Adaptive News Access

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Adaptive News Access

• Daniel Billsus
• billsus_at_fxpal.com
• Presented by Chirayu Wongchokprasitti

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Introduction

• WWW is a common source for news access anywhere
  and anytime.
• The availability of updated news content recently
  overloads us
• Adaptive web technology can help discovering
  relevant content from thousands of sources.

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Types of Adaptive News Access

• News Personalization
• Adaptive News Navigation
• Contextual Recommendations
• News Aggregation

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News Personalization

• Dynamic content
• News stories are released and updated
  continuously
• Content-based methods fit to news personalization
• Content-based methods predict users interest
  based on text alone
• Changing interests
• Users interests tend to change frequently.
• A user model can adjust its interests quickly.
• The techniques of changing target concepts is
  known as concept drift.

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News Personalization (cont.)

• Multiple interests
• Users are interested in different news topics
• A user model must be capable of representing
  multiple interests
• The k-nearest-neighbor methods (kNN) are good
  choices.
• Novelty
• A new unknown story is considered most
  interesting.
• A new story too close to what user previously
  accessed is classified as a known story.

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News Personalization (cont.)

• Avoiding tunnel vision
• Personalization should not get in the way of
  finding important novel information.
• Editorial input
• A user model ranks stories by a prediction
  function.
• Retaining editorial input is an important feature
  for news organizations.
• To ensure users will get to see the top n stories.

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News Personalization (cont.)

• Brittleness
• A single action, with or without intention,
  should not have a radical effect on a user model.
• Availability of meta-tags
• News personalization algorithms can usually not
  rely on the availability of meta-tags.

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News Personalization (cont.)
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Adaptive News Navigation

• The objective is to simplify access to relevant
  content.
• This technique focuses on analyzing users access
  patterns to determine the position of menu items
  within a menu hierarchy.
• This approach is suitable to mobile applications
  due to limited screen space.

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Adaptive News Navigation (cont.)

• On average, the number of selected menu and
  scroll operations was reduced by over 50.
• However, this approach does not provide any news
  recommendations.

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Contextual Recommendations

• An approach draws on currently displayed
  information on the screen as an expression of the
  users current interests.
• The system extracts textual information on the
  users screen and the extracted text is used to
  retrieve related content.
• Statistical term-weighting techniques are used to
  identify informative terms.
• Blinkx is a publicly available contextual
  recommender (http//www.blinkx.com).

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Contextual Recommendations (cont.)
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News Aggregation

• News aggregators are services that aggregate
  content from many news sources, and then adapt to
  the current news landscape as a whole.
• The services use RSS (Rich Site Summary) feeds to
  provide links to available content.
• A news aggregation implementation can use
  statistical term-weighting and text similarity
  techniques.
• Google News (http//news.google.com) is one of
  these services.

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News Aggregation (cont.)
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Case Study

• Adaptive News Personalization for Mobile Content
  Access
• Learning User Models for News Access
• Evaluation

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Adaptive News Personalization for Mobile Content
Access

• The constraints of mobile information access make
  personalization important to produce usable
  applications.
• A news system in mobile personalizes the orders
  of news sections the most relevant stories are
  displayed on the topmost

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Adaptive News Personalization for Mobile Content
Access (cont.)
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Learning User Models for News Access

• The system uses a machine learning approach to
  build a simple model of each users interests.
• A combination of similarity-based methods and
  Bayesian methods achieves the balance of learning
  and adapting quickly to change interests while
  avoiding brittleness.

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Learning User Models for News Access (cont.)

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

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Learning User Models for News Access (cont.)

• The purpose of the short-term model
• First, it should contain information about
  recently read events, so that stories which
  belong to the same thread can be identified.
• To allow for identification of stories that user
  already knows.
• The k-nearest-neighbor algorithm (kNN) is used to
  achieve the desired functionality.
• Convert news stories to tf-idf vectors
  (term-frequency/inverse-document-frequency).
• Use the cosine similarity measure to quantify the
  similarity of two vectors.

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Learning User Models for News Access (cont.)

• The purpose of the long-term model is to model a
  users general preferences.
• The system periodically selects informative words
  for each news category from a large sample of
  stories.
• The goal of the feature selection process is to
  select informative words that reoccur over a long
  period of time.
• A naïve Bayesian classifier is used to assess the
  probability of stories being interesting.

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Learning User Models for News Access (cont.)
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Evaluation

• They summarize the results from two studies that
  compare personalization information access to
  static one.
• First, the alternating sessions experiment
  quantifies the difference between static and
  adaptive information access
• A half of users used its user modeling approach.
• The other half received news in static order from
  the source.

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Evaluation (cont.)

• The average display rank of selected stories was
  6.7 in the static mode and 4.2 in the adaptive
  mode (based on 50 users that selected 340 stories
  out of 1882 headlines).
• The analysis of the distribution of selected
  stories.
• In the static mode, 68.7 of the selected stories
  on the top two headline screens
• In the adaptive mode, 86.7 on the top two

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Evaluation (cont.)
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Evaluation (cont.)

• Second, the alternating stories experiment
  displays stories selected with respect to both
  the adaptive and static modes on the same screen.
• Advantages
• The system still adapts to users interests.
• Allow a direct comparison between the two
  selection strategies.

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Evaluation (cont.)

• The difference was not as pronounced as in the
  alternating sessions experiment.
• The average display rank of selected stories was
  5.8 in the static mode and 5.27 in the adaptive
  mode.
• The analysis of the distribution of selected
  stories.
• In the static mode, 75.57
• In the adaptive mode, 80.44
• Users are more likely to select adaptive stories
  (19.02) than static ones (13.26) which amounts
  to a 43.44 increase in selected content.

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Evaluation (cont.)
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Evaluation (cont.)

• In summary, the alternating sessions and
  alternating stories experiments show adaptive
  information access is higher than static access.
• The alternating sessions experiment showed
  adaptive order helps shifting interesting stories
  towards the beginning of personalized lists.
• The alternating stories experiment showed the
  system is capable of ordering content that the
  top-ranked items have a significantly higher
  chance to be selected that the ranked static ones.

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Recent Trends and Systems

• Podcasting
• Online audio distribution of news content.
• Collaborative filtering techniques is applicable
  to podcast recommendation.
• Personalization and the Blogosphere
• Blogosphere refers to the set of all webblogs.
• Some systems support personalized blog access
  such as Findory.com, NewsGator.com.
• News Zeitgeist
• Zeitgeist is a German word that means the spirit
  (Geist) of the time (Zeit).
• The goal is to automatically identify the most
  popular topics of the current Blogosphere.

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Conclusions References

• We need new technology to help leverage the full
  potential web-based news distribution.
• 1 Billsus, D. (2005). Adaptive News Access
• 2 Billsus, D., Pazzani, M. (2000). User
  Modeling for Adaptive News Access. User Modeling
  and User-Adapted Interaction, 10(2/3) 147-180.
• 3 Chiu, B. Webb, G. (1998) Using decision
  trees for agent modeling improving prediction
  performance. User Modeling and User-Adapted
  Interaction, 8, 131-152.

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Questions or Comments?