Hybrid recommenders: incorporating metadata awareness into latent factor models

Authors:
Edson B. Santos Junior;Marcelo G. Manzato;Rudinei Goularte
Affiliations:
São Paulo University, São Carlos, Brazil;São Paulo University, São Carlos, Brazil;São Paulo University, São Carlos, Brazil
Venue:
Proceedings of the 19th Brazilian symposium on Multimedia and the web
Year:
2013

Citing 20
Cited 0

Hybrid Recommender Systems: Survey and Experiments

User Modeling and User-Adapted Interaction
An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms

Information Retrieval
A Hybrid Recommender System Combining Collaborative Filtering with Neural Network

AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Accurately interpreting clickthrough data as implicit feedback

Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search ranking by incorporating user behavior information

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Lessons from the Netflix prize challenge

ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Factorization meets the neighborhood: a multifaceted collaborative filtering model

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Online-updating regularized kernel matrix factorization models for large-scale recommender systems

Proceedings of the 2008 ACM conference on Recommender systems
Collaborative Filtering for Implicit Feedback Datasets

ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Collaborative Filtering Based on Demographic Attribute Vector

FCC '09 Proceedings of the 2009 ETP International Conference on Future Computer and Communication
Factor in the neighbors: Scalable and accurate collaborative filtering

ACM Transactions on Knowledge Discovery from Data (TKDD)
Performance of recommender algorithms on top-n recommendation tasks

Proceedings of the fourth ACM conference on Recommender systems
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations

ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Recommender Systems Handbook

Recommender Systems Handbook
MyMediaLite: a free recommender system library

Proceedings of the fifth ACM conference on Recommender systems
Combining demographic data with collaborative filtering for automatic music recommendation

KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Local implicit feedback mining for music recommendation

Proceedings of the sixth ACM conference on Recommender systems
Discovering latent factors from movies genres for enhanced recommendation

Proceedings of the sixth ACM conference on Recommender systems
gSVD++: supporting implicit feedback on recommender systems with metadata awareness

Proceedings of the 28th Annual ACM Symposium on Applied Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a hybrid recommender algorithm which integrates a set of different user's inputs into a unified and generic latent factor model to improve prediction accuracy. The technique can exploit users' demographics, such as age, gender and occupation, along with implicit feedback and items' metadata. Depending on the personal information from users, the recommender selects content whose subject is semantically related to their interests. The method was evaluated in the MovieLens dataset and compared against other approaches reported in the literature. The results show the effectiveness of incorporating metadata awareness into a latent factor model.