Integrating heterogeneous information via flexible regularization framework for recommendation

Shi, Chuan; Liu, Jian; Zhuang, Fuzhen; Yu, Philip S.; Wu, Bin

doi:10.1007/s10115-016-0925-0

Cited by 70 publications

(35 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several efforts have been made for HIN based recommendation. Most of these works mainly leverage meta-path based similarities to enhance the recommendation performance [7], [9]- [11]. Next, we will present a new heterogeneous network embedding based approach to this task, which is able to effectively exploit the information reflected in HINs.…”

Section: Preliminarymentioning

confidence: 99%

Heterogeneous Information Network Embedding for Recommendation

Shi

Zhao

et al. 2019

IEEE Trans. Knowl. Data Eng.

Self Cite

1,008

479

View full text Add to dashboard Cite

Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called HIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.

show abstract

Section: Preliminarymentioning

confidence: 99%

Heterogeneous Information Network Embedding for Recommendation

Shi

Zhao

et al. 2019

IEEE Trans. Knowl. Data Eng.

Self Cite

1,008

479

View full text Add to dashboard Cite

show abstract

“…Luo et al [14] proposed a collaborative filtering based social recommendation containing heterogeneous relations. Shi et al [25] introduced weighted heterogeneous information network In [24], the similarities of both users and items are evaluated unser dual regularization framework.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

Jin

Qin

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

105

View full text Add to dashboard Cite

There is an influx of heterogeneous information network (HIN) based recommender systems in recent years since HIN is capable of characterizing complex graphs and contains rich semantics. Although the existing approaches have achieved performance improvement, while practical, they still face the following problems. On one hand, most existing HIN-based methods rely on explicit path reachability to leverage path-based semantic relatedness between users and items, e.g., metapath-based similarities. These methods are hard to use and integrate since path connections are sparse or noisy, and are often of different lengths. On the other hand, other graph-based methods aim to learn effective heterogeneous network representations by compressing node together with its neighborhood information into single embedding before prediction. This weakly coupled manner in modeling overlooks the rich interactions among nodes, which introduces an early summarization issue. In this paper, we propose an end-to-end Neighborhood-based Interaction Model for Recommendation (NIRec) to address above problems. Specifically, we first analyze the significance of learning interactions in HINs and then propose a novel formulation to capture the interactive patterns between each pair of nodes through their metapath-guided neighborhoods. Then, to explore complex interactions between metapaths and deal with the learning complexity on large-scale networks, we formulate interaction in a convolutional way and learn efficiently with fast Fourier transform. The extensive experiments on four different types of heterogeneous graphs demonstrate the performance gains of NIRec comparing with state-of-the-arts. To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations. CCS CONCEPTS • Information systems → Data mining; • Computer systems organization → Heterogeneous (hybrid) systems.

show abstract

“…HIN has been widely used in many data mining tasks [8]. HIN based recommendations also have been proposed to utilize rich heterogeneous information in recommender systems, while they usually focus on rating prediction with the "shallow" model [5], [11].…”

Section: Heterogeneous Information Networkmentioning

confidence: 99%

“…In order to exploit users' similar purchase preference, latent factor models (e.g., matrix factorization) [2], [3] have been proposed, which usually factorize the user-item interaction matrix (e.g., rating matrix) into two low-rank user- specific and item-specific factors, and then use the low-rank factors to make predictions. Since latent factor models may suffer from data sparsity, many extended latent factor models integrate auxiliary information into the matrix factorization framework, such as social recommendation [4] and heterogeneous network based recommendation [5]. Recently, with the surge of deep learning, deep neural networks are also employed to deeply capture the latent features of users and items for recommendation.…”

Section: Introductionmentioning

confidence: 99%

Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks

Shi

Han

et al. 2021

IEEE Trans. Knowl. Data Eng.

Self Cite

View full text Add to dashboard Cite

Recently, recommender systems play a pivotal role in alleviating the problem of information overload. Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the interaction information between users and items, although some recently extended models utilize some auxiliary information to learn a unified latent factor for users and items. The unified latent factor only represents the characteristics of users and the properties of items from the aspect of purchase history. However, the characteristics of users and the properties of items may stem from different aspects, e.g., the brand-aspect and category-aspect of items. Moreover, the latent factor models usually use the shallow projection, which cannot capture the characteristics of users and items well. Deep neural network has shown tremendous potential to model the non-linearity relationship between users and items. It can be used to replace shallow projection to model the complex correlation between users and items. In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors. Through modelling the rich object properties and relations in recommender system as a heterogeneous information network, NeuACF first extracts different aspect-level similarity matrices of users and items respectively through different meta-paths, and then feeds an elaborately designed deep neural network with these matrices to learn aspect-level latent factors. Finally, the aspect-level latent factors are fused for the top-N recommendation. Moreover, to fuse information from different aspects more effectively, we further propose NeuACF++ to fuse aspect-level latent factors with self-attention mechanism. Extensive experiments on three real world datasets show that NeuACF and NeuACF++ significantly outperform both existing latent factor models and recent neural network models.

show abstract

Integrating heterogeneous information via flexible regularization framework for recommendation

Cited by 70 publications

References 26 publications

Heterogeneous Information Network Embedding for Recommendation

Heterogeneous Information Network Embedding for Recommendation

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks

Contact Info

Product

Resources

About