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Transcript of Implicit
ImplicitAn Agent-Based Recommendation System
for Web Search
Presented byShaun McQuaker
Presentation based on paper Implicit: An Agent-Based Recommendation System
Alexander Birukov, Enrico Blanzieri, and Paolo GiorginiDepartment of Information and
Communication TechnologyUniversity of Trento Italy
Overview
Problem Definition Implicit Culture and SICS Implicit System Structure Experimental Results Related Work Conclusions
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
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
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
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
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 PO Scene as the pair <B,A>, where B ,
and A A Situation as <a,,t>, where a P and is
a scene Executed situated action as the action
executed in given situation.
Implicit Culture: basic definitions (2)
Random variable ha,t that describes the action that the agent a executes at the time t
Expected action as the expected value of ha,t , E(ha,t ) Situated expected action as the expected value of ha,t
given a situation <a,,t>; E(ha,t |<a,,t>) Cultural constraint theory for a group GP, 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
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
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
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
SICS Overview
Observer DB
Observer stores in a data base the situated executed actions of the agents of G. Inductive
ModuleInductive 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
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
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
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.
Implicit Usage (1)
Implicit Usage (2)
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?
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
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.
Sample User Profile
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
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
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
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.
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
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.