Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
ACM Computing Surveys (CSUR)
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Recommender systems and their impact on sales diversity
Proceedings of the 8th ACM conference on Electronic commerce
Introduction to Information Retrieval
Introduction to Information Retrieval
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Enhancing diversity in Top-N recommendation
Proceedings of the third ACM conference on Recommender systems
Anatomy of the long tail: ordinary people with extraordinary tastes
Proceedings of the third ACM international conference on Web search and data mining
A comparison of co-occurrence and similarity measures as simulations of context
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
| Hi-index | 0.00 |
In order to satisfy and positively surprise the users, a recommender system needs to recommend items the users will like and most probably would not have found on their own. This requires the recommender system to recommend a broader range of items including niche items as well. Such an approach also support online-stores that often offer more items than traditional stores and need recommender systems to enable users to find the not so popular items as well. However, popular items that hold a lot of usage data are more easy to recommend and, thus, niche items are often excluded from the recommendations. In this paper, we propose a new collaborative filtering approach that is based on the items' usage contexts. The approach increases the rating predictions for niche items with fewer usage data available and improves the aggragate diversity of the recommendations.