Implicit

1. Implicit An Agent-Based Recommendation System for Web Search Presented by Shaun McQuaker shaun_mcquaker@hotmail.com Presentation based on paper Implicit: An Agent-Based Recommendation System Alexander Birukov, Enrico Blanzieri, and Paolo Giorgini Department of Information and Communication Technology University of Trento Italy

2. Overview • Problem Definition • Implicit Culture and SICS • Implicit System Structure • Experimental Results • Related Work • Conclusions

3. Problem Definition • Increasing amount of web content • On July 2004 there were 285,139,107 hosts on the Internet • Finding relevant information is a hard task • Approximately 56.3% of the Internet users search the web at least once per day • 33% rarely look at second page of results

4. Problem Solutions • Authority-based search engines • Recommendation systems • Systems that deal with the content of the web pages • Systems that use a collaborative approach • Agents and multi-agent systems • Software agent that assists its user

5. Solution: Implicit • Agent-based recommendation system • Intended to improve web search of a community of people with similar interests • Based on the concept of Implicit Culture

6. Implicit Culture Motivation • An agent interacting in a new environment • Humans experience culture shock • New user of a system, where is the printer? • Solutions • Just ask someone • Represent relevant knowledge and give it to the agent • Agent with observational and learning capabilities

7. Implicit Culture: basic definitions (1) LetPbe a set of agents, Oa set of objects,Aa set of actions. We define: • EnvironmenteÍ PÈO • Scene as the pair <B,A>, where B Í e, and A Í A • Situation as <a,s,t>, where a Î Pand s is a scene • Executed situated actionas the action executed in given situation.

8. Implicit Culture: basic definitions (2) • Random variable ha,tthat describes the action that the agent a executes at the time t • Expected actionas the expected value of ha,t , E(ha,t ) • Situated expected actionas the expected value of ha,t given a situation <a,s,t>; E(ha,t |<a,s,t>) • Cultural constraint theory for a group GÍ P, as a theory on the situated expected actions of the agents ofG • Cultural action w.r.t.G, as an executed action that satisfies a cultural constraint theory for G

9. Implicit Culture Solution • Provides a method where new agents can behave similarly to existing agents. • Control the environment • Change environment to express implicit knowledge of the agent. • Directory Finder for services • Existing agents may have optimized behaviour thus a new agent entering performs in an optimal manner

10. Implicit Culture System • Has goal of achieving implicit culture • Achieves it by • Building validated cultural constraints from observations of situated actions • Presenting scenes to agent such that their actions satisfy this constraint • Directory recommends service that best fits request

11. SICS • Systems for Implicit Culture Support • Goal: produce Implicit Culture phenomenon • Architecture • Observer, stores executed situated actions done by agents in the group • Inductive module, uses actions to produce a cultural constraint theory • Composer, using theory and actions to manipulate scenes faced by the agents

12. SICS Overview S0 Observerstores in a data base the situated executed actions of the agents of G. Inductive Module S Inductive Moduleusing the data from the DB induces a cultural constraint theoryS. Can use clustering techniques, a priori learning. DB Observer Composer • Composerproposes to a group G’ a set of scenes such that the expected situated actions satisfiesS. • Two sub-components: • Cultural Actions Finder • Scene Producer

13. SICS Composer • Cultural Actions Finder • Takes as input the theory Sand executed situated actions of G’ and produces cultural actions that satisfy S. • Scenes Producer • Takes one of the cultural actions produced by CAF and executed situated actions of G, and produces scenes such that the expected situated action is the cultural action. • Directory Finder Example • Cultural theory: request(x,DF,s) ^ inform(DF,x,y) -> request(x,y,s) • Agent in G’ makes request(x,DF,s) • CAF produces request(x,y,s) • SP proposes y to provide service s, thus inform(DF,x,y) • It is now expected that the agent (x) will chose y to provide service s

14. Implicit • Implemented in JADE • SICS module incorporated in agent to produce recommendations • Agents communicate with outside search source, Google. • Agents are collaborative • Send messages between each other

15. Implicit Messages • Query Message • Information about user query or agent query • Reply Message • Contains recommended link or ID of another agent • Feedback Message • Contains accepted/reject links or agent Ids.

16. Implicit Usage (1)

17. Implicit Usage (2)

18. Experimental Purpose • Understand how the insertion of a new member into the community affects the relevance, in terms of precision and recall, of the links that are produced by SICS. • Also after a certain number of interactions, will personal agents be able to propose links accepted in previous searches?

19. Experimental Measurements • Link is relevant to a particular keyword if probability of acceptance is above a certain threshold (0.1) • Precision is the number of suggested relevant links to total number of suggested links. • Recall is the ratio of proposed relevant links to the total number of relevant links

20. User Interaction • User profiles replace user interaction. • 10x10 matrix of keywords vs. rank • Values denote probability that link is relevant • Assume all users are similar, thus personal profile is derived from a base profile. • User accepts only one link, other suggested links are rejected. • Datasets replace queries to Google.

21. Sample User Profile

22. Experiment Details • SICS module suggests links for keywords after observing user acceptance. • Suggestions are given by other agents based on their user profiles • User will accept or reject suggest links. • Feedback is sent • Relevant/Irrelevant links are enumerated • Precision and Recall are calculated

23. Experimental Results • More agents = more relevant link suggestions • Agents with same profile in community of 4 or 5 agents performed on average better across all tests • Agents have determined which link is the most relevant given a group of agents with the same profile (interests). • An Implicit Culture has been established

24. Related Work • InfoSpiders, analyze hyperlinks on current page to propose new documents • Goal-oriented web search • What to do if my pet is sick? • Take it to a veterinarian, return closest veterinarian office • Referral Network • Agents have interest, expertise, neighbours • Can query, provide answers or referrals • Ontology to facilitate knowledge sharing

25. Future Work • Improve composer module by using association rules • Analyze social relations between agents • Hybrid Referral Network and Implicit Culture • Using ontologies agents could connect to related communities • Search each community for relevant links.

26. Conclusions • Agents interacting in Implicit Culture allow better recommendations to be made • Prevents new agents from searching “from scratch” • Uses power of other agents as well as a search engine • Process is transparent to user

27. References • Birukov Alexander, Blanzieri Enrico, Giorgini Paolo (2005), Implicit: An Agent-Based Recommendation System, Department of Information and Communication Technology, University of Trento, Italy. • Blanzieri Enrico, Giorgini Paolo (2000), From Collaborative Filtering to Implicit Culture: a general agent-based framework, ITC-IRST Trento, Italy, University of Trento, Italy. • Lin Weiyang, Alvarez A. Sergio, Ruiz Carolina (2001), Efficient Adaptive-Support Association Rule Mining for Recommender Systems, Microsoft Corporation, Department of Computer Science, Boston College, Department of Computer Science, Worcester Polytechnic Institute.