A personalized recommendation method under the cloud platform based on users' long-term preferences and instant interests. (October 2022)

• Record Type: Journal Article
• Authors: Pei, Huining; Liu, Xinyu; Huang, Xueqin; Wu, Meng; Wen, Zhiqiang; Zhao, Fanghua
• Is Part Of: Advanced engineering informatics. Volume 54(2022)
• Publication Date: 2022-10
• Subjects: Cloud platform -- Personalized recommendations -- Long short-term preference -- Residual Bi-RNN -- Dual attention mechanism; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101763
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Rich consumer online text data are embedded in the cloud platform. Using new technologies has become a central issue for acquiring consumer preference, analyzing consumer demand, and performing personalized recommendation services. In order to recommend the cloud platform services efficiently and accurately, this paper proposes a personalized recommendation model referred to as Residual bi-directional Recurrent Neural Network with Dual Attentive mechanism (BiRDA) for the service recommend to cloud platforms, by combining users' long-term preferences with instant interest. The proposed recommender prototype is summarized as follows. (1) Analyzing the relationship between long-term preferences and instant interests based on co-opetition theory. (2) Extracting users' online text data from the cloud platform. (3) Deriving the product attribute words of user preference using an analysis of online text data. (4) Product attribute words are transformed into the form of word vectors. (5) The word vector is input into the Residual bi-directional Recurrent Neural Network (Res-BiRNN) to make the prediction. On the one hand, the long-term preference is expressed by the user's field of expertise (i.e., answer content). On the other hand, the even interest is expressed by the user's changing interest (i.e., question data). (6) Assigning different weights to long-term preferences and instant interest using the dual attention mechanism to output predictions. (7) Generating recommendation lists for users based on the predicted values. Accordingly, BiRDA is compared with five state-of-the-art recommendation methods (i.e., DREAM, BINN, SHAN, Caser, and DeepMove), as well as six variants of the BiRDA model, Using users' Q&A datasets from NiorcngeCDS cloud platform, XMAKE cloud platform, and Asksubarme cloud platform as examples. The experiments show that the proposed method is more efficient and accurate than the other models. Therefore, the study offers some important insights into allowing a large number of resources under the cloud platform to be fully utilized and provides a novel idea for the construction of the cloud platform front-end.