Web Search Personalization with Ontological User Profile Advisor: Dr. Jai-Ling Koh Speaker: Shun-hong Sie.

Slides:

Advertisements

Similar presentations

Haystack: Per-User Information Environment 1999 Conference on Information and Knowledge Management Eytan Adar et al Presented by Xiao Hu CS491CXZ.

Advertisements

Chapter 5: Introduction to Information Retrieval

1 Evaluation Rong Jin. 2 Evaluation  Evaluation is key to building effective and efficient search engines usually carried out in controlled experiments.

Query Chains: Learning to Rank from Implicit Feedback Paper Authors: Filip Radlinski Thorsten Joachims Presented By: Steven Carr.

Explorations in Tag Suggestion and Query Expansion Jian Wang and Brian D. Davison Lehigh University, USA SSM 2008 (Workshop on Search in Social Media)

Personalizing Search via Automated Analysis of Interests and Activities Jaime Teevan Susan T.Dumains Eric Horvitz MIT,CSAILMicrosoft Researcher Microsoft.

6/16/20151 Recent Results in Automatic Web Resource Discovery Soumen Chakrabartiv Presentation by Cui Tao.

Mobile Web Search Personalization Kapil Goenka. Outline Introduction & Background Methodology Evaluation Future Work Conclusion.

Gimme’ The Context: Context- driven Automatic Semantic Annotation with CPANKOW Philipp Cimiano et al.

Retrieval Evaluation. Brief Review Evaluation of implementations in computer science often is in terms of time and space complexity. With large document.

Personalised Search on the World Wide Web Originally by Micarelli, Gasparetti, Sciarrone & Gauch

Retrieval Evaluation: Precision and Recall. Introduction Evaluation of implementations in computer science often is in terms of time and space complexity.

Evaluating the Performance of IR Sytems

Retrieval Evaluation. Introduction Evaluation of implementations in computer science often is in terms of time and space complexity. With large document.

University of Kansas Department of Electrical Engineering and Computer Science Dr. Susan Gauch April 2005 I T T C Dr. Susan Gauch Personalized Search Based.

An investigation of query expansion terms Gheorghe Muresan Rutgers University, School of Communication, Information and Library Science 4 Huntington St.,

Xiaomeng Su & Jon Atle Gulla Dept. of Computer and Information Science Norwegian University of Science and Technology Trondheim Norway June 2004 Semantic.

Personalized Ontologies for Web Search and Caching Susan Gauch Information and Telecommunications Technology Center Electrical Engineering and Computer.

Disscussion about the FIPA Interaction Protocols FIPA IP Technical Committee (IP-TC) Gabriel Hopmans Morpheus Software Maastricht, the Netherlands.

Quality-aware Collaborative Question Answering: Methods and Evaluation Maggy Anastasia Suryanto, Ee-Peng Lim Singapore Management University Aixin Sun.

Challenges in Information Retrieval and Language Modeling Michael Shepherd Dalhousie University Halifax, NS Canada.

Personalization in Local Search Personalization of Content Ranking in the Context of Local Search Philip O’Brien, Xiao Luo, Tony Abou-Assaleh, Weizheng.

A Recommendation Mechanism for Contextualized mobile advertising S.-T. Yuan et al., Expert Systems with Applications, vol. 24, no. 4, pp , 2003.

SEEKING STATEMENT-SUPPORTING TOP-K WITNESSES Date: 2012/03/12 Source: Steffen Metzger (CIKM’11) Speaker: Er-gang Liu Advisor: Dr. Jia-ling Koh 1.

1 Context-Aware Search Personalization with Concept Preference CIKM’11 Advisor ： Jia Ling, Koh Speaker ： SHENG HONG, CHUNG.

Leveraging Conceptual Lexicon ： Query Disambiguation using Proximity Information for Patent Retrieval Date : 2013/10/30 Author : Parvaz Mahdabi, Shima.

Practical Learning Design in a Future Learning Object Economy Tore Hoel Norwegian eStandard project Edinburgh, Oct 23rd 2003.

Reyyan Yeniterzi Weakly-Supervised Discovery of Named Entities Using Web Search Queries Marius Pasca Google CIKM 2007.

Dr. Susan Gauch When is a rock not a rock? Conceptual Approaches to Personalized Search and Recommendations Nov. 8, 2011 TResNet.

