Exploiting Tag-Based Personalization for

Recommendation on Social Web

Frederico Durão

Aalborg – Denmark13.02.2012

Advised byProf. Peter Dolog

Outline• Introduction

– Motivation, problem statement, research questions

• Literature Review– User modeling, recommender systems and search engine

• Research Overview– Intuition, model and achievements

• Conclusion and Future Works– Contributions and looking ahead

Motivation• The World Wide Web was born in the early nineties when Tim Berners-Lee had

the idea of sharing information between scientists from remote laboratories [Tim Berners-Lee and Mark Fischett, 2000].

• The Web 2.0 has instituted decentralized content creation, thus leading the Web into a more open, connected, and democratic environment [Tim O’Reilly, 2005].

• Social Web: an environment for online communication and collaboration in which user participation is the primary driver of value [Breslin, 2009].

• An underlying feature of social web applications, Collaborative Tagging allows users to assign keywords, known as TAGS, to resources on the Web such as photos, videos, and websites.

Social Tagging Applications

Tags

Tagging<user, tag, resource>

• User– You, me, a system, someone who assigns a tag to a resource;

• Tag– a word, or a set of words that describe a resource;

• Resource– text, link, bookmark, image, video, you decide;

Picture of PelePicture of MaradonaTags Tags

user

Personalized Tag-based Recommendation

Tag Amount

F1 100

Ferrari 70

BMW 50

Racing 20

Woman 2

Tag Amount

Travel 80

Beach 40

Paradise 50

Holidays 20

Work 1

Car Racing

Hawaii Trip

Party Event

Problems

• The exposure of users to both tags and resources creates the immense availability of social data on the Web.

• Coping with such amount of information becomes critical since users on the Web are heterogeneous and have distinct interests.

People are different in taste and preferences

How to make proper recommendations ?

Research Questions

• RQ1: How can we learn and rank user preferences from tagging information for personalization?

• RQ2: How can we personalize information using tag-based user profiles for social web applications?

• RQ3: How can social aspects impact the performance of tag-based personalization models for social web applications?

Literature Review

• User Modeling– An essential activity for maintaining information about the user’s knowledge, beliefs, goals,

abilities, attitudes, and preferences. [Peter Brusilovsky, 2001]

• Recommender Systems– Software tools that support users to make decisions among various alternatives by

suggesting items that could be of interest of them. [Tariq Mahmood and Francesco Ricci, 2009]

• Search Engines– Computer programs that support users to find specific piece of information within vast

collection of documents. [Ricardo Baeza-Yates and Berthier Ribeiro-Neto, 1999]

Related Work

• User Modeling

Stereotype-Based - User Models for E-Commerce – Tagging-Based Highlights:

Related Work (2)

• Recommender Systems

Collaborative Filtering - Content-Based - PageRank/FolkRank Highlights: (ratings) - (item features/tags) - (links/taggings)

Related Work (3)

• Search Engine

User Clicks - Query Log - Query Expansion - Social SearchHighlights:

Research Overview

Paper N. Title Research Question

1 A Personalized Tag-Based Recommendation in Social Web Systems 1,2

2 Extending a Hybrid Tag-based Recommender System with Personalization 2

3 A Multi-Factor Tag-Based Personalized Search 1,2

4 Social and Behavioral Aspects of a Tag-based Recommender System 3

5 Recommending Open Linked Data in Creativity Sessions using Web Portals with Collaborative Real Time Environment

2,3

6 Improving Tag-Based Recommendation with the Collaborative Value of Wiki Pages for Knowledge Sharing

1,2,3

Paper 1: A Personalized Tag-Based Recommendations in Social Web Systems

• Assumption

– Resources that share same tags

are likely to be related to each other.

• Personalization Tag-Based Model– to recommend unknown documents dmax,u for all user , which maximize the

personalized function persRec as:

• Multifactor Model

UuD

Factor N. Method Goal

1 Similarity between tags Similarity

2 Tag Popularity (Social Factor) Social Confidence

3 Tag Representativeness Representativeness

4 Affinity between User and Tag Personalization

(Tags: football, sports)

(Tags: reading, books)

(Tags: football, books)

Paper 1: A Personalized Tag-Based Recommendations in Social Web Systems

• Evaluation Goal (Qualitative Assessment of the Tag-based Recommender)– Assess the efficiency of the proposed approach by measuring the degree of satisfaction of users

about the received recommendations.

• Methodology– 38 participants from 12 countries

– Participant -> Del.icio.us account -> 10 bookmarks at least

– 5542 tags and 1143 bookmarks.

• Experiment Results

Recommendations Accepted Rejected

5/5 59% 41%

>= 3/5>= 3/5 58%58% 42%42%

Paper 2: Extending a Hybrid Tag-based Recommender System with Personalization

• Assumption– Syntax variations reduces considerably the chances of finding tag similarity.

– Example: tags “Berlin” and “Germany” are related and should be considered in the similarity calculus.

• Semantic Similarity Model – the semantic factor

– SemanticSimilarity(tag1,tag2) = WordNet(tag1,tag2) x Ontology(tag1, tag2)

• Example

SemanticSimilarity(Berlin,Germany)

• Semantic Group Achievements

• Evaluation / Comparison with previous study

• Example of tag sets similarity calculus

WordNet Ontologies Both

45% 24% 31%

Precision Rates

Recommendations Accepted Items Rejected Items

Pure Syntax-Based 70% 57%

+ Semantic Factor 77% 40%

Paper 2: Extending a Hybrid Tag-based Recommender System with Personalization

Paper 3: A Multi-Factor Tag-Based Personalized Search

• Assumption– Multiples indicators of user preference

can better determine what is more or less

relevant than a single one.

