Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
User reputation in a comment rating environment
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Generalizing matrix factorization through flexible regression priors
Proceedings of the fifth ACM conference on Recommender systems
Co-author Relationship Prediction in Heterogeneous Bibliographic Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
ETF: extended tensor factorization model for personalizing prediction of review helpfulness
Proceedings of the fifth ACM international conference on Web search and data mining
When will it happen?: relationship prediction in heterogeneous information networks
Proceedings of the fifth ACM international conference on Web search and data mining
Personalized recommendation of user comments via factor models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Relevance search in heterogeneous networks
Proceedings of the 15th International Conference on Extending Database Technology
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With the growing amount of information available online, recommender systems are starting to provide a viable alternative and complement to search engines, in helping users to find objects of interest. Methods based on Matrix Factorization (MF) models are the state-of-the-art in recommender systems. The input to MF is user feedback, in the form of a rating matrix. However, users can be engaged in interactions with multiple types of entities across different contexts, leading to multiple rating matrices. In other words, users can have interactions in a heterogeneous information network. Generally, in a heterogeneous network, entities from any two entity types can have interactions with a weight (rating) indicating the level of endorsement. Collective Matrix Factorization (CMF) has been proposed to address the recommendation problem in heterogeneous networks. However, a main issue with CMF is that entities share the same latent factor across different contexts. This is particularly problematic in two cases: Latent factors for entities that are cold-start in a context will be learnt mainly based on the data from other contexts where these entities are not cold-start, and therefore the factors are not properly learned for the cold-start context. Also, if a context has more data compared to another context, then the dominant context will dominate the learning process for the latent factors for entities shared in these two contexts. In this paper, we propose a context-dependent matrix factorization model, HeteroMF, that considers a general latent factor for entities of every entity type and context-dependent latent factors for every context in which the entities are involved. We learn a general latent factor for every entity and transfer matrices for every context to convert the general latent factors into a context-dependent latent factor. Experiments on two real life datasets from Epinions and Flixster demonstrate that HeteroMF substantially outperforms CMF, particularly for cold-start entities and for contexts where interactions in one contexts are dominated by other contexts.