book

Practical Recommender Systems

Name: Practical Recommender Systems
Author: Kim Falk
ISBN: 9781617292705

by Kim Falk

February 2019

Beginner to intermediate

432 pages

13h 29m

English

Manning Publications

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Copyright
Brief Table of Contents
Table of Contents
Preface
Acknowledgments
About this book
About the author
About the cover illustration
Part 1. Getting ready for recommender systems
Chapter 1. What is a recommender?
1.1. Real-life recommendations1.2. Taxonomy of recommender systems1.3. Machine learning and the Netflix Prize1.4. The MovieGEEKs website1.5. Building a recommender systemSummary

Chapter 2. User behavior and how to collect it
2.1. How (I think) Netflix gathers evidence while you browse2.2. Finding useful user behavior2.3. Identifying users2.4. Getting visitor data from other sources2.5. The collector2.6. What users in the system are and how to model themSummary
Chapter 3. Monitoring the system
3.1. Why adding a dashboard is a good idea3.2. Doing the analytics3.3. Personas3.4. MovieGEEKs dashboardSummary
Chapter 4. Ratings and how to calculate them
4.1. User-item preferences4.2. Explicit or implicit ratings4.3. Revisiting explicit ratings4.4. What are implicit ratings?4.5. Calculating implicit ratings4.6. How to implement implicit ratings4.7. Less frequent items provide more valueSummary
Chapter 5. Non-personalized recommendations
5.1. What’s a non-personalized recommendation?5.2. How to make recommendations when you have no data5.3. Implementing the chart and the groundwork for the recommender system component5.4. Seeded recommendationsSummary
Chapter 6. The user (and content) who came in from the cold
6.1. What’s a cold start?6.2. Keeping track of visitors6.3. Addressing cold-start problems with algorithms6.4. Those who doesn’t ask, won’t know6.5. Using association rules to start recommending things fastSummary
Part 2. Recommender algorithms
Chapter 7. Finding similarities among users and among content
7.1. Why similarity?7.2. Essential similarity functions7.3. k-means clustering7.4. Implementing similaritiesSummary
Chapter 8. Collaborative filtering in the neighborhood
8.1. Collaborative filtering: A history lesson8.2. Calculating recommendations8.3. Calculating similarities8.4. Amazon’s algorithm to precalculate item similarity8.5. Ways to select the neighborhood8.6. Finding the right neighborhood8.7. Ways to calculate predicted ratings8.8. Prediction with item-based filtering8.9. Cold-start problems8.10. A few words on machine learning terms8.11. Collaborative filtering on the MovieGEEKs site8.12. What’s the difference between association rule recs and collaborative recs?8.13. Levers to fiddle with for collaborative filtering8.14. Pros and cons of collaborative filteringSummary
Chapter 9. Evaluating and testing your recommender
9.1. Business wants lift, cross-sales, up-sales, and conversions9.2. Why is it important to evaluate?9.3. How to interpret user behavior9.4. What to measure9.5. Before implementing the recommender...9.6. Types of evaluation9.7. Offline evaluation9.8. Offline experiments9.9. Implementing the experiment in MovieGEEKs9.10. Evaluating the test set9.11. Online evaluation9.12. Continuous testing with exploit/exploreSummary
Chapter 10. Content-based filtering
10.1. Descriptive example10.2. Content-based filtering10.3. Content analyzer10.4. Extracting metadata from descriptions10.5. Finding important words with TF-IDF10.6. Topic modeling using the LDA10.7. Finding similar content10.8. Creating the user profile10.9. Content-based recommendations in MovieGEEKs10.10. Evaluation of the content-based recommender10.11. Pros and cons of content-based filteringSummary
Chapter 11. Finding hidden genres with matrix factorization
11.1. Sometimes it’s good to reduce the amount of data11.2. Example of what you want to solve11.3. A whiff of linear algebra11.4. Constructing the factorization using SVD11.5. Constructing the factorization using Funk SVD11.6. Doing recommendations with Funk SVD11.7. Funk SVD implementation in MovieGEEKs11.8. Explicit vs. implicit data11.9. Evaluation11.10. Levers to fiddle with for Funk SVDSummary
Chapter 12. Taking the best of all algorithms: Implementing hybrid recommenders
12.1. The confused world of hybrids12.2. The monolithic12.3. Mixed hybrid recommender12.4. The ensemble12.5. Feature-weighted linear stacking (FWLS)12.6. ImplementationSummary
Chapter 13. Ranking and learning to rank
13.1. Learning to rank an example at Foursquare13.2. Re-ranking13.3. What’s learning to rank again?13.4. Bayesian Personalized Ranking13.5. Implementation of BPR13.6. Evaluation13.7. Levers to fiddle with for BPRSummary
Chapter 14. Future of recommender systems
14.1. This book in a few sentences14.2. Topics to study next14.3. What’s the future of recommender systems?14.4. Final thoughts
Index
List of Figures
List of Tables
List of Listings

Content preview from Practical Recommender Systems

Index

[A][B][C][D][E][F][G][H][I][J][K][L][M][N][O][P][Q][R][S][T][U][V][W][X][Z]

A

absolute ordering active learning add_impression function add_vector advertisements algorithms addressing cold starts with association rules categories domain knowledge and business segments collaborative filtering content-based filtering hybrid recommenders in future of recommender systems overview ALS (Alternating Least Squares) method analytics dashboard, MovieGEEKs, 2^nd AP (average precision) architecture choosing in future of recommender systems argmax association rules, 2^nd getting all item sets, sizing one, and calculating support getting all transactions MovieGEEKs website displaying recs finding ...

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Practical Recommender Systems

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Index

A

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