Location-based deep factorization machine model for service recommendation

Wang, Qingren; Zhang, Min; Zhang, Yiwen; Zhong, Jinqin; Sheng, Victor S.

doi:10.1007/s10489-021-02998-9

Cited by 7 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [31] believed that the state of unknown QoS is a hidden state, so they proposed a hidden state initialization and perception approach based on LDA. Different from the above idea, Wang et al [32] employed information entropy into the combination of FM and DNN to mine more useful features. Recently, Zhang et al [33] proposed a novel deep neural network model with multistage and multiscale feature fusion.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Location-Aware Deep Interaction Forest for Web Service QoS Prediction

Zhu,

Ding,

Yang

2024

Applied Sciences

View full text Add to dashboard Cite

With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.

show abstract

Section: Deep Learning Methodsmentioning

confidence: 99%

Location-Aware Deep Interaction Forest for Web Service QoS Prediction

Zhu,

Ding,

Yang

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…A kernel which detects the similarity is implemented in order to fetch the locality information through some specific networks, such as friendship network of users. Additionally, there are heterogeneous information network based recommendation models [20][21][22] that implemented the deep neural network learning methods to try improving the results and solving the issue.…”

Section: A Recommendation Systemsmentioning

confidence: 99%

REHREC: Review Effected Heterogeneous Information Network Recommendation System

Khalilzadeh,

Cicekli

2024

IEEE Access

View full text Add to dashboard Cite

Heterogeneous data networks have bunches of rich secret data that assist us in the creation of successful recommendation frameworks. Specifying the meaningful meta-paths helps us to extract the hidden information that exists in a heterogeneous data network. Although user and business nodes are used to specify these meaningful meta-paths, review nodes have not been used. Furthermore, written reviews assist us to obtain informative data about a point of interest in a recommendation system. We use new meta-paths which employs review nodes in order to extract the hidden information in heterogeneous data networks. In this work, it is tried to unify rich written review texts with the heterogeneous information network and their effects in recommendation systems are analyzed. Our experiments show that the review texts improve the recommendation system when meaningful meta-paths are chosen.

show abstract