Adaptive News AccessAdaptive News Access

Daniel BillsusDaniel Billsusbillsus@fxpal.combillsus@fxpal.com

Presented by Chirayu WongchokprasittiPresented by Chirayu Wongchokprasitti

IntroductionIntroduction

WWW is a common source for news WWW is a common source for news access anywhere and anytime.access anywhere and anytime.

The availability of updated news The availability of updated news content recently overloads uscontent recently overloads us

Adaptive web technology can help Adaptive web technology can help discovering relevant content from discovering relevant content from thousands of sources. thousands of sources.

Types of Adaptive News Types of Adaptive News AccessAccess

News PersonalizationNews Personalization Adaptive News NavigationAdaptive News Navigation Contextual RecommendationsContextual Recommendations News AggregationNews Aggregation

News PersonalizationNews Personalization Dynamic contentDynamic content

News stories are released and updated News stories are released and updated continuouslycontinuously

Content-based methods fit to news Content-based methods fit to news personalizationpersonalization

Content-based methods predict user’s interest Content-based methods predict user’s interest based on text alonebased on text alone

Changing interestsChanging interests User’s interests tend to change frequently.User’s interests tend to change frequently. A user model can adjust its interests quickly.A user model can adjust its interests quickly. The techniques of changing target concepts is The techniques of changing target concepts is

known as known as concept driftconcept drift..

News Personalization (cont.)News Personalization (cont.) Multiple interestsMultiple interests

Users are interested in different news topicsUsers are interested in different news topics A user model must be capable of A user model must be capable of

representing multiple interestsrepresenting multiple interests The k-nearest-neighbor methods (kNN) are The k-nearest-neighbor methods (kNN) are

good choices.good choices. NoveltyNovelty

A new “A new “unknownunknown” story is considered most ” story is considered most interesting.interesting.

A new story too close to what user previously A new story too close to what user previously accessed is classified as a “accessed is classified as a “knownknown” story.” story.

News Personalization (cont.)News Personalization (cont.) Avoiding tunnel visionAvoiding tunnel vision

Personalization should not get in the way of Personalization should not get in the way of finding important novel information.finding important novel information.

Editorial inputEditorial input A user model ranks stories by a prediction A user model ranks stories by a prediction

function.function. Retaining editorial input is an important Retaining editorial input is an important

feature for news organizations.feature for news organizations. To ensure users will get to see the top To ensure users will get to see the top nn

stories.stories.

News Personalization (cont.)News Personalization (cont.)

BrittlenessBrittleness A single action, with or without A single action, with or without

intention, should not have a radical intention, should not have a radical effect on a user model.effect on a user model.

Availability of meta-tagsAvailability of meta-tags News personalization algorithms can News personalization algorithms can

usually not rely on the availability of usually not rely on the availability of meta-tags.meta-tags.

News Personalization (cont.)News Personalization (cont.)

Adaptive News NavigationAdaptive News Navigation The objective is to simplify access to The objective is to simplify access to

relevant content.relevant content. This technique focuses on analyzing This technique focuses on analyzing

user’s access patterns to determine user’s access patterns to determine the position of menu items within a the position of menu items within a menu hierarchy.menu hierarchy.

This approach is suitable to mobile This approach is suitable to mobile applications due to limited screen applications due to limited screen space.space.

Adaptive News NavigationAdaptive News Navigation (cont.)(cont.)

On average, the number of selected On average, the number of selected menu and scroll operations was menu and scroll operations was reduced by over 50%. reduced by over 50%.

However, this approach does not However, this approach does not provide any news provide any news recommendations.recommendations.

Contextual Contextual RecommendationsRecommendations

An approach draws on currently displayed An approach draws on currently displayed information on the screen as an information on the screen as an expression of the user’s current interests.expression of the user’s current interests.

The system extracts textual information The system extracts textual information on the user’s screen and the extracted on the user’s screen and the extracted text is used to retrieve related content.text is used to retrieve related content.

Statistical term-weighting techniques are Statistical term-weighting techniques are used to identify informative terms.used to identify informative terms.

BlinkxBlinkx is a publicly available contextual is a publicly available contextual recommender (recommender (http://http://www.blinkx.comwww.blinkx.com).).

Contextual Recommendations Contextual Recommendations (cont.)(cont.)

