Top-N Recommendation with Novel Rank Approximation

Kang, Zhao; Cheng, Qiang

doi:10.1137/1.9781611974348.15

Cited by 8 publications

(2 citation statements)

References 27 publications

(42 reference statements)

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“…Recently, self-expression has been successfully utilized in subspace recovery (Elhamifar and Vidal 2009;Luo et al 2011), low rank representation (Kang, Peng, and Cheng 2015b;2015a), and recommender systems (Kang and Cheng 2016). It represents each data point in terms of the other points.…”

Section: Introductionmentioning

confidence: 99%

Twin Learning for Similarity and Clustering: A Unified Kernel Approach

Kang¹,

Peng²,

Cheng³

2017

AAAI

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Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However similarity measurement is challenging because it is usually impacted by many factors, e.g., the choice of similarity metric, neighborhood size, scale of data, noise and outliers. Thus the learned similarity matrix is often not suitable, let alone optimal, for the subsequent clustering. In addition, nonlinear similarity often exists in many real world data which, however, has not been effectively considered by most existing methods. To tackle these two challenges, we propose a model to simultaneously learn cluster indicator matrix and similarity information in kernel spaces in a principled way. We show theoretical relationships to kernel k-means, k-means, and spectral clustering methods. Then, to address the practical issue of how to select the most suitable kernel for a particular clustering task, we further extend our model with a multiple kernel learning ability. With this joint model, we can automatically accomplish three subtasks of finding the best cluster indicator matrix, the most accurate similarity relations and the optimal combination of multiple kernels. By leveraging the interactions between these three subtasks in a joint framework, each subtask can be iteratively boosted by using the results of the others towards an overall optimal solution. Extensive experiments are performed to demonstrate the effectiveness of our method.

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Section: Introductionmentioning

confidence: 99%

Twin Learning for Similarity and Clustering: A Unified Kernel Approach

Kang¹,

Peng²,

Cheng³

2017

AAAI

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“…By learning an aggregation coefficient matrix [13], recently, sparse linear method (SLIM) [17] has been proposed and shown to be effective. However, it just captures relations between items that have been co-purchased/co-rated by at least one user [12]. Moreover, it only explores the linear relations between items.…”

Section: Introductionmentioning

confidence: 99%

Top-N Recommendation on Graphs

Kang

Peng

Cheng

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.

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