DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain

Bi, Yanchao; Song, Liqiang; Yao, Mengqiu; Wu, Zhenyu; Wang, Jianming; Xiao, Jing

doi:10.1145/3397271.3401193

Cited by 46 publications

(27 citation statements)

References 11 publications

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“…Regarding the KG representation method (KGE), the use of a predominant method was not found. Different works highlight the use of different methods: TransE [10], TransD [68], RotatE [50], and TransH [20].…”

Section: Knowledge Graphs and Semantic Web Technologiesmentioning

confidence: 99%

“…Other works evidencing the use of multiple entities and data dimensions such as (1) Reference [68] present DCDIR, which utilizes a cross-domain mechanism to give personalized recommendations for new users in the insurance domain; (2) the KG built-in [20] incorporates data from supply-demand networks between business services and users, community network structures between users and between services, POIs, and detailed service content; (3) in [20], the KGE method called TransH is used to create dense representations of a KG; TransH facilitated the prediction of underlying relationships between users, POIs, and business services accurately.…”

Section: E-commerce Business and Financial Sectormentioning

confidence: 99%

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A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Chicaiza

Valdiviezo-Díaz

2021

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In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.

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Section: Knowledge Graphs and Semantic Web Technologiesmentioning

confidence: 99%

Section: E-commerce Business and Financial Sectormentioning

confidence: 99%

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Chicaiza

Valdiviezo-Díaz

2021

Information

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“…Cold start, in the beginning, refers to the way a computer cuts off power and restarts, and later extends to the state of a product before a new product user comes into existence, establishes an effective relationship with the user, and continues to generate content and interaction. Cold-start problem is a common research question in recommendation systems [16][17][18] and its biggest problem with cold start is that the system cannot do anything about not collecting enough user information or making any inferences about the project.…”

Section: Cold-start Problemmentioning

confidence: 99%

Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

Tey

Lin

et al. 2021

J Big Data

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Recent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.

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“…DARec [80] employs the domain adaptation technique in [23,33] to learn domain-invariant user representations for CDR and it has achieved remarkable performance. Further, some works [3,58,85] propose to model domain-speciic features of user representations by employing a multi-layer perceptron (MLP) as the mapping function across domains.…”

Section: Introductionmentioning

confidence: 99%

Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation

Chen

Zhang

Tsang

et al. 2023

ACM Trans. Inf. Syst.

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Cross domain recommendation (CDR) is one popular research topic in recommender systems. This paper focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learn the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of choosing samples to train the mapping. In this paper, we attempt to learn both features of user preferences in a more principled way. We assume that each user’s preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformation. Based on this assumption, we propose an equivalent transformation learner (ETL) which models the joint distribution of user behaviors across domains. The equivalent transformation in ETL relaxes the idea of shared-user representation and allows the learned preferences in different domains to preserve the domain-specific features as well as the overlapped features. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-the-art methods. Codes and data are available online: https://github.com/xuChenSJTU/ETL-master

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DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain

Cited by 46 publications

References 11 publications

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation

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