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Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Complementary product recommendation aims to provide users with products that are often purchased together to meet common needs. Existing complementary product recommendation methods mainly consider modeling the content characteristics（visual and textual content） of products, but do not consider the preferences of users when purchasing products. To this end, this paper designed a complementary product recommendation model that integrates user preferences, termed as CPRUP（complementary product recommendation models that integrate user preferences）.The model first computed the complementarity of image and text features between products; then it combined knowledge graph with attention mechanism, mines the correlation between user’s historical purchased products based on n-hop neighbors, and proposed a user representation based on knowledge graph to extract user preferences for complementary products; finally, it implemented joint learning of complementarity and user preferences based on neural networks. Experiments on the Amazon dataset show that the proposed CPRUP model has increased the ACC by 5%and the Precision by 4% compared to the suboptimal baseline model, indicating that the CPRUP model can more accurately recommend complementary products to users. [ABSTRACT FROM AUTHOR]

Complementary product recommendation is to recommend products to users that can be used together. Existing product recommendation methods consider all features of product’s images and texts, but do not consider the relationship between visual and textual modalities, and not all features contribute equally to the complementary relationship. Based on this situation, this paper proposes a complementary product recommendation model fusing with attention mechanism, dubbed as CPRFA. The model first enriches its feature representation with the product’s image and text information, and secondly, in order to unify heterogeneous products with multimodal information, it further converts them into image and text vector representations, and then uses the attention mechanism to adaptively assign weights to different features of the product, and finally learns the relationship between the product and the candidate products for the recommendation of complementary products. In the experiments based on the Amazon dataset, the CPRFA model proposed in this paper outperforms other baseline models in all indicators. Experiments show that the CPRFA model can recommend complementary products to users more accurately. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to fully mine nodes' features and better integrate these features simultaneously in the heterogeneous information network, this paper proposed a AMF L& GRec Firstly, AMFL & GRec used the Leader Rank algorithm to extract the target node' global sequence, and used a meta-path-bas ed heterogeneous information network embedding model to extract the node' local sequence,and used the skip-gram model to learn the node' global and local features. And then it used the self-attention mechanism to learn the preference of the target nodes' local and global features to obtain the feature representation of the target node in a single meta-path. Secondly, it used the self-attention mechanism to fuse the representation of the same node under different meta-paths to obtain the final feature representation. Finally, it utilized a multi-layer perceptron to achieve the task of rating prediction. This paper conducted a large number of experiments on two real datasets. The experimental results verify that the AMFL & GRec algorithm can not only capture the micro(local) structure of densely connected nodes, but also capture the global structure of the node in the network, and finally obtain nodes' overall (local +global) characteristics. At the same time, the experimental results also prove that the AMFL & GRec's rating prediction performance is better than the baselines. It proves that in the heterogeneous information network utilizing the self-attention mechanism to consider the nodes' preferences for local and global features and meta-paths can improve the accuracy of rating prediction. [ABSTRACT FROM AUTHOR]

Online reviews play a significant role in users' purchasing decisions. In order to improve their reputation or degrade their competitors' products, some