Information Storage and Retrieval Techniques Unit V
- 2. Overview Overview • Definition Definition • Ways its used Ways its used • Problems Problems • Maintenance Maintenance • The future The future
- 3. What is it? What is it? • Recommender systems are a technological Recommender systems are a technological proxy for a social process. proxy for a social process. • Recommender systems are a way of Recommender systems are a way of suggesting like or similar items and ideas to a suggesting like or similar items and ideas to a users specific way of thinking. users specific way of thinking. • Recommender systems try to automate Recommender systems try to automate aspects of a completely different information aspects of a completely different information discovery model where people try to find discovery model where people try to find other people with similar tastes and then ask other people with similar tastes and then ask them to suggest new things. them to suggest new things.
- 4. Example Example • Customer A Customer A – Buys Metalica CD Buys Metalica CD – Buys Megadeth CD Buys Megadeth CD • Customer B Customer B – Does search on Metalica Does search on Metalica – Recommender system Recommender system suggests Megadeth from suggests Megadeth from data collected from data collected from customer A customer A
- 5. Motivation for Recommender Motivation for Recommender Systems Systems • Automates quotes like: Automates quotes like: – "I like this book; you might be interested in "I like this book; you might be interested in it" it" – "I saw this movie, you’ll like it“ "I saw this movie, you’ll like it“ – "Don’t go see that movie!" "Don’t go see that movie!"
- 6. Further Motivation Further Motivation • Many of the top commerce sites use Many of the top commerce sites use recommender systems to improve sales. recommender systems to improve sales. • Users may find new books, music, or Users may find new books, music, or movies that was previously unknown to movies that was previously unknown to them. them. • Also can find the opposite for e.g.: Also can find the opposite for e.g.: movies or music that will definitely not movies or music that will definitely not be enjoyed. be enjoyed.
- 7. Where is it used? Where is it used? • Massive E-commerce sites use this tool Massive E-commerce sites use this tool to suggest other items a consumer may to suggest other items a consumer may want to purchase want to purchase • Web personalization Web personalization
- 8. Ways its used Ways its used • Survey’s filled out by past users for the Survey’s filled out by past users for the use of new users use of new users • Search-style Algorithms Search-style Algorithms • Genre matching Genre matching • Past purchase querying Past purchase querying
- 9. Recommender System Types Recommender System Types • Collaborative/Social-filtering system Collaborative/Social-filtering system – aggregation of – aggregation of consumers’ preferences and recommendations to consumers’ preferences and recommendations to other users based on similarity in behavioral patterns other users based on similarity in behavioral patterns • Content-based system Content-based system – supervised machine learning – supervised machine learning used to induce a classifier to discriminate between used to induce a classifier to discriminate between interesting and uninteresting items for the user interesting and uninteresting items for the user • Knowledge-based system Knowledge-based system – knowledge about users – knowledge about users and products used to reason what meets the user’s and products used to reason what meets the user’s requirements, using discrimination tree, decision requirements, using discrimination tree, decision support tools, case-based reasoning (CBR) support tools, case-based reasoning (CBR)
- 10. Content-based Collaborative Content-based Collaborative Information Filtering Information Filtering • Relevance feedback Relevance feedback – positive/negative – positive/negative prototypes. prototypes. • Feature selection Feature selection – removal of non- – removal of non- informative terms. informative terms. • Learning to recommend Learning to recommend – agent counts – agent counts with 2 matrices; user vs. category matrix with 2 matrices; user vs. category matrix (for successful classification) and user’s (for successful classification) and user’s recommendation factor (1 to 5) or binary. recommendation factor (1 to 5) or binary.
- 12. Examples Examples Amazon.com Books, movies, music Books, movies, music CDNOW.com Music Music Ebay.com (feedback (feedback forms) forms) Anything Anything Reel.com Movies Movies Barnes & Noble Books Books
- 13. Problems Problems • Inconclusive user feedback forms Inconclusive user feedback forms • Finding users to take the feedback surveys Finding users to take the feedback surveys • Weak Algorithms Weak Algorithms • Poor results Poor results • Poor Data Poor Data • Lack of Data Lack of Data • Privacy Control (May NOT explicitly Privacy Control (May NOT explicitly collaborate with recipients) collaborate with recipients)
- 14. Maintenance Maintenance • Costly Costly • Information becomes outdated Information becomes outdated • Information quantity (large, disk space Information quantity (large, disk space expansion) expansion)
- 15. The Future of Recommender The Future of Recommender Systems Systems • Extract implicit negative ratings through Extract implicit negative ratings through the analysis of returned item. the analysis of returned item. • How to integrate community with recommendations • Recommender systems will be used in the future to predict demand for products, enabling earlier communication back the supply chain.