Personalizing Web Page Recommendation via Collaborative Filtering and

Topic-Aware Markov Model

Qingyan Yang, Ju Fan, Jianyong Wang, Lizhu Zhou

Database Research Group, DCS&T, Tsinghua University

Motivation

Recommender framework

Experimental evaluation

Conclusions

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AgendAgendaa

Motivation

Recommender framework

Experimental evaluation

Conclusions

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• The Web is explosively growing▪By the end of 2009 (source: the 25th Internet Report, 2010)

◦ 33,600,000,000 Web pages in China◦ Twice as many as that in 2003

• Finding desired information is more difficult.▪Users often wander aimless on the Web without

visiting pages of his/her interests▪Or spend a long time on finding the expected

information.

MotivationMotivation

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Web page recommendation

• Objective ▪To understand users' navigation behavior▪To show some pages of users' interests at a

specific time• Existing popular solutions

▪Markov model and its variants▪Temporal relation is important.

Web page recommendationWeb page recommendation

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If the browsing sequence is "A B C … A B C … A B C", Then C is recommended when A and B are visited one after another

• No personalized recommendations▪All users receive the same results

• Topic information of pages is neglected.▪Two pages, which are sequentially visited, may be

very different in terms of topics.

Limitations Limitations

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• Personalized Web page recommendation• Two novel features

▪Personalization◦ Meet preference of different users

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PIGEON: our solutionPIGEON: our solution

I am a blog about finance

• Two novel features▪Personalization▪Topical coherence

◦ To be relevant to users' present missions

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PIGEON: our solutionPIGEON: our solution

Motivation

Recommender framework

Experimental evaluation

Conclusions

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Recommender frameworkRecommender framework

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Data representationData representation• Navigation graph

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Time User ID IP address Target Source

(09:44:44) (0e0c…) (211.90.-.-) A ()

(09:44:58) (0e0c…) (211.90.-.-) B A

(10:14:29) (0e0c…) (211.90.-.-) G A

2

1

32

2 2

1

4 2 62

1

A

B

C

D

E

F

G

H I J

K

L

MWeb page

Edge: jump relation

Weight: relation frequency

Jump relation

Topic discoveryTopic discovery• Basic idea

▪We assume that pages with similar URLs or evolved in jump relations are topically relevant.

• URLs Features ▪Keywords. e.g., http://dblp.uni-trier.de/db/index.html

▪Expanded by Manifold-based keyword propagation

• Web page clustering▪Each cluster represents one topic

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Example Example

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2

1

32

2 2

1

4 2 6

2

1

A

B

C

D

E

F

G

H I J

K

L

M

Topic-Aware Markov ModelTopic-Aware Markov Model• Take n-grams as states. e.g., n=2

• Web page preference score▪Maximum likelihood estimation▪e.g., P(D|BC) = f(BCD)/f(BC) = 1/2

A B C D B C AAB BC CD DB CA

A C C A, B D BAB BC CD AC CC CADB CA BD DB Topical stateTemporal

state

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A B C D B C A

Personalized RecommenderPersonalized Recommender• Collaborative filtering

▪Basic idea

~s(u;p) = kXu0

sim(u;u0)s(u0;p)~s(u;p) = kXu0

sim(u;u0)s(u0;p) u : active user; p : Webpageu : active user; p : Webpage

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user similarities

Web page preference

User SimilarityUser Similarity• User profile

▪A set of topics• Similarity measurement

▪Topic similarity▪Maximum weight matching

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sim(u1;u2) = 0:9+ 0:8+ 1:03 = 0:9sim(u1;u2) = 0:9+ 0:8+ 1:03 = 0:9

0.9

0.81.0

Motivation

Recommender framework

Experimental evaluation

Conclusions

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Experiment settingsExperiment settings• Data set

▪1,402,371 records of 375 users in 34 days▪First 30 days for training and 4 days for testing

• Metrics are precision and recall• Comparative methods

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Temporal Topical Personalized

Baseline Y

TAMM Y Y

PIGEON Y Y Y

Experimental evaluationExperimental evaluation

1st-order model 2nd-order model

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Motivation

Recommender framework

Experimental evaluation

Conclusions

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ConclusionsConclusions

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• Taking user similarities into account, we could recommend Web pages to meet different users' preferences.

• We discover users' interested topics using an effective graph-based clustering algorithm.

• We devise a topic-aware Markov model to learn navigation patterns which contribute to the topically coherent recommendations.

THANKS THANKS

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