News AggregationNews Aggregation News aggregators are services that News aggregators are services that

aggregate content from many news aggregate content from many news sources, and then adapt to the current sources, and then adapt to the current news landscape as a whole.news landscape as a whole.

The services use The services use RSS RSS (Rich Site (Rich Site Summary)Summary) feeds to provide links to feeds to provide links to available content.available content.

A news aggregation implementation can A news aggregation implementation can use statistical term-weighting and text use statistical term-weighting and text similarity techniques.similarity techniques.

Google News (Google News (http://http://news.google.comnews.google.com) is ) is one of these services.one of these services.

News AggregationNews Aggregation (cont.)(cont.)

Case StudyCase Study

Adaptive News Personalization for Adaptive News Personalization for Mobile Content AccessMobile Content Access

Learning User Models for News Learning User Models for News AccessAccess

EvaluationEvaluation

Adaptive News Personalization Adaptive News Personalization for Mobile Content Accessfor Mobile Content Access

The constraints of mobile information The constraints of mobile information access make personalization access make personalization important to produce usable important to produce usable applications.applications.

A news system in mobile A news system in mobile personalizes the orders of news personalizes the orders of news sections the most relevant stories sections the most relevant stories are displayed on the topmostare displayed on the topmost

Adaptive News Personalization Adaptive News Personalization for Mobile Content Accessfor Mobile Content Access

(cont.)(cont.)

Learning User Models for News Learning User Models for News AccessAccess

The system uses a machine learning The system uses a machine learning approach to build a simple model of approach to build a simple model of each user’s interests.each user’s interests.

A combination of similarity-based A combination of similarity-based methods and Bayesian methods methods and Bayesian methods achieves the balance of learning and achieves the balance of learning and adapting quickly to change interests adapting quickly to change interests while avoiding brittleness.while avoiding brittleness.

Learning User Models for News Learning User Models for News AccessAccess (cont.)(cont.)

These two algorithms form a multi-These two algorithms form a multi-strategy learning approach to learn strategy learning approach to learn two separate user models.two separate user models. Short-term interests user modelShort-term interests user model Long-term interests user modelLong-term interests user model

Learning User Models for News Learning User Models for News AccessAccess (cont.)(cont.)

The purpose of the short-term modelThe purpose of the short-term model First, it should contain information about First, it should contain information about

recently read events, so that stories which recently read events, so that stories which belong to the same thread can be identified.belong to the same thread can be identified.

To allow for identification of stories that user To allow for identification of stories that user already knows.already knows.

The The k-nearest-neighbork-nearest-neighbor algorithm ( algorithm (kNNkNN) is ) is used to achieve the desired functionality.used to achieve the desired functionality. Convert news stories to Convert news stories to tf-idftf-idf vectors (term- vectors (term-

frequency/inverse-document-frequency).frequency/inverse-document-frequency). Use the cosine similarity measure to quantify Use the cosine similarity measure to quantify

the similarity of two vectors.the similarity of two vectors.

Learning User Models for News Learning User Models for News AccessAccess (cont.)(cont.)

The purpose of the long-term model is to The purpose of the long-term model is to model a user’s general preferences.model a user’s general preferences.

The system periodically selects informative The system periodically selects informative words for each news category from a large words for each news category from a large sample of stories.sample of stories.

The goal of the feature selection process is The goal of the feature selection process is to select informative words that reoccur to select informative words that reoccur over a long period of time.over a long period of time.

A naA naïïve Bayesian classifier is used to assess ve Bayesian classifier is used to assess the probability of stories being interesting. the probability of stories being interesting.

Learning User Models for News Learning User Models for News AccessAccess (cont.)(cont.)

EvaluationEvaluation

They summarize the results from two They summarize the results from two studies that compare personalization studies that compare personalization information access to static one.information access to static one.

First, the “alternating sessions” experiment First, the “alternating sessions” experiment quantifies the difference between static quantifies the difference between static and adaptive information accessand adaptive information access A half of users used its user modeling approach.A half of users used its user modeling approach. The other half received news in static order The other half received news in static order

from the source.from the source.

EvaluationEvaluation (cont.)(cont.)

The average display rank of selected The average display rank of selected stories was 6.7 in the static mode and 4.2 stories was 6.7 in the static mode and 4.2 in the adaptive mode (based on 50 users in the adaptive mode (based on 50 users that selected 340 stories out of 1882 that selected 340 stories out of 1882 headlines).headlines).

