Personalized entity recommendation

Yu, Xiao; Ren, Xiang; Sun, Yizhou; Gu, Quanquan; Sturt, Bradley; Khandelwal, Urvashi; Norick, Brandon; Han, Jiawei

doi:10.1145/2556195.2556259

Cited by 582 publications

(75 citation statements)

References 27 publications

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“…rating time, rating location). Other terms may be used to indicate a ributes interchangably such as metadata [57], features [18] , taxonomy [53], entities [116], demographical data [84], categories [19], contexture information [107], etc. e above setups all share the same mathematical representation; thus technically we do not distinguish them in this paper.…”

Section: Comparison Experimentsmentioning

confidence: 99%

“…We notice several relevant works that perform low-rank factorization or representation learning in heterogeneous graphs, such as [48,59,73,77,80,116,122]. e interactions of users and items can be represented by a heterogeneous graph of two node types.…”

Section: Heterogeneous Graphsmentioning

confidence: 99%

“…With di erent de nitions of f θ and p, the two-stage process may run only once or iteratively until convergence. e de nition of s(u, i) in surveyed papers includes PageRank [48,59], PathSim [116] and so on. e likelihood function p guides the similarity-related parameters θ to t the distribution objective of observed similarities s or ratings R. e objective may be given a ributes X as learning auxiliary.…”

Section: Heterogeneous Graphsmentioning

confidence: 99%

See 2 more Smart Citations

Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

et al. 2020

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A ribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). is paper surveys works in the past decade developing a ribute-aware CF systems, and discovered that mathematically they can be classi ed into four di erent categories. We provide the readers not only the high level mathematical interpretation of the existing works in this area but also the mathematical insight for each category of models. Finally we provide in-depth experiment results comparing the e ectiveness of the major works in each category.

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Section: Comparison Experimentsmentioning

confidence: 99%

Section: Heterogeneous Graphsmentioning

confidence: 99%

Section: Heterogeneous Graphsmentioning

confidence: 99%

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Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

et al. 2020

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“…Following this paradigm, a great many applications such as classification, clustering, recommendation, and outlier detection have been studied [16, 17, 19, 20, 25, 28]. However, many of these existing works rely on feature engineering [20, 25, 28]. Meanwhile, we aim at proposing an unsupervised feature learning method for general HINs that can serve as the basis for different downstream applications.…”

Section: Related Workmentioning

confidence: 99%

“…Such objects and relations, acting as strongly-typed nodes and edges, constitute numerous heterogeneous information networks (HINs) [16, 19]. HINs have received increasing interests in the past decade due to its capability of retaining the rich type information, as well as the accompanying wide applications such as recommender system [25], clustering [20], and outlier detection [28]. As an example, the IMDb network is an HIN containing information about users’ preferences over movies and have five different node types: user, movie, actor, director, and genre.…”

Section: Introductionmentioning

confidence: 99%

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

Shi¹,

Gui²,

Zhu³

et al. 2018

Proceedings of the 2018 SIAM International Conference on Data Mining

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Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due to the heterogeneity in HINs, the typed edges may not fully align with each other. In order to capture the semantic subtlety, we propose the concept of aspects with each aspect being a unit representing one underlying semantic facet. Meanwhile, network embedding has emerged as a powerful method for learning network representation, where the learned embedding can be used as features in various downstream applications. Therefore, we are motivated to propose a novel embedding learning framework—ASPEM—to preserve the semantic information in HINs based on multiple aspects. Instead of preserving information of the network in one semantic space, ASPEM encapsulates information regarding each aspect individually. In order to select aspects for embedding purpose, we further devise a solution for ASPEM based on dataset-wide statistics. To corroborate the efficacy of ASPEM, we conducted experiments on two real-words datasets with two types of applications—classification and link prediction. Experiment results demonstrate that ASPEM can outperform baseline network embedding learning methods by considering multiple aspects, where the aspects can be selected from the given HIN in an unsupervised manner.

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Content‐based and knowledge graph‐based paper recommendation: Exploring user preferences with the knowledge graphs for scientific paper recommendation

Tang

Liu

Qian

2021

Concurrency and Computation

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Researchers usually face difficulties in finding scientific papers relevant to their research interests due to increasing growth. Recommender systems emerge as a leading solution to filter valuable items intelligently. Recently, deep learning algorithms, such as convolutional neural network, improved traditional recommendation technologies, for example, the graph‐based or content‐based methods. However, existing graph‐based methods ignore high‐order association between users and items on graphs, and content‐based methods ignore global features of texts for explicit user preferences. Therefore, this paper proposes a Content‐based and knowledge Graph‐based Paper Recommendation method (CGPRec), which uses a two‐layer self‐attention block to obtain global features of texts for more complete explicit user preferences, and proposes an improved graph convolutional network for modeling high‐order associations on the knowledge graph to mine implicit user preferences. And the knowledge graph in this paper is constructed with concept nodes, user nodes, paper nodes, and other meta‐data nodes. Experimental results on a public dataset, CiteULike‐a, and a real application log dataset, AHData, show that our model outperforms compared with baseline methods.

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Personalized entity recommendation

Cited by 582 publications

References 27 publications

Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

Content‐based and knowledge graph‐based paper recommendation: Exploring user preferences with the knowledge graphs for scientific paper recommendation

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