a first approach to argument-based recommender systems based on defeasible logic programming

A First Approach to Argument-based Recommender Systems based on

Defeasible Logic Programming

Carlos I. ChesñevarDept. of Computer Science

Universidad de Lleida - Spain

Ana G. MaguitmanComputer Science Dept.

Indiana University – USA

Guillermo R. SimariDept. of Computer Science and Eng.

Universidad Nacional del Sur – Argentina

Outline

(3) Recommender Systems (RS)

(2) Argumentation Framework DeLP

(4) Argument-Based RS

(5) An Argument-Based Search Engine

(6) Conclusions. Ongoing work.

(1) Introduction and motivations

Recommender Systems address the problem of information overload by providing guidelines or hints.

The Problem: Information Overload

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!

Our Proposal

• Integrate recommender system technologies with a defeasible argumentation framework.

• To enhance practical reasoning capabilities of current recommender systems

Outline

(3) Recommender Systems (RS)

(2) Argumentation Framework DeLP

(4) Argument-Based RS

(5) An Argument-Based Search Engine

(6) Conclusions. Ongoing work.

(1) Introduction and motivations

flies(X) bird(X), broken_wing(X)

flies(X) bird(X)

bird(X) penguin(X)

bird(opus)

broken_wing(opus)

(,)

bird(opus)

flies(opus)

, flies(opus)

={ flies(X) bird(X) }

Extension of logic programming which allows to reason with tentative, defeasible information.

Argument ,L 1) L 2) P, P3) There is no such that

satisfies 1) and 2).

DeLP (1)

bird(opus)

flies(opus)

bird(opus), broken_wing(opus)

flies(opus)

, flies(opus) , flies(opus)

Specificity is a syntax-based criterion used to define preference ( ) among arguments.

An argument , L defeats another argument , Q if

, L is in conflict with , Q

, Q is preferred over , L or is unrelated to , L

DeLP (2)

U

UD

D

D

U

UD

L

An argument , L is warranted if the root of the associated tree is labelled as U.

In order to determine whether an argument , L is finally acceptable, a dialectical tree rooted in , L can be built.

Leaves are U-nodes.

Inner node U iff every children node is a D-node.

Inner node D iff at least one children node is a U-node.

DeLP (3)

How DeLP works

DeLP Interpreter

Abstract Machine

?- flies(opus)

• YES, there exists a warranted argument , L )

• NO, there exists a warranted argument for , L

• UNDECIDED, none of the above cases hold.

Possible Answers to Query L

User Query Defeasible rules

Strict rules

Facts

DeLP Program P

Outline

(3) Recommender Systems (RS)

(2) Argumentation Framework DeLP

(4) Argument-Based RS

(5) An Argument-Based Search Engine

(6) Conclusions. Ongoing work.

(1) Introduction and motivations

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.

Goals for Recommender Systems

• Find what the user wants.

• Know what the user likes.

• Gain trustworthiness from the user.

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.

Hybrid RS: outline

Outline

(3) Recommender Systems (RS)

(2) Argumentation Framework DeLP

(4) Argument-Based RS

(5) An Argument-Based Search Engine

(6) Conclusions. Ongoing work.

(1) Introduction and motivations

Argument-based RS

Model the users’ preference criteria in terms of a DeLP program built on top of a content-based search engine.

Users’ preference criteria are:

• Incomplete

• Potentially Inconsistent

Encoding Users’ Preferences

DeLP

Program

user: preferences and behavior of active user (facts, strict rules and defeasible rules)

pool: preferences and behavior from a pool of users (defeasible rules)

domain: domain background knowledge (facts, strict rules and defeasible rules)

Argument-Based RS Architecture

Prioritizing Recommendations

Recommendations can be prioritized according to their epistemic status:

• Sw warranted results

• Su undecided results

• Sd defeated results.

Outline

(3) Recommender Systems (RS)

(2) Argumentation Framework DeLP

(4) Argument-Based RS

(5) An Argument-Based Search Engine

(6) Conclusions. Ongoing work.

(1) Introduction and motivations

Argument-Based Search Engine

A Case-Study: Solving Web Search Queries

Consider a journalist who wants to search for news articles about recent outbreaks of bird flu.

Outbreaks of bird flu

?

Querying a Conventional Search Engine

news regarding

bird flu

Too many results!

Applying Implicit KnowledgeArticles 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.

DeLP Program

rel(X) author(X,A), trust(A).

rel(X) author(X,A), trust(A),

outdated(X).

trust(A) not faked-news(A).

rel(X) address(X, Url), biased(Url).

biased(Url) thailandian(Url).

biased(Url) japanese(Url).

biased(Url) japanese(Url), domain(Url,D),

D =“jpt.jp...”.

Defeasible Rules

DeLP Program

rel(X) author(X,bob-beak).

outdated(X) date(X,D), getdate(Today),

(TodayD)>100.

thailandian(X) [Computed elsewhere]

japanese(X) [Computed elsewhere]

domain(Url, D) [Computed elsewhere]

getdate(T) [Computed elsewhere]

faked-news(chin-yao-lin)

Strict Rules

Search Results Facts 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)

Is this Article Relevant?

author(s1,chin-yao-lin)address(s1,“jpt.jp/...”)date(s1, 20031003)

rel(s1)

author(s1,chin-yao-lin) trust(chin-yao-lin)

not faked-news(chin-yao-lin)

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”)

faked-news(chin-yao-lin)

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

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)

author(s2,jen-doe) trust(jen-doe) outdated(s2)

not faked-news(jen-doe)

rel(s2)

Warranted!

Is this Article Relevant?

author(s3, jane-truth)address(s3, “jpt.jp”)date(s3, 20031003)

rel(s3)

author(s3, jane-truth) trust(jane-truth)

not faked_news(jane-truth)

rel(s3)

address(s3,“jpt.jp/...”) biased(“jpt.jp/...”)

japanese(“jpt.jp/...”)

biased(“jpt.jp/...”)

japanese(“jpt.jp/...”) domain(“jpt.jp/...”;“jpt.jp/...”) (“jpt.jp” =“jpt.jp”)

Warranted!

Is this Article Relevant?

author(s4, bob-beak)address(s4, “mynews.com/...”)date(s4, 20031003)

Warranted!

rel(s4)

Outline

(3) Recommender Systems (RS)

(2) Argumentation Framework DeLP

(4) Argument-Based RS

(5) An Argument-Based Search Engine

(6) Conclusions. Ongoing work.

(1) Introduction and motivations

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.

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.

Questions?