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

04/18/23 2DB Group, DCS&T, Tsinghua University

AgendAgendaa

Motivation

Recommender framework

Experimental evaluation

Conclusions

04/18/23 3DB Group, DCS&T, Tsinghua University

• 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

04/18/23 404/18/23 4DB Group, DCS&T, Tsinghua University

04/18/23 DB Group, DCS&T, Tsinghua University 5

Web page recommendation

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

04/18/23 604/18/23 6DB Group, DCS&T, Tsinghua University

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

04/18/23 DB Group, DCS&T, Tsinghua University 7

• Personalized Web page recommendation• Two novel features

▪Personalization◦ Meet preference of different users

04/18/23 DB Group, DCS&T, Tsinghua University 8

PIGEON: our solutionPIGEON: our solution

I am a blog about finance

• Two novel features▪Personalization▪Topical coherence

◦ To be relevant to users' present missions

04/18/23 DB Group, DCS&T, Tsinghua University 9

PIGEON: our solutionPIGEON: our solution

Motivation

Recommender framework

Experimental evaluation

Conclusions

04/18/23 10DB Group, DCS&T, Tsinghua University

Recommender frameworkRecommender framework

04/18/23 DB Group, DCS&T, Tsinghua University 11

Data representationData representation

• Navigation graph

04/18/23 DB Group, DCS&T, Tsinghua University 12

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 6

2

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

04/18/23 DB Group, DCS&T, Tsinghua University 13

Example Example

04/18/23 DB Group, DCS&T, Tsinghua University 14

2

1

32

22

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 A

AB BC CD DB CA

A C C A, B D B

AB BC CD AC CC CADB CA BD DB Topical stateTemporal

state

04/18/23 15DB Group, DCS&T, Tsinghua University

A B C D B C A

Personalized RecommenderPersonalized Recommender

• Collaborative filtering▪Basic idea

~s(u;p) = kX

u0

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

u0

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

04/18/23 16DB Group, DCS&T, Tsinghua University

user similarities

Web page preference

User SimilarityUser Similarity

• User profile▪A set of topics

• Similarity measurement▪Topic similarity▪Maximum weight matching

04/18/23 17DB Group, DCS&T, Tsinghua University

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

3= 0:9sim(u1;u2) =

0:9+ 0:8+ 1:03

= 0:9

0.9

0.81.0

Motivation

Recommender framework

Experimental evaluation

Conclusions

04/18/23 18DB Group, DCS&T, Tsinghua University

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

04/18/23 19DB Group, DCS&T, Tsinghua University

Temporal Topical Personalized

Baseline Y

TAMM Y Y

PIGEON Y Y Y

Experimental evaluationExperimental evaluation

1st-order model 2nd-order model

04/18/23 20DB Group, DCS&T, Tsinghua University

Motivation

Recommender framework

Experimental evaluation

Conclusions

04/18/23 21DB Group, DCS&T, Tsinghua University

ConclusionsConclusions

04/18/23 DB Group, DCS&T, Tsinghua University 22

• 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

04/18/23 DB Group, DCS&T, Tsinghua University 23