The analysis of the distribution of selected The analysis of the distribution of selected stories.stories. In the static mode, 68.7% of the selected In the static mode, 68.7% of the selected

stories on the top two headline screensstories on the top two headline screens In the adaptive mode, 86.7% on the top twoIn the adaptive mode, 86.7% on the top two

Evaluation (cont.)Evaluation (cont.)

EvaluationEvaluation (cont.)(cont.)

Second, the “alternating stories” Second, the “alternating stories” experiment displays stories selected experiment displays stories selected with respect to both the adaptive and with respect to both the adaptive and static modes on the same screen.static modes on the same screen.

Advantages:Advantages: The system still adapts to user’s interests.The system still adapts to user’s interests. Allow a direct comparison between the Allow a direct comparison between the

two selection strategies.two selection strategies.

EvaluationEvaluation (cont.)(cont.) The difference was not as pronounced as in the The difference was not as pronounced as in the

“alternating sessions” experiment.“alternating sessions” experiment. The average display rank of selected stories was The average display rank of selected stories was

5.8 in the static mode and 5.27 in the adaptive 5.8 in the static mode and 5.27 in the adaptive mode.mode.

The analysis of the distribution of selected stories.The analysis of the distribution of selected stories. In the static mode, 75.57%In the static mode, 75.57% In the adaptive mode, 80.44%In the adaptive mode, 80.44%

Users are more likely to select adaptive stories Users are more likely to select adaptive stories (19.02%) than static ones (13.26%) which (19.02%) than static ones (13.26%) which amounts to a 43.44% increase in selected content.amounts to a 43.44% increase in selected content.

Evaluation (cont.)Evaluation (cont.)

EvaluationEvaluation (cont.)(cont.) In summary, the “In summary, the “alternating sessionsalternating sessions” and ” and

““alternating storiesalternating stories” experiments show adaptive ” experiments show adaptive information access is higher than static access.information access is higher than static access.

The “The “alternating sessionsalternating sessions” experiment showed ” experiment showed adaptive order helps shifting interesting stories adaptive order helps shifting interesting stories towards the beginning of personalized lists.towards the beginning of personalized lists.

The “The “alternating storiesalternating stories” experiment showed the ” experiment showed the system is capable of ordering content that the system is capable of ordering content that the top-ranked items have a significantly higher top-ranked items have a significantly higher chance to be selected that the ranked static chance to be selected that the ranked static ones.ones.

Recent Trends and SystemsRecent Trends and Systems PodcastingPodcasting

Online audio distribution of news content.Online audio distribution of news content. Collaborative filtering techniques is applicable Collaborative filtering techniques is applicable

to podcast recommendation.to podcast recommendation. Personalization and the BlogospherePersonalization and the Blogosphere

Blogosphere refers to the set of all webblogs.Blogosphere refers to the set of all webblogs. Some systems support personalized blog Some systems support personalized blog

access such as access such as Findory.comFindory.com, , NewsGator.comNewsGator.com.. News ZeitgeistNews Zeitgeist

Zeitgeist is a German word that means “the Zeitgeist is a German word that means “the spirit (spirit (GeistGeist) of the time () of the time (ZeitZeit)”.)”.

The goal is to automatically identify the most The goal is to automatically identify the most popular topics of the current Blogosphere.popular topics of the current Blogosphere.

Conclusions & ReferencesConclusions & References

We need new technology to help leverage We need new technology to help leverage the full potential web-based news the full potential web-based news distribution.distribution.

[1] Billsus, D. (2005). Adaptive News Access[1] Billsus, D. (2005). Adaptive News Access[2] Billsus, D., & Pazzani, M. (2000). User Modeling [2] Billsus, D., & Pazzani, M. (2000). User Modeling

for Adaptive News Access. for Adaptive News Access. User Modeling and User Modeling and User-Adapted InteractionUser-Adapted Interaction, 10(2/3): 147-180., 10(2/3): 147-180.

[3] Chiu, B. & Webb, G. (1998) Using decision trees [3] Chiu, B. & Webb, G. (1998) Using decision trees for agent modeling: improving prediction for agent modeling: improving prediction performance. performance. User Modeling and User-Adapted User Modeling and User-Adapted InteractionInteraction, 8, 131-152., 8, 131-152.

Questions or Comments?Questions or Comments?