ImplicitAn Agent-Based Recommendation System

for Web Search

Presented byShaun McQuaker

shaun_mcquaker@hotmail.com

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.