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) Let P be a set of agents, O a set of objects, A a set of actions. We define: Environment   P  O Scene as the pair, where B  , and A  A Situation as, where a  P and  is a scene Executed situated action as the action executed in given situation.

8. Implicit Culture: basic definitions (2) Random variable h a,t that describes the action that the agent a executes at the time t Expected action as the expected value of h a,t, E( h a,t ) Situated expected action as the expected value of h a,t given a situation ; E( h a,t | ) Cultural constraint theory for a group G  P, as a theory on the situated expected actions of the agents of G 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 Observer DB Observer stores in a data base the situated executed actions of the agents of G. Inductive Module  Inductive Module using the data from the DB induces a cultural constraint theory  Can use clustering techniques, a priori learning. Composer Composer proposes to a group G’ a set of scenes such that the expected situated actions satisfies  Two sub-components: Cultural Actions Finder Scene Producer 

13. SICS Composer Cultural Actions Finder  Takes as input the theory  and executed situated actions of G’ and produces cultural actions that satisfy . 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.