Book Recommender System using python .pptx

pretty basics but helpful for some

1. Book Recommended Systems

2. What is Recommended Systems

3. Recommender Systems Application areas

4. In the Social Web

5. What are recommender systems for? Introduction How do they work (Part I) ? Collaborative Filtering How to measure their success? Evaluation techniques How do they work (Part II) ? Content-based Filtering Knowledge-Based Recommendations Hybridization Strategies Advanced topics Explanations Human decision making Agenda

6. Introduction

7. Why using Recommender Systems? Value for the customer Find things that are interesting Narrow down the set of choices Help me explore the space of options Discover new things Entertainment … Value for the provider Additional and probably unique personalized service for the customer Increase trust and customer loyalty Increase sales, click trough rates, conversion etc. Opportunities for promotion Obtain more knowledge about customers …

8. Real-world check Myths from industry Amazon.com generates 30-40 percent of their sales through the recommendation lists Netflix (DVD rental and movie streaming) generates 20-25 percent of their sales through the recommendation lists There must be some value in it See recommendation of groups, jobs or people on LinkedIn Friend recommendation and ad personalization on Facebook Song recommendation at last.fm News recommendation at Forbes.com (plus 37% CTR)

9. Recommender systems Recommender systems reduce information overload by estimating relevance

10. Types of Recommender Systems? Personal recommender systems Collaborative recommender systems Content-based recommender systems Knowledge-based recommender systems Hybrid recommender systems

11. Paradigms of recommender systems

12. Collaborative recommender systems Collaborative: "Tell me what's popular among my peers"

13. Content-based recommender systems Content-based: "Show me more of the same what I've liked "

14. Knowledge-based recommender systems Knowledge-based: "Tell me what fits based on my needs"

15. Hybrid recommender systems Hybrid: combinations of various inputs and/or composition of different mechanism

16. Evaluation of Recommender Systems

17. What is a good recommendation? Total sales numbers Promotion of certain items … Click-through-rates Interactivity on platform … Customer return rates Customer satisfaction and loyalty What are the measures in practice?

18. Group B : Assignments No. 7 Developing an book recommend-er ( a book that the reader should read and is new) Expert system Flow of application : Top Rated and New added book will recormanded first. Functionality provide for add new book Database: SQLite Control Used : Listview: To show list on screen Spinner: To show drop down on screen

19. Screens Top rated books Recommender by system

20. Screens Click on Book & make Recommendation i.e give rating

21. Screens Add New book.

22. Thank you for your attention! http://recsys.acm.org http://www.recommenderbook.net