Presented bySUN Jianshan

Information department of college of business

Recommendation techniques

Intended Learning OutcomesIntended Learning Outcomesof this courseof this course

Intended Learning OutcomesIntended Learning Outcomesof 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 techniquesare 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 auser has given to items.

The technique combines these ratings to a profile of the user’s interests basedon 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 arecategorized based on the attributes of their demographic profiles or on similar rating preferences in order to find users with similarfeatures.

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!