Recommendation survey and summary

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<ul><li> 1. Recommendation Survey and Summary Chen Ting Zhao05/22/12 </li> <li> 2. Agenda Motivation Recommendation Techniques Overview Techniques, Advantages &amp; Problem Recommendation Scenario Domain Specific: E-Commerce: Amazon Music: Pandora Recommendation as a service: Choice Stream Current Problems and Issues05/22/12 2 </li> <li> 3. Motivation Web2.0 Search Search Recommendation Recommendation E-commerce social (music movie and bookmark...) Recommendation Recommendation As a Service E-commerce domain general 05/22/12 3 </li> <li> 4. Recommendation Techiques - Overview The techniques used by recommendation engines can be classified based on the information sources they use. The available sources are: user features (demographics) : age, gender, profession, income, location... item features: keyword, genres... user-item ratings: gathered through questionarures, explict ratings, transaction data Model 05/22/12 4 </li> <li> 5. Demographic Recommendation User feature item Advantages user-item preferences cold start item domain-independent. Problems taste domain book movie music feature gray sheep problem preference 05/22/12 5 </li> <li> 6. Content-based Recommendation Item feature item model Item taste profile profile item feature item Advantages model taste Problems item item item item feature cold start preference movie music book website item 100 05/22/12 6 </li> <li> 7. Collaborative Filtering user-item preferences user behavior item user 3 user-based user item item-based item preference item model-based preference feature model preference Advantages item item machine-readable domain-independent Problems item user cold start preference user-item preference taste gray sheep problem preference 05/22/12 7 </li> <li> 8. Rule-based Recommendation user-item preference preference item Advantages Problems domain 05/22/12 8 </li> <li> 9. Hybrid Approaches Weighted Hybirdization: linear formula recommendation weight combine Switching Hybridization item Mixed Hybridization section Feature Combination feature recommendation Feature Auggmentation: feature Cascaded Hybridization: recommendation priority low-priority high-priority Meta-Level Hybridization: recommendation model 05/22/12 9 </li> <li> 10. Personalized vs. Non-Personalized Non-personalized recommendations admin popular items. Personalized recommendations 05/22/12 10 </li> <li> 11. E-commerce Domain - Amazon Amazon recommendation recommend website Amazon Amazon trace website section recommendation Content-based collaborative Item based / collaborative User based Amazon social Amazon item Amazon profile taste 05/22/12 11 </li> <li> 12. Music Domain - Pandora Pandora music item item item items content-based item user item 05/22/12 12 </li> <li> 13. Recommendation as a service : ChoiceStream ChoiceStream is a personalisation company that offers their recommendation technology Real Relevance Recommendations as a fully-hosted service for e- commerce vendors. ChoiceStream is using a hybrid system based on a variety of techniques that are chosen and combined depending on the concrete recommendation use case on hand. 05/22/12 13 </li> <li> 14. Problem and Issues Data Collection explict implict explict: demographic data, preference info, search terms explicit rating, comments... implict: tracking users behavior click sequences, reading time, transaction data... Cold Start preference item user social data Stability vs Plasticity history data taste model rating interest loose Sparsity user-item rating recommendation / rating 05/22/12 14 </li> <li> 15. Problem and Issues - cont. Performance &amp; Scalablity Demographic content-based item-based model-based offline social real-time offline item user User Input Consistency user white sheep black sheep gray sheep item opinion gray sheep Privary 05/22/12 15 </li> </ul>