• Corpus ID: 16257940

Recommendation by Mining Multiple User Behaviors with Group Sparsity

@inproceedings{Yuan2014RecommendationBM,
  title={Recommendation by Mining Multiple User Behaviors with Group Sparsity},
  author={Ting Yuan and Jian Cheng and Xi Sheryl Zhang and Shuang Qiu and Hanqing Lu},
  booktitle={AAAI},
  year={2014}
}
Recently, some recommendation methods try to improve the prediction results by integrating information from user's multiple types of behaviors. How to model the dependence and independence between different behaviors is critical for them. In this paper, we propose a novel recommendation model, the Group-Sparse Matrix Factorization (GSMF), which factorizes the rating matrices for multiple behaviors into the user and item latent factor space with group sparsity regularization. It can (1) select… 

Figures and Tables from this paper

Exploiting Incidence Relation Between Subgroups for Improving Clustering-Based Recommendation Model
TLDR
Experimental results on different scales of MovieLens datasets demonstrate that the proposed improvedMatrix factorization method outperforms state-of-the-art clustering-based recommendation methods, especially on sparse datasets.
Personalized Next Point-of-Interest Recommendation via Latent Behavior Patterns Inference
TLDR
A Bayesian Personalized Ranking approach is furnished to jointly model the next POI recommendation under the influence of user's latent behavior pattern, and a third-rank tensor is proposed to model the successive check-in behaviors.
Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns
TLDR
This paper proposes to adopt a third-rank tensor to model the successive check-in behaviors of users under the influence of user's latent behavior pattern and furnish a Bayesian Personalized Ranking (BPR) approach and derive the optimization criterion accordingly.
Mixture Matrix Approximation for Collaborative Filtering
TLDR
A mixture matrix approximation method is proposed, in which it is assumed that the user-item ratings follow mixture distributions and the user/item feature vectors vary among different stars to better characterize the diverse interests of users/items.
Next and Next New POI Recommendation via Latent Behavior Pattern Inference
TLDR
This article proposes to utilize personalized latent behavior patterns learned from contextual features, e.g., time of day, day of week, and location category, to improve the effectiveness of the recommendations of next and next new point-of-interest recommendations.
A Deep Neural Network Model for Rating Prediction Based on Multi-layer Prediction and Multi-granularity Latent Feature Vectors
TLDR
A Deep neural network model based on Multi-layer prediction and Multi-granularity latent feature vectors (DMM model), which ensures fully use of the information in rating matrix and side information and thus may result in better performance.
When Personalization Meets Conformity: Collective Similarity based Multi-Domain Recommendation
TLDR
A Collective Structure Sparse Representation (CSSR) method for multi-domain recommendation based on adaptive $k$-Nearest-Neighbor framework, which imposes the lasso and group lasso penalties as well as least square loss to jointly optimize the collective similarity.
MPMA: Mixture Probabilistic Matrix Approximation for Collaborative Filtering
TLDR
Experimental study using MovieLens and Netflix datasets demonstrates that MPMA outperforms five state-of-the-art MA based CF methods in recommendation accuracy with good scalability.
Improving Implicit Recommender Systems with View Data
TLDR
This work proposes to model the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more effective and flexible than typical pointwise matrix factorization (MF) methods.
...
1
2
3
...

References

SHOWING 1-10 OF 20 REFERENCES
SoRec: social recommendation using probabilistic matrix factorization
TLDR
A factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users' social network information and rating records is proposed.
Multi-Domain Collaborative Filtering
TLDR
A Probabilistic framework is proposed which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains.
Personalized recommendation via cross-domain triadic factorization
TLDR
This paper proposes a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors.
Factorization meets the neighborhood: a multifaceted collaborative filtering model
TLDR
The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model and a new evaluation metric is suggested, which highlights the differences among methods, based on their performance at a top-K recommendation task.
Multi-relational matrix factorization using bayesian personalized ranking for social network data
TLDR
This work proposes a formalization of the cold-start problem and a principled approach to it based on multi-relational factorization techniques, and derives a principled feature extraction scheme from the social data to extract predictors for a classifier on the target relation.
Transfer Learning in Collaborative Filtering for Sparsity Reduction
TLDR
This paper discovers the principle coordinates of both users and items in the auxiliary data matrices, and transfers them to the target domain in order to reduce the effect of data sparsity.
Relational learning via collective matrix factorization
TLDR
This model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems, which can handle any pairwise relational schema and a wide variety of error models.
Probabilistic Matrix Factorization
TLDR
The Probabilistic Matrix Factorization (PMF) model is presented, which scales linearly with the number of observations and performs well on the large, sparse, and very imbalanced Netflix dataset and is extended to include an adaptive prior on the model parameters.
Item-based collaborative filtering recommendation algorithms
TLDR
This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction
TLDR
This paper proposes an efficient algorithm for reconstructing the target rating matrix by expanding the codebook, a compact and informative and yet compact cluster-level rating pattern representation referred to as a codebook for transferring useful knowledge from the auxiliary task domain.
...
1
2
...