithms. • Main contributions including a local search algorithm called Random Subset optimization algorithm and an incentive compatible and budget-balanced protocol called leave-one-out protocol for bounded-rational agents.

11. Distributed constraint optimization – A. Petcu • Objective: Develop a Multiagent Constraint OPtimization (MCOP) for solving numerous practical problems like planning but distributed on many agents • Developing a mechanism to build MCOP in a linear number of messages (DPOP). • Study dynamic environments (problems can change over time) and a self-stabilizing version of DPOP that can be applied in and techniques that maintain privacy.

12. Using an Ontological A-priori Score to Infer User’s Preferences Advisor: Prof Boi Faltings – EPFL

13. Presentation Layout • Introduction • Introduce the problem and existing techniques • Transferring User’s Preference • Introduce the assumptions behind our model • Explain the transfer of preference • Validation of the model • Experiment on MovieLens • Conclusion • Remarks & Future work

14. 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)

15. Recommendation Systems • Three approaches to build a RS: [1][2][3][4][5] • Case-Based Filtering: uses previous cases i.e.: Collaborative Filtering (cases – user’s ratings) • Good performances – low cognitive requirements • Sparsity, latency, shilling attacks and cold start problem • Content-Based Filtering: uses item’s description i.e.: Multi-Attribute Utility Theory (descriptions-attributes) • Match user’s 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

16. A Major Problem in RS: The Elicitation Problem => Incomplete user’s model • Collaborative Filtering • Multi-Attribute Utility Theory I134 4 I245 3 I55 4 I4 5 Central Problem of RS

17. Presentation Layout • Introduction • Introduce the problem and existing techniques • Transferring User’s Preference • Introduce the assumption behind our model • Explain the transfer of preference • Validation of the model • Experiment on MovieLens • Conclusion • Remarks & Future work

18. Transport On-land On-sea Vehicle Boat <7 >6 Car Bus City All_terrain SUV Compact Ontology D1 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

19. 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 The Score of Concept -S • The RP viewed as predicting the scoreS 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

20. E(c)= ∫xfc(x)dx = 1 leafs n+2 1 APS 0,5 • APS(c)= root n+2 #descendants 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 • APS uses no user information

21. Select the closest concept to bus. i.e.: find most similar concept – IJCAI’07 Inference Idea Select the best Lowest Common Ancestor lca(SUV, bus) – AAAI’06 Vehicle Car Bus S(bus)=??? SUV Utilities S(SUV)=0.8 Pickup S(Pickup)=0.6

22. Upward Inference A1the score depends on the features of the item • Going up k levels ⇒ remove k known features vehicle K levels SUV • 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)

23. A3 Users are pessimistic liking some features liking others Downward Inference A2Features contributes independently to the score • Going down l levels ⇒ adding l unknown features vehicle l levels bus • Adding features ⇒ S↗ or S↔ (S =∑S) S(bus|vehicle)=α S(vehicle) α≥1 ⇏ • S(bus|vehicle)= S(vehicle) + β(vehicle, bus) • β∈[0..1] is ∑features in bus not present in vehicle • How to compute β? • β= APS(bus) - APS(vehicle)

24. Elicited from the user Use APS 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(Bus|SUV)= αS(SUV) + β SUV • The score of a concept x knowing y is defined as: S(y|x)= α(x,lcax,y)S(x) + β(y,lcax,y) • The score function is asymmetric

25. Presentation Layout • Introduction • Introduce the problem and existing techniques • Transferring User’s Preference • Introduce the assumption behind our model • Explain the transfer of preference • Validation of the model • WordNet (built best similarity metric – see IJCAI’07) • Experiment on MovieLens • Conclusion • Remarks & Future work

26. 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

27. Validation – Transfer - II • Experiment Setup – for each 943 users • Filtered users with less than 65 ratings • Split user’s 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 – WebKDD’05) • 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}

28. Validation – Transfer - III

29. Validation – Transfer - IV

30. 1 n+2 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

31. 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 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 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.

32. References - II [AAAI’06] Inferring User’s Preferences Using Onotlogies Vincent Schickel and Boi Faltings, In Proc. AAAI’06 pp 1413 – 1419, 2006. [IJCAI’07] OSS: A Semantic Similarity Function based on Hierarchical Ontologies Vincent Schickel and Boi Faltings, To appear in Proc. IJCAI’07. [LNAI 4198] Overcoming Incomplete User Models In Recommendation Systems via an Ontology. Vincent Schickel and Boi Faltings, LNAI 4198, pp 39 -57, 2006. Thank-you Slides: http://people.epfl.ch/vincent.schickel-zuber