A Simple Unsupervised Query Categorizer for Web Search Engines Prashant Ullegaddi and Vasudeva Varma Search and Information Extraction Lab Language Technologies.

Xiaoying Gao Computer Science Victoria University of Wellington Intelligent Agents COMP 423.

1 A Unified Relevance Model for Opinion Retrieval (CIKM 09’) Xuanjing Huang, W. Bruce Croft Date: 2010/02/08 Speaker: Yu-Wen, Hsu.

UOS 1 Ontology Based Personalized Search Zhang Tao The University of Seoul.

Exploring Online Social Activities for Adaptive Search Personalization CIKM’10 Advisor ： Jia Ling, Koh Speaker ： SHENG HONG, CHUNG.

Mining the Web to Create Minority Language Corpora Rayid Ghani Accenture Technology Labs - Research Rosie Jones Carnegie Mellon University Dunja Mladenic.

WEB SEARCH PERSONALIZATION WITH ONTOLOGICAL USER PROFILES Data Mining Lab XUAN MAN.

Clustering Personalized Web Search Results Xuehua Shen and Hong Cheng.

Giorgos Giannopoulos (IMIS/”Athena” R.C and NTU Athens, Greece) Theodore Dalamagas (IMIS/”Athena” R.C., Greece) Timos Sellis (IMIS/”Athena” R.C and NTU.

Personalized Search Xiao Liu

CONCLUSION & FUTURE WORK Normally, users perform search tasks using multiple applications in concert: a search engine interface presents lists of potentially.

Binxing Jiao et. al (SIGIR ’10) Presenter : Lin, Yi-Jhen Advisor: Dr. Koh. Jia-ling Date: 2011/4/25 VISUAL SUMMARIZATION OF WEB PAGES.

Contextual Ranking of Keywords Using Click Data Utku Irmak, Vadim von Brzeski, Reiner Kraft Yahoo! Inc ICDE 09’ Datamining session Summarized.

IR Theory: Relevance Feedback. Relevance Feedback: Example  Initial Results Search Engine2.

Enhancing Cluster Labeling Using Wikipedia David Carmel, Haggai Roitman, Naama Zwerdling IBM Research Lab (SIGIR’09) Date: 11/09/2009 Speaker: Cho, Chin.

Knowledge based Personalization by Wonjung Kim. Outline Introduction Background – InfoQuilt system Personalization in InfoQuilt Related Work Conclusions.

Personalized Interaction With Semantic Information Portals Eric Schwarzkopf DFKI

Date : 2013/03/18 Author : Jeffrey Pound, Alexander K. Hudek, Ihab F. Ilyas, Grant Weddell Source : CIKM’12 Speaker : Er-Gang Liu Advisor : Prof. Jia-Ling.

Of 33 lecture 1: introduction. of 33 the semantic web vision today’s web (1) web content – for human consumption (no structural information) people search.

© 2003 Ontopia AS 1 The TAO of Topic Maps An Introduction to Topic Maps, Ontologies, and Published Subjects Steve Pepper, CEO,

Mining Dependency Relations for Query Expansion in Passage Retrieval Renxu Sun, Chai-Huat Ong, Tat-Seng Chua National University of Singapore SIGIR2006.

What Does the User Really Want ? Relevance, Precision and Recall.

Post-Ranking query suggestion by diversifying search Chao Wang.

Learning in a Pairwise Term-Term Proximity Framework for Information Retrieval Ronan Cummins, Colm O’Riordan Digital Enterprise Research Institute SIGIR.

PERSONALIZED DIVERSIFICATION OF SEARCH RESULTS Date: 2013/04/15 Author: David Vallet, Pablo Castells Source: SIGIR’12 Advisor: Dr.Jia-ling, Koh Speaker:

Identifying “Best Bet” Web Search Results by Mining Past User Behavior Author: Eugene Agichtein, Zijian Zheng (Microsoft Research) Source: KDD2006 Reporter:

Hybrid Content and Tag-based Profiles for recommendation in Collaborative Tagging Systems Latin American Web Conference IEEE Computer Society, 2008 Presenter:

Personalizing Web Search Jaime Teevan, MIT with Susan T. Dumais and Eric Horvitz, MSR.

