Social explorative attention based recommendation for content distribution platforms

Xiao, Wenyi; Zhao, Huan; Pan, Haojie; Song, Yangqiu; Zheng, Vincent W.; Yang, Qiang

doi:10.1007/s10618-020-00729-1

Cited by 3 publications

(1 citation statement)

References 54 publications

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“…A neural co-attention model utilizes auxiliary information of meta-based neighbors for top-n recommendation of heterogeneous information networks [35]. By leveraging the higher-order friends in the social network, Xiao et al [36] designed a social explorative attention network to make personal interest recommendations. Ye et al [37] utilized both influence graph and the preference graph to fuse different user and item embeddings to make rating predictions.…”

Section: Attention Network For Collaborative Filteringmentioning

confidence: 99%

A Multiscale Neighbor-Aware Attention Network for Collaborative Filtering

Zheng,

Jing,

Cao

et al. 2023

Electronics

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Most recommender systems rely on user and item attributes or their interaction records to find similar neighbors for collaborative filtering. Existing methods focus on developing collaborative signals from only one type of neighbors and ignore the unique contributions of different types of neighbor views. This paper proposes a multiscale neighbor-aware attention network for collaborative filtering (MSNAN). First, attribute-view neighbor embedding is modeled to extract the features of different types of neighbors with co-occurrence attributes, and interaction-view neighbor embedding is leveraged to describe the fine-grained neighborhood behaviors of ratings. Then, a matched attention network is used to identify different contributions of multiscale neighbors and capture multiple types of collaborative signals for overcoming sparse recommendations. Finally, we make the rating prediction by a joint learning of multi-task loss and verify the positive effect of the proposed MSNAN on three datasets. Compared with traditional methods, the experimental results of the proposed MSNAN not only improve the accuracy in MAE and RMSE indexes, but also solve the problem of poor performance for recommendation in sparse data scenarios.

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Section: Attention Network For Collaborative Filteringmentioning

confidence: 99%

A Multiscale Neighbor-Aware Attention Network for Collaborative Filtering

Zheng,

Jing,

Cao

et al. 2023

Electronics

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Side Information Fusion for Recommender Systems over Heterogeneous Information Network

Zhao

Yao

Song

et al. 2021

ACM Trans. Knowl. Discov. Data

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Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users’ preferences (ratings) based on their past behaviors. Recently, various types of side information beyond the explicit ratings users give to items, such as social connections among users and metadata of items, have been introduced into CF and shown to be useful for improving recommendation performance. However, previous works process different types of information separately, thus failing to capture the correlations that might exist across them. To address this problem, in this work, we study the application of heterogeneous information network (HIN), which offers a unifying and flexible representation of different types of side information, to enhance CF-based recommendation methods. However, we face challenging issues in HIN-based recommendation, i.e., how to capture similarities of complex semantics between users and items in a HIN, and how to effectively fuse these similarities to improve final recommendation performance. To address these issues, we apply metagraph to similarity computation and solve the information fusion problem with a “matrix factorization (MF) + factorization machine (FM)” framework. For the MF part, we obtain the user-item similarity matrix from each metagraph and then apply low-rank matrix approximation to obtain latent features for both users and items. For the FM part, we apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and, at the same time, identify the useful metagraphs. Besides FMG, a two-stage method, we further propose an end-to-end method, hierarchical attention fusing, to fuse metagraph-based similarities for the final recommendation. Experimental results on four large real-world datasets show that the two proposed frameworks significantly outperform existing state-of-the-art methods in terms of recommendation performance.

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An Attentive Survey of Attention Models

ChaudhariSneha

MithalVarun

PolatkanGungor

et al. 2021

ACM Trans. Intell. Syst. Technol.

367

117

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Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we propose a taxonomy that groups existing techniques into coherent categories. We review salient neural architectures in which attention has been incorporated and discuss applications in which modeling attention has shown a significant impact. We also describe how attention has been used to improve the interpretability of neural networks. Finally, we discuss some future research directions in attention. We hope this survey will provide a succinct introduction to attention models and guide practitioners while developing approaches for their applications.

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Social explorative attention based recommendation for content distribution platforms

Cited by 3 publications

References 54 publications

A Multiscale Neighbor-Aware Attention Network for Collaborative Filtering

A Multiscale Neighbor-Aware Attention Network for Collaborative Filtering

Side Information Fusion for Recommender Systems over Heterogeneous Information Network

An Attentive Survey of Attention Models

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