A linear iteration time layout algorithm for visualising high-dimensional data
Proceedings of the 7th conference on Visualization '96
SWAMI (poster session): a framework for collaborative filtering algorithm development and evaluation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Fast multidimensional scaling through sampling, springs and interpolation
Information Visualization
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Semisupervised learning from dissimilarity data
Computational Statistics & Data Analysis
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparing State-of-the-Art Collaborative Filtering Systems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
The adaptive web
Pushing the boundaries of crowd-enabled databases with query-driven schema expansion
Proceedings of the VLDB Endowment
Collaborative filtering via temporal euclidean embedding
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Efficient retrieval of recommendations in a matrix factorization framework
Proceedings of the 21st ACM international conference on Information and knowledge management
Multi-space probabilistic sequence modeling
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards scalable and accurate item-oriented recommendations
Proceedings of the 7th ACM conference on Recommender systems
| Hi-index | 0.01 |
Recommendation systems suggest items based on user preferences. Collaborative filtering is a popular approach in which recommending is based on the rating history of the system. One of the most accurate and scalable collaborative filtering algorithms is matrix factorization, which is based on a latent factor model. We propose a novel Euclidean embedding method as an alternative latent factor model to implement collaborative filtering. In this method, users and items are embedded in a unified Euclidean space where the distance between a user and an item is inversely proportional to the rating. This model is comparable to matrix factorization in terms of both scalability and accuracy while providing several advantages. First, the result of Euclidean embedding is more intuitively understandable for humans, allowing useful visualizations. Second, the neighborhood structure of the unified Euclidean space allows very efficient recommendation queries. Finally, the method facilitates online implementation requirements such as mapping new users or items in an existing model. Our experimental results confirm these advantages and show that collaborative filtering via Euclidean embedding is a promising approach for online recommender systems.