Metric Factorization with Item Cooccurrence for Recommendation

Dai, Honglin; Wang, Liejun; Qin, Jiwei

doi:10.3390/sym12040512

Cited by 4 publications

(4 citation statements)

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“…This is a metric factorization method, which maps the user and item to Euclidean space and then leverage the norm clipping and covariance regularization to fulfill data regularization. MFReg (Wang et al , 2020). This method is based on ML, which takes implicit feedback and social information into consideration. MFIC (Dai et al , 2020). This is a ML method, which integrates the item co-occurrence with metric factorization and adopts the Euclidean distance to measure the user and item proximity.…”

Section: Methodsmentioning

confidence: 99%

“…(6) MFIC (Dai et al, 2020). This is a ML method, which integrates the item co-occurrence with metric factorization and adopts the Euclidean distance to measure the user and item proximity.…”

Section: Evaluation Algorithmsmentioning

confidence: 99%

“…Deng et al (2019) made a favorable improvement to enhance CF prediction. Dai et al (2020) introduced the item co-occurrence into ML for recommendations. These ML-based methods can reinforce the interpretability and uncover the essential relationships among data samples by producing metric.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing rating prediction for recommendation by metric learning with gray relational analysis

Chen

Zhang

Qishan

2021

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PurposeThe purpose of the paper is to improve the rating prediction accuracy in recommender systems (RSs) by metric learning (ML) method. The similarity metric of user and item is calculated with gray relational analysis.Design/methodology/approachFirst, the potential features of users and items are captured by exploiting ML, such that the rating prediction can be performed. In metric space, the user and item positions can be learned by training their embedding vectors. Second, instead of the traditional distance measurements, the gray relational analysis is employed in the evaluation of the position similarity between user and item, because the latter can reduce the impact of data sparsity and further explore the rating data correlation. On the basis of the above improvements, a new rating prediction algorithm is proposed. Experiments are implemented to validate the effectiveness of the algorithm.FindingsThe novel algorithm is evaluated by the extensive experiments on two real-world datasets. Experimental results demonstrate that the proposed model achieves remarkable performance on the rating prediction task.Practical implicationsThe rating prediction algorithm is adopted to predict the users' preference, and then, it provides personalized recommendations for users. In fact, this method can expand to the field of classification and provide potentials for this domain.Originality/valueThe algorithm can uncover the finer grained preference by ML. Furthermore, the similarity can be measured using gray relational analysis, which can mitigate the limitation of data sparsity.

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Section: Methodsmentioning

confidence: 99%

Section: Evaluation Algorithmsmentioning

confidence: 99%

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Enhancing rating prediction for recommendation by metric learning with gray relational analysis

Chen

Zhang

Qishan

2021

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“…Distance metric learning has become one of the most attractive research areas in machine learning, pattern recognition, and computer vision [33][34][35]. Ye et al [36] proposed an adaptive metric learning (AML) model, which combines the ideas of clustering and distance metric learning.…”

Section: Related Workmentioning

confidence: 99%

Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation

Liu

Xin²,

Jian

et al. 2020

Mathematical Problems in Engineering

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Point-of-interest (POI) recommendation is a valuable service to help users discover attractive locations in location-based social networks (LBSNs). It focuses on capturing users’ movement patterns and location preferences by using massive historical check-in data. In the past decade, matrix factorization has become a mature and widely used technology in POI recommendation. However, the inner product of latent vectors adopted in matrix factorization methods does not satisfy the triangle inequality property, which may limit the expressiveness and lead to suboptimal solutions. Besides, the extreme sparsity of check-in data makes it challenging to capture users’ movement preferences accurately. In this paper, we propose a joint geosequential preference and distance metric factorization framework, called GeoSeDMF, for POI recommendation. First, we introduce a distance metric factorization method that is capable of learning users’ personalized preferences from a position and distance perspective in the metric space. Specifically, we convert the user-POI interaction matrix into a distance matrix and factorize it into user and POI dense embeddings. Additionally, we measure users’ personalized preference for the POI by using the Euclidean distance metric instead of the inner product. Then, we model the users’ geospatial preference by applying a geographic weight coefficient and model the users’ sequential preference by using the Euclidean distance of continuous check-in locations. Moreover, a pointwise loss strategy and AdaGrad algorithm are adopted to optimize the positions and relationships of users and POIs in a metric space. Finally, experimental results on three large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.

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DNR: A Unified Framework of List Ranking With Neural Networks for Recommendation

Wei¹,

Qin

Zeng

2021

IEEE Access

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Metric Factorization with Item Cooccurrence for Recommendation

Cited by 4 publications

References 28 publications

Enhancing rating prediction for recommendation by metric learning with gray relational analysis

Enhancing rating prediction for recommendation by metric learning with gray relational analysis

Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation

DNR: A Unified Framework of List Ranking With Neural Networks for Recommendation

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