Personalised Search on the World Wide Web

Presented by: Team Grape

About

Team Grape: Jin Wu Kewei Duan Linh Duy To Miaolai Han Takazumi Matsumoto The Paper: Personalized Search on the World Wide Web Alessandro Micarelli Fabio Gasparetti Filippo Sciarrone Susan Gauch The interactive stuff: MOT lesson Grapple lessons: Text only , Depth first

Overview

1. Introduction 2. A Short Overview on Personalised Search 3. Contextualised Search 4. Personalisation Based on Search Histories 5. Personalisation Based on Rich Representations of User Needs 6. Collaborative Search Engines 7. Adaptive Result Clustering 8. Hyperlink-Based Personalisation 9. Combined Approaches to Personalisation 10. Conclusions

Introduction

• • • • Personalisation “adapting the results according to each user’s information needs” (Micarelli et al., 2007, p. 195) Searching the WWW Dealing with the information overload Limitations of traditional search engines Information access paradigms: – Searching by surfing (hyperlink directories) – Searching by query (Information Retrieval) – Recommendation (suggested items)

Content and Collaborative-based Personalisation

• • • Originally: information retrieval Content-based: – Consider individuals - mostly used – Polysemy & synonymy leads to vocabulary problem → irrelevant information Collaborative-based: – – Consider models of different users User similarity → similar information needs – – Social navigation Not employed in search engines

• • •

User Modelling in Personalised Systems

User modelling/profiling techniques: – Track visited pages & search history → important feature learned → more relevant information – – Simplest cases: registration form or questionnaire More complex cases: user model consists of a dynamic information structure Examples: – Google Alert: explicit approach & routing query → limited – Google Personalized Search: deliver customised search based on user profile User modelling components affect search in 3 distinct phases: – Part of retrieval process – Re-ranking – Query modification

Source of Personalisation

• • • Data mining & machine learning Relevant feedback & query expansion – Explicit relevant feedback – Implicit relevant feedback Further sources: desktop search systems

• • • • • •

An Overview on Personalisation Approaches

Current context: based on implicit feedback using client based software Search History: – Limited to web search history – Done during retrieval process → fast response Rich user models: explicit feedback → build rich representation of user needs Collaborative approach: relevant resources based on previous ratings by user with similar tastes & preferences Result clustering: results grouped into clusters, each related to same topic Hyper textual data: include additional factors in ranking algorithm

Contextual Search

• • • • A new approach for search The information system proactively suggests information based on a person’s working context Just-in-Time IR (JITIR) Rhodes

JITIR

• • • • Monitors the user’s actions Non-intrusive Automatically identify relevant information Retrieve resources automatically

Based on Agents

• • • Remembrance Agent Margin Notes Agent Jimminy Agent

Personalisation Based on Search Histories

Flight Visa Credit Card Citizenship Travel

Online Approaches

• • Capture history information as soon as they are available, affecting user models and providing personalised results taking into consideration the last interactions of the user Two different types of information are collected: – submitted queries – snippets

Offline Approaches

• • • Exploit history information in a distinct pre processing step, usually analysing relationships between queries and documents visited by users CubeSVD Algorithm based on the click through algorithm Time-consuming

Personalisation Based on Rich Representations of User Needs

Three prototypes ifWeb, Wifs, InfoWeb • • • Based on complex representations of user needs (user models) Built using explicit user feedback on results Based on frames and semantic networks (AI)

ifWeb

• • • • • User model-based intelligent agent Weighted semantic network for user profile Autonomous focused crawling to find related documents based on previously identified documents Updates user profile using user feedback Reduces the weight of unused concepts (rent)

Wifs

• • • • • Content-based approach Filters HTML and text documents from AltaVista, reordering links based on UM Frame-based user model structure

A frame has slots which contains terms (topics), associated with other terms (co-keywords), forming a semantic network

The terms are stored in a Terms DataBase that is created beforehand (by experts) Instead of traditional IR, the relevance of a document is calculated from the occurrence and relevance of terms in the document

Wifs

• • • • • Content-based approach Frame-based user model structure

A frame has slots which contains terms (topics), associated with other terms (co-keywords), forming a semantic network

The terms are stored in a Terms DataBase that is created beforehand (by experts) Filters HTML and text documents from AltaVista, reordering links based on UM Instead of traditional IR, the relevance of a document is calculated from the occurrence and relevance of terms in the document

(From Micarelli et al., 2007)

InfoWeb

• • • • • Content-based approach Adaptive retrieval of documents in digital libraries, based on Vector Space (IR) Stereotype knowledge base

Contains most significant documents for a specific category of user (domain), created beforehand (by an expert)

k-means clustering on document collection beforehand

Each cluster is seeded by a representative document for each class of user

User model starts as a stereotype, evolves based on feedback

Collaborative Search Engine

• ‘SearchParty’ module – Social filtering – Stores user queries and the results users clicked • Knowledge Sea – Social adaptive navigation system – Exploits both traditional IR and social navigation approaches – Results represented by colour lightness

Collaborative Search Engine

• Calculate similarity measures among user needs – Identified by queries, selected resources – Two queries might contain no common terms but returns similar results – E.g. ‘PDA’ and ‘handheld computer’ • Statistical model – Based on the probability a page was selected for a given query – Focus on relative frequency instead of content analysis techniques

Collaborative Search Engine

• Compass Filter – Based on web communities – Pre-processing the web structure – If user frequently visit a community, the results in the same community are boosted

Adaptive Result Clustering

• Traditional Search Engines – Rank the list by similarity of query and page – Might take a long time – Important that users clearly describe what they are looking for • Organise the results – By grouping pages into folders and sub folders – On a graphical interactive map

Adaptive Result Clustering

• Clustering – Query process needs to be fast – Usually performed after retrieval of query results – Does not require pre-defined categories – Provides concise and accurate descriptions • Further clustering systems – SnakeT – Scatter / Gather

Hyperlink-Based Personalisation

Main algorithms: • • • PageRank: PR value HubFinder: hub value HubRank: PR value & hub value

Combined Approaches to Personalisation

• • Perform personalisation using multiple adaptive approaches Outride: Browsing history & current context infoFACTORY: Integrate web tools & services

Outride

• Outride includes: Contextualisaion

Interrelated conditions that occur within an activity

• Individualisation

Characteristics that distinguish an individual

infoFACTORY

• A large set of integrated web tools and services that are able to evaluate and classify documents retrieved following a user profile • • • New Has potential Interesting

Conclusions

• • • • • • Information is crucial to users Need to filter and personalise resources to deal with information overload successfully Increases search engine accuracy and reduces time wasted sorting through irrelevant results Can be extended e.g. targeted advertising Some systems already in use, others under development (e.g. Semantic Web) Future directions: – Predicting future user behaviour (plan-recognition) – Language semantic analysis (Natural Language Processing)

Thanks for listening

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