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Recommendation systems Paolo Ferragina Dipartimento di Informatica Università di Pisa Slides only! Recommendation systems Paolo Ferragina Dipartimento di Informatica Università di Pisa Slides only!

Recommendations n We have a list of restaurants n with and ratings for some Recommendations n We have a list of restaurants n with and ratings for some Which restaurant(s) should I recommend to Dave?

Basic Algorithm n Recommend the most popular restaurants n n say # positive votes Basic Algorithm n Recommend the most popular restaurants n n say # positive votes minus # negative votes What if Dave does not like Spaghetti?

Smart Algorithm n Basic idea find the person “most similar” to Dave : according Smart Algorithm n Basic idea find the person “most similar” to Dave : according to cosine-similarity (i. e. Estie), and then recommend something this person likes. n Perhaps recommend Straits Cafe to Dave Do you want to rely on one person’s opinions?

Main idea U V W Y d 1 d 2 d 3 d 4 Main idea U V W Y d 1 d 2 d 3 d 4 d 5 d 6 d 7 What do we suggest to U ?

Search Engines Advertising Slides only! Search Engines Advertising Slides only!

Classic approach… Socio-demo Geographic Contextual Classic approach… Socio-demo Geographic Contextual

Search Engines vs Advertisement n First generation -- use only on-page, web-text data n Search Engines vs Advertisement n First generation -- use only on-page, web-text data n Word frequency and language Pure search vs Paid search n Second generation -- use off-page, web-graph data n n Link (or connectivity) analysis Anchor-text (How people refer to a page) Ads show on search (who pays more), Goto/Overture n Third generation -- answer “the need behind the query” n n n Focus on “user need”, rather than on query Integrate multiple data-sources Click-through data 2003 Google/Yahoo New model All players now have: SE, Adv platform + network

The new scenario n SEs make possible n n n aggregation of interests unlimited The new scenario n SEs make possible n n n aggregation of interests unlimited selection (Amazon, Netflix, . . . ) Incentives for specialized niche players The biggest money is in the smallest sales !!

Two new approaches n Sponsored search: Ads driven by search keywords (and user-profile issuing Two new approaches n Sponsored search: Ads driven by search keywords (and user-profile issuing them) Ad. Words

+$ -$ +$ -$

Two new approaches n Sponsored search: Ads driven by search keywords (and user-profile issuing Two new approaches n Sponsored search: Ads driven by search keywords (and user-profile issuing them) Ad. Words n Context match: Ads driven by the content of a web page (and user-profile reaching that page) Ad. Sense

How does it work ? 1) 2) 3) Match Ads to query or pg How does it work ? 1) 2) 3) Match Ads to query or pg content Order the Ads Pricing on a click-through IR Econ

Visited Pages Clicked Banner Web Searches Clicks on Search Results Web usage data !!! Visited Pages Clicked Banner Web Searches Clicks on Search Results Web usage data !!!

Dictionary problem Dictionary problem

A new game n Similar to web searching, but: Ad-DB is smaller, Ad-items are A new game n Similar to web searching, but: Ad-DB is smaller, Ad-items are small pages, ranking depends on clicks For advertisers: n n n What words to buy, how much to pay SPAM is an economic activity For search engines owners: n How to price the words n Find the right Ad n Keyword suggestion, geo-coding, business control, language restriction, proper Ad display