• Multi-factor Personalized Search Model– Tag Frequency

– Weighing Factor Example:• One factor can be more important that the other.

– Personalized Search Score (PSS)

Factor Weigh

User’s personal tags (1) 0.6Tags assigned to favorite pages (2) 0.3

Tags assigned to frequent visited pages (3)

0.1

(1)(2)

(3)

Paper 3: A Multi-Factor Tag-Based Personalized Search

• Evaluation

– MovieLens dataset: 1,147,048 ratings and 95,580 tags applied to 10,681 movies by 13,190 users.

– Comparison of our approach against 6 similarity methods:• Cosine Similarity, Matching Coefficient, Dice, Jaccard and Euclidean Distance

• Results

61.6% of precision improvement over traditional text-based informationretrieval (non-pers)

and

6,13% of precision gain over cosine similarity, the best method compared

Paper 4: Social and Behavioral Aspects of a Tag-based Recommender System

• Exploratory Research

– Which social issues interfered in the performance recommendations ?

– Focus on user’s tagging behavior.

• Question 1: Why correctly generated

recommendations were rejected?

– Low novelty. Items already known.

– Items not really interesting.

– Recommendation written in a language “x” but tagged in English.• Therefore, recommendations are correctly generated.• Ex: Some Danish rejected recommendations written in Chinese because they simply could not read them.

Paper 4: Social and Behavioral Aspects of a Tag-based Recommender System

• Question 2: How is your purpose while tagging?

• Question 3: Do you tag only for self understating?

Social means: use of popular terms

Social means: use of popular terms

Social tags facilitate Tag-based recommendations

Social tags facilitate Tag-based recommendations

Paper 5: Recommending Open Linked Data in Creativity Sessions Using Web Portals with Collaborative Real Time Environment

• Assumption– Semantic-based recommendations

can support participants in a brainstorming sessions.

• Method– Searching semantic relations on the from Linked Open Data on the Web.

– Adapting to a creative technique 5W1H - When, Where, Who, What, Why and How

Interrogatives

What

When

Who

Where

How

Ontology Property

:type

:hasYear

:foaf

:stateOf

:toDo

Interrogative – Property Mapping

Suggestions for

“Desert”

Camel

Sahara

Dunes

Paper 5: Recommending Open Linked Data in Creativity Sessions Using Web Portals with Collaborative Real Time Environment

• Evaluation– Comparison of groups (A, B, C) without semantic recommendations against groups with the

support of recommendations (D, E, F).

• Results

– On average: 55,9% of the recommendations were rated with highest ratings (4-5) whereas

44,1% of the recommendations were rated with lowest ratings (1-2-3).

– User comments:

Paper 6: Improving Tag-Based Recommendation with the Collaborative Value of Wiki Pages for Knowledge Sharing

• Assumption– The collaborative value of wiki pages (such as Wikipedia) reflects its knowledge sharing capacity.

– We compute this social commitment and used it as ranking factor.

– Intended for problem solving.

• Method: WPCV - Wiki Page Collaborative Value.

- User Knowledge

- User Interactivity

Wiki Page1 Wiki Page 2

Silvio Berlusconi

Obama

Nelson Mandela

Fred

Which page should be recommended?

- You knowledge increases the more interact with your friends.

- Everything you write, read, tag, comment increases UK.

Paper 6: Improving Tag-Based Recommendation with the Collaborative Value of Wiki Pages for Knowledge Sharing

• Evaluation Methodology– 63 participants using a semantic wiki solve a given task:

• To fill out incomplete wiki pages by collecting information placed in other pages of the system.

– They were required to navigate through the pages using our recommendations to find the needed information.

• Two set of recommendations were provided: one powered by WPCV and the other purely tag-based recommendation.

• Results• Qualitative Assessment

• Subjective Assessment

Improvements of precision, recall and f-measure at rates of 11%, 7% and 12% against PTB.

user participation over time (30 days)

Conclusion• RESEARCH QUESTION 1

– Tag-based models to capture and rank user preferences by observing user activity including tagging, searching, rating, commenting and social networking.

• RESEARCH QUESTION 2– Multi-factor personalization model to generate user – oriented information from

tag-based user profiles.

• RESEARCH QUESTION 3– Analyses of social and behavioral aspects that harm the effectiveness of

recommendations.

Future Works• Flexible user models that allow individuals to interact with personalization

systems.

• Use of a time decay factor for adjusting user’s preference over time.

• Recommendations should invest on diversity to avoid redundancy.

• Attenuate the new user problem by moving towards a hybrid approaches.

• Personalization is never dissociated of criticism since it invades the user’s privacy.

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Thanks

• To my family,

• To my supervisor,

• To the PhD committee members,

• To my MI friends,

• To my IS friends,

• To my KiWi friends,

• To my all Casiopeia personnel,

• To my always helpful secretaries,

• To my work colleagues,

• To my close friends in Aalborg,

• To my M-Eco friends,

• To your patience with me

Publications

• Nothing more than my obligation

• http://vbn.aau.dk/en/persons/frederico-durao(f26eaca1-ec6a-4315-85d4-9c38fa167956)/publications.html

Tag-Based Personalization

1. Tagged resources and user tag-based profile are collected.

2. Resource tags are compared against user tag-based profile.

3. Tag-based personalized are generated.