Recommender Systems Handbook

@inproceedings{Shapira2015RecommenderSH,
  title={Recommender Systems Handbook},
  author={Bracha Shapira},
  booktitle={Springer US},
  year={2015}
}
Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd recommender systems handbook as the choice of reading, you can find here. 
Being Confident about the Quality of the Predictions in Recommender Systems
TLDR
It was found that unrated items have lower confidence compared to the entire item set - highlighting the importance of explanations for novel but risky recommendations.
Recommender System for News Articles using Supervised Learning
TLDR
The present thesis intends to observe the value of using a recommender algorithm to find users likes by observing her domain preferences, and will show how news topics can be used to recommend news articles.
Do You Have a Pop Face? Here is a Pop Song. Using Profile Pictures to Mitigate the Cold-start Problem in Music Recommender Systems
TLDR
It is proved that the preliminary work attempts to mitigate the cold-start problem using the profile picture of the user as a sole information, following the intuition that a correspondence may exist between the pictures that people use to represent themselves and their taste.
Collaborative Filtering for Movie Recommendation using RapidMiner
TLDR
A brief overview of collaborative filtering based movie recommender system and their implementation using rapid miner is presented.
Machine learning for recommendation systems in job postings selection
Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user?s
RECIPROCAL RECOMMENDATION IN MATCHMAKING SYSTEMS
TLDR
A detailed study has been done on this class of recommenders for online dating to match people whose interests mutually coincide in and hence likely to communicate with each other.
Managing natural noise in collaborative recommender systems
TLDR
A novel approach to detect and correct those inconsistent ratings that might bias recommendations, by using global information about user and item preferences is proposed, which characterizes items and users by their ratings and classifies a rating as noisy if it contradicts user or item tendencies.
CoRec: a co-training approach for recommender systems
TLDR
A framework, named CoRec, is proposed, which is based on a co-training approach that drives two recommenders to agree with each other's predictions to generate their own predictions, to demonstrate the efficiency of the approach under different configurations.
What to read next?: making personalized book recommendations for K-12 users
TLDR
BReK12, a book recommender that makes personalized suggestions tailored to each K-12 user U based on books available on a social book-marking site that are similar in content to the ones known to be of interest to U, has been bookmarked by users with reading patterns similar to U's, and can be comprehended by U.
Recommender Systems Using Social Network Analysis: Challenges and Future Trends
TLDR
Recommender systems are software tools and techniques dedicated to generate meaningful suggestions about new items (products and services) for particular customers (the users of the RS) to help them to make decisions in multiple contexts.
...
1
2
3
4
5
...