CPLR: Collaborative pairwise learning to rank for personalized recommendation

Liu, Hongzhi; Wu, Zhonghai; Zhang, Xing

doi:10.1016/j.knosys.2018.02.023

Cited by 49 publications

(26 citation statements)

References 25 publications

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“…In the future, we will explore the following research directions: (1) we will study the applications of the proposed models in various domains, like personalized recommendation (Liu et al, 2018); (2) we will explore other techniques to fuse the ordered relation information from different paths (Liu et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

Zhu¹,

Liu²,

Wu³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Incompleteness is a common problem for existing knowledge graphs (KGs), and the completion of KG which aims to predict links between entities is challenging. Most existing KG completion methods only consider the direct relation between nodes and ignore the relation paths which contain useful information for link prediction. Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning. In addition, these path-based models always ignore nonlinear contributions of path features for link prediction. To solve these problems, we propose a novel KG completion method named OPTransE. Instead of embedding both entities of a relation into the same latent space as in previous methods, we project the head entity and the tail entity of each relation into different spaces to guarantee the order of relations in the path. Meanwhile, we adopt a pooling strategy to extract nonlinear and complex features of different paths to further improve the performance of link prediction. Experimental results on two benchmark datasets show that the proposed model OPTransE performs better than state-of-the-art methods.

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Section: Discussionmentioning

confidence: 99%

Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

Zhu¹,

Liu²,

Wu³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

Self Cite

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show abstract

“…Qiu et al [33] constructed the preference chain of item groups for each user. Liu et al [24] utilized collaborative information mined from the interactions between users and items. [36,47] proposed dynamic negative sampling strategies to maximize the utility of a gradient step by choosing "difficult" negative samples.…”

Section: Learning To Rankmentioning

confidence: 99%

“…In existing efforts, only low-order connections are considered when measuring the similarity among vertices [24,30,33]. In this paper, we use the spectral features, which contains the information of high-order connections, to enhance the pairwise learning.…”

Section: Learning To Rankmentioning

confidence: 99%

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Spectrum-enhanced Pairwise Learning to Rank

Qin

2019

The World Wide Web Conference

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To enhance the performance of the recommender system, side information is extensively explored with various features (e.g., visual features and textual features). However, there are some demerits of side information: (1) the extra data is not always available in all recommendation tasks; (2) it is only for items, there is seldom highlevel feature describing users. To address these gaps, we introduce the spectral features extracted from two hypergraph structures of the purchase records. Spectral features describe the similarity of users/items in the graph space, which is critical for recommendation. We leverage spectral features to model the users' preference and items' properties by incorporating them into a Matrix Factorization (MF) model.In addition to modeling, we also use spectral features to optimize. Bayesian Personalized Ranking (BPR) is extensively leveraged to optimize models in implicit feedback data. However, in BPR, all missing values are regarded as negative samples equally while many of them are indeed unseen positive ones. We enrich the positive samples by calculating the similarity among users/items by the spectral features. The key ideas are: (1) similar users shall have similar preference on the same item; (2) a user shall have similar perception on similar items. Extensive experiments on two real-world datasets demonstrate the usefulness of the spectral features and the effectiveness of our spectrum-enhanced pairwise optimization. Our models outperform several state-of-the-art models significantly.

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“…Song et al [12] put forward a Generalized AUC (GAUC) metric to quantify the ranking performance in a signed social network. Based on the idea that users tend to prefer items selected by their neighbors, Liu et al [13] proposed a top-N recommendation algorithm called Collaborative Pairwise Learning to Rank (CPLR). Lu Yu et al [14] tried to incorporate multiple types of user-item relationships into a unified pairwise ranking model to optimize approximately the MAP and MRR ranking metrics.…”

Section: Top-n Recommendationmentioning

confidence: 99%

A Semi-Supervised Model for Top-N Recommendation

Chen

Peng

2018

Symmetry

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Top-N recommendation is an important recommendation technique that delivers a ranked top-N item list to each user. Data sparsity is a great challenge for top-N recommendation. In order to tackle this problem, in this paper, we propose a semi-supervised model called Semi-BPR (Semi-Supervised Bayesian Personalized Ranking). Our approach is based on the assumption that, for a given model, users always prefer items ranked higher in the generated recommendation list. Therefore, we select a certain number of items ranked higher in the recommendation list to construct an intermediate set and optimize the metric Area Under the Curve (AUC). In addition, we treat the intermediate set as a teaching set and design a semi-supervised self-training model. We conduct a series of experiments on three popular datasets to compare the proposed approach with several state-of-the-art baselines. The experimental results demonstrate that our approach significantly outperforms the other methods for all evaluation metrics, especially for sparse datasets.

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CPLR: Collaborative pairwise learning to rank for personalized recommendation

Cited by 49 publications

References 25 publications

Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

Spectrum-enhanced Pairwise Learning to Rank

A Semi-Supervised Model for Top-N Recommendation

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