Predicting Short-Term Interests Using Activity-Based Search Context CIKM’10 Advisor: Jia Ling, Koh Speaker: Yu Cheng, Hsieh.

An Adaptive User Profile for Filtering News Based on a User Interest Hierarchy Sarabdeep Singh, Michael Shepherd, Jack Duffy and Carolyn Watters Web Information.

Doctoral Thesis Presentation Mohammed Nazim Uddin Dept. of Computer Science & Information Engineering, INHA University, Korea Advisor: Professor Geun-Sik.

1 Personalizing Search via Automated Analysis of Interests and Activities Jaime Teevan, MIT Susan T. Dumais, Microsoft Eric Horvitz, Microsoft SIGIR 2005.

Ontology Engineering and Feature Construction for Predicting Friendship Links in the Live Journal Social Network Author:Vikas Bahirwani 、 Doina Caragea.

Personalized Ontology for Web Search Personalization S. Sendhilkumar, T.V. Geetha Anna University, Chennai India 1st ACM Bangalore annual Compute conference,

Designing Cross-Language Information Retrieval System using various Techniques of Query Expansion and Indexing for Improved Performance Hello everyone,

Associative Query Answering via Query Feature Similarity

Learning Literature Search Models from Citation Behavior

Date : 2013/1/10 Author : Lanbo Zhang, Yi Zhang, Yunfei Chen

Date: 2012/11/15 Author: Jin Young Kim, Kevyn Collins-Thompson,

Information Retrieval and Web Design

Introduction to Search Engines

Presentation transcript:

Web Search Personalization with Ontological User Profile Advisor: Dr. Jai-Ling Koh Speaker: Shun-hong Sie

Outline Introduction Web & Search personalization Experimental Conclusion

Terminology Query – A search query that comprises of one or more keywords. Context – The representation of a user’s intent for information seeking. Ontology – Explicit specification of concepts and relationships that can exist between them.

How do we begin search? 野草莓是怎麼一回事？野草莓、圓山、學運

For search result Query or localized context Domain knowledge Long-term interests

Motivation Combine search technologies and knowledge about query and user context into a single framework in order to provide the most appropriate answer for a user’s information needs.

Personalized Search Google (or the other search engine like?!) – Feedback – Re-ranking – Snippets associated – Time consuming

Ontologies for web personalization Ontological user profile – Concept are annotated by interest scores derived and updated implicitly on the user’s information access behavior.

Ontologies for web personalization Cold-start Match exist concept Collect use behavior Build user’s ontology Maintain and update

Representation of Reference Ontology N:total number of document in the training set n i total number of documents that contain term i n: concept

Context Model conceptscore

Spreading Activation module Assume user behavior can be learned Compute the weights for the relations between each concept and all of its subconcepts using a measure of containment Computer a term vector for each document d i and compare the term vector for d i with the term vectors for concept C j in the user profile.

Algorithm 1: Spreading Activation Algorithm

Algorithm 2 Algorithm for the Normalization and Updating of Interest Scores in the Ontological User Profile

Search Personalization

Algorithm 3: Re-ranking Algorithm

Experimental Top-n Recall Top-n Precision Data Sets – ODP Training set test set profile set

Experimental evaluation User profile convergence – Rate of increase in interest scores stabilizes over incremental updates. Effectiveness of search personalization

User profile convergence

Average Top-n Recall and Top-n Precision comparisons

Standard search with various query sizes

CONCLUSIONS Use personal ontology can be used to effectively tailor search results based on users’ interests and preferences.

The Norwegian National Knowledge Base SNL Skien coun- cil Cap Lex NBL Henrik Ibsen Hedda Gabler Skien Et dukkehjem A doll’s house wrote born in wrote “reality” topic mapinformation knowledge other topic maps are merged in... Ibsen- centre Et dukkehjem Helmer Dr. Rank Mrs. Linde Krogstad Nora © 2003 Ontopia AS

Summary of Core Topic Maps Concepts A pool of information or data –any type or format A knowledge layer, consisting of: knowledge layer information layer Associations –expressing relationships between knowledge topics composed by born in composed by Occurrences –information that is relevant in some way to a given knowledge topic = The TAO of Topic Maps Topics –a set of knowledge topics for the domain in question Puccini Tosca Lucca Madame Butterfly