A First Approach to Argument-based Recommender Systems based on Defeasible Logic Programming

1. A First Approach to Argument-based Recommender Systems based on Defeasible Logic Programming

2. Outline • (1) Introduction and motivations • (2) Argumentation Framework DeLP • (3) Recommender Systems (RS) • (4) Argument-Based RS • (5) An Argument-Based Search Engine • (6) Conclusions. Ongoing work.

3. The Problem: Information Overload • Recommender Systems address the problem of information overload by providing guidelines or hints.

4. Limitations of Traditional Views • Unable to perform qualitative inference on the recommendations. • Unable to deal with the defeasible nature of user’s preferences. • Unable to provide explanations: trustworthiness issues!

5. Our Proposal • Integrate recommender system technologies with a defeasible argumentation framework. • To enhance practical reasoning capabilities of current recommender systems

6. Outline • (1) Introduction and motivations • (2) Argumentation Framework DeLP • (3) Recommender Systems (RS) • (4) Argument-Based RS • (5) An Argument-Based Search Engine • (6) Conclusions. Ongoing work.

7. DeLP (1) P(,) • Extension of logic programming which allows to reason with tentative, defeasible information. flies(X) bird(X), broken_wing(X) flies(X) bird(X) bird(X) penguin(X) bird(opus) broken_wing(opus)  A, flies(opus) • Argument A,L • 1) AL • 2) AP, ~P • 3) There is no AA such that satisfies 1) and 2). flies(opus) bird(opus) A={ flies(X) bird(X) }

8. DeLP (2) • An argument A, Ldefeats another argument B, Qif • A, Lis in conflict with B, Q • B, Qis preferred over A, Lor is unrelated to A, L  flies(opus) flies(opus) bird(opus) bird(opus), broken_wing(opus) B, flies(opus) A, flies(opus) Specificity is a syntax-based criterion used to define preference ( ) among arguments.

9. DeLP (3) L A D U D D U D U U In order to determine whether an argument A, Lis finally acceptable, a dialectical treerooted in A, Lcan be built. • Leaves are U-nodes. • Inner node U iffeverychildren node is a D-node. • Inner node D iffat leastone children node is a U-node. • An argumentA, Lis warrantedif the root of the associated tree is labelled as U.

10. How DeLP works Defeasible rules Strict rules Facts DeLP Program P User Query ?-flies(opus) Possible Answers to QueryL • YES, there exists a warranted argument A, L) • NO, there exists a warranted argument for A, L • UNDECIDED, none of the above cases hold. DeLP Interpreter Abstract Machine

11. Outline • (1) Introduction and motivations • (2) Argumentation Framework DeLP • (3) Recommender Systems (RS) • (4) Argument-Based RS • (5) An Argument-Based Search Engine • (6) Conclusions. Ongoing work.

12. Recommender Systems • Programs that create a model of the user’s preferences, or the user’s task, to help identify worthwhile items such as news, web pages, books, etc.

13. Goals for Recommender Systems • Find what the user wants. • Know what the user likes. • Gain trustworthiness from the user.

14. Traditional Approaches • Collaborative Filtering Recommenders:Infer preferences of individual users based on behavior of multiple users. • Content-Based Recommenders:Infer preferences of individual users based on what the user liked in the past. • Hybrid Recommenders:Combine both.

15. Hybrid RS: outline

16. Outline • (1) Introduction and motivations • (2) Argumentation Framework DeLP • (3) Recommender Systems (RS) • (4) Argument-Based RS • (5) An Argument-Based Search Engine • (6) Conclusions. Ongoing work.

17. Argument-based RS • Users’ preference criteria are: • Incomplete • Potentially Inconsistent Model the users’ preference criteria in terms of a DeLP program built on top of a content-based search engine.

18. Encoding Users’ Preferences Puser: preferences and behavior of active user (facts, strict rules and defeasible rules) Ppool: preferences and behavior from a pool of users (defeasible rules) Pdomain: domain background knowledge (facts, strict rules and defeasible rules) DeLP Program P

19. Argument-Based RS Architecture

20. Prioritizing Recommendations • Recommendations can be prioritized according to their epistemic status: • Swwarranted results • Suundecided results • Sddefeated results.

21. Outline • (1) Introduction and motivations • (2) Argumentation Framework DeLP • (3) Recommender Systems (RS) • (4) Argument-Based RS • (5) An Argument-Based Search Engine • (6) Conclusions. Ongoing work.

22. Argument-Based Search Engine

23. A Case-Study: Solving Web Search Queries Outbreaks of bird flu ? • Consider a journalist who wants to search for news articles about recent outbreaks of bird flu.

24. Querying a Conventional Search Engine news regarding bird flu Too many results!

25. Applying Implicit Knowledge Articles written by Bob Beak are reliable. Usually, if the journalist is trustworthy then the article is reliable. Old articles are not reliable. If a journalist never faked a report then she is reliably. Thailandian and Japanese newspapers usually offer a biased viewpoint on bird flu outbreaks. The “Japanese Times” is non biased. Chin Yao Lin faked a report.

26. DeLP Program Defeasible Rules

27. DeLP Program Strict Rules

28. Search Results author(s1, chin-yao-lin) address(s1, “jpt.jp/...”) date(s1, 20031003) author(s2, jane-doe) address(s2, “jpt.jp/...”) date(s2, 20031003) author(s3, jane-truth) address(s3, “jpt.jp”) date(s3, 20031003) author(s4, bob-beak) address(s4, “mynews.com/...”) date(s4, 20031003) Facts

29. Is this Article Relevant? rel(s1) author(s1,chin-yao-lin) address(s1,“jpt.jp/...”) date(s1, 20031003) author(s1,chin-yao-lin) trust(chin-yao-lin) notfaked-news(chin-yao-lin) rel(s1) faked-news(chin-yao-lin) address(s1, “jpt.jp/...”) biased(“jpt.jp/...”) japanese(“jpt.jp/...”) biased(“jpt.jp/...”) japanese(“jpt.jp/...”) domain(“jpt.jp/...”; “jpt.jp/...”) (“jpt.jp” = “jpt.jp”)

30. Is this Article Relevant? (cntd) author(s1, chin-yao-lin) address(s1, “jpt.jp/...”) date(s1, 20031003) rel(s1) address(s1, “jpt.jp/...”) biased(“jpt.jp/...”) japanese(“jpt.jp/...”) biased(“jpt.jp/...”) japanese(“jpt.jp/...”) domain(“jpt.jp/...”, “jpt.jp/...”)(“jpt.jp”=“jpt.jp”) Undecided

31. Is this Article Relevant? author(s2, jen-doe) address(s2, “news.co.uk/...”) date(s2, 20001003) rel(s2) author(s2, jen-doe) trust(jen-doe) not faked-news(jen-doe) rel(s2) author(s2,jen-doe) trust(jen-doe) outdated(s2) Warranted! not faked-news(jen-doe)

32. Is this Article Relevant? rel(s3) author(s3, jane-truth) trust(jane-truth) author(s3, jane-truth) address(s3, “jpt.jp”) date(s3, 20031003) not faked_news(jane-truth) rel(s3) address(s3,“jpt.jp/...”) biased(“jpt.jp/...”) japanese(“jpt.jp/...”) Warranted! biased(“jpt.jp/...”) japanese(“jpt.jp/...”) domain(“jpt.jp/...”;“jpt.jp/...”)(“jpt.jp” =“jpt.jp”)

33. Is this Article Relevant? author(s4, bob-beak) address(s4, “mynews.com/...”) date(s4, 20031003) rel(s4) Warranted!

34. Outline • (1) Introduction and motivations • (2) Argumentation Framework DeLP • (3) Recommender Systems (RS) • (4) Argument-Based RS • (5) An Argument-Based Search Engine • (6) Conclusions. Ongoing work.

35. Conclusions • Information needs are complex: • Users’ preferences are frequently inconsistent and incomplete. • Domain knowledge is inconsistent and incomplete. • Traditional recommender systems are unable to perform qualitative inference on the recommendations. • We have proposed a novel way of enhancing recommendation technologies through the use of qualitative analysis using argumentation.

36. Ongoing Work • Implementation! DeLP is fully implemented since 1996, and as a programming language since 1999. • Extraction of relevant features from Web search results to encode them as part of a DeLP program. • Represent semi-structured text through logical formulas. • Defeasible rule discovery. • Integration with specialized argument assistance tools.

37. Questions?