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Recommendation techniques

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Presented by SUN Jianshan Information department of college of business Recommendation techniques
Intended Learning OutcomesIntended Learning Outcomes of this courseof this course Intended Learning OutcomesIntended Learning Outcomes of this courseof this course
Researchers’ troubles •Every day, a researcher: – Spend about half of their working time just searching for information – Do not receive timely update information related to their research – Find uneasy to connect with other researchers for joint research activities
Everything is changing • Based on above problems of researchers , recommendation techniques ‘s coming will have great influence in all aspects of our life. Traditional future Recommendation techniques
What’s the recommendation techniques ？ Recommender techniques are information agents that attempt to predict which items out of a large pool a user may be interested in and recommend the best ones to the target user.
Techniques category • The techniques can be classified based on the information sources they use . • The available sources are the user features (demographics) (e.g. age, gender, income, location), the item features (e.g. keywords, genres), the user-item ratings (explicit ratings, transaction data) and knowledge about user and item(for reasoning).
Techniques category
An open question If you want to buy a pair of trousers in a shop, what kinds of suggestions will you get?
non-personalized recommendation Non-personalized recommendations are identical for each user. The recommendations are often based on the popularity of items (e.g. average ratings, sales data). Maybe the clerk advises you to buy some popular trousers
Content-based recommendation Content-based recommendation methods use the information about item features and the ratings a user has given to items. The technique combines these ratings to a profile of the user’s interests based on the features of the rated items. Maybe the clerk advises you to buy some trousers according to your styles and preferences
Collaborative filtering recommendation The users are categorized based on the attributes of their demographic profiles or on similar rating preferences in order to find users with similar features. The technique then recommends items that are preferred by these similar users Maybe you will receive suggestions from you like-minded friends (similar demographic profiles or similar preference)
Knowledge-based recommendation Maybe you will take the recommendations considering the knowledge about price ,quality and so on. Considering the users’ specific tasks, Knowledge-based recommendation can address this problem by using a model of knowledge.
Examples
Techniques category
Thank you for your attention If you want to know more about this interesting techniques , please wait for next class!

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Recommendation techniques

  • 1. Presented by SUN Jianshan Information department of college of business Recommendation techniques
  • 2. Intended Learning OutcomesIntended Learning Outcomes of this courseof this course Intended Learning OutcomesIntended Learning Outcomes of this courseof this course
  • 3. Researchers’ troubles •Every day, a researcher: – Spend about half of their working time just searching for information – Do not receive timely update information related to their research – Find uneasy to connect with other researchers for joint research activities
  • 4. Everything is changing • Based on above problems of researchers , recommendation techniques ‘s coming will have great influence in all aspects of our life. Traditional future Recommendation techniques
  • 5. What’s the recommendation techniques ？ Recommender techniques are information agents that attempt to predict which items out of a large pool a user may be interested in and recommend the best ones to the target user.
  • 6. Techniques category • The techniques can be classified based on the information sources they use . • The available sources are the user features (demographics) (e.g. age, gender, income, location), the item features (e.g. keywords, genres), the user-item ratings (explicit ratings, transaction data) and knowledge about user and item(for reasoning).
  • 8. An open question If you want to buy a pair of trousers in a shop, what kinds of suggestions will you get?
  • 9. non-personalized recommendation Non-personalized recommendations are identical for each user. The recommendations are often based on the popularity of items (e.g. average ratings, sales data). Maybe the clerk advises you to buy some popular trousers
  • 10. Content-based recommendation Content-based recommendation methods use the information about item features and the ratings a user has given to items. The technique combines these ratings to a profile of the user’s interests based on the features of the rated items. Maybe the clerk advises you to buy some trousers according to your styles and preferences
  • 11. Collaborative filtering recommendation The users are categorized based on the attributes of their demographic profiles or on similar rating preferences in order to find users with similar features. The technique then recommends items that are preferred by these similar users Maybe you will receive suggestions from you like-minded friends (similar demographic profiles or similar preference)
  • 12. Knowledge-based recommendation Maybe you will take the recommendations considering the knowledge about price ,quality and so on. Considering the users’ specific tasks, Knowledge-based recommendation can address this problem by using a model of knowledge.
  • 15. Thank you for your attention If you want to know more about this interesting techniques , please wait for next class!

Hinweis der Redaktion

  1. These items can be of any type, like movies, music, books, Websites , commodities or collaborators. The user’s interest in an item is expressed through the rating the user gives the item. A recommendation system has to predict the ratings for items that the user has not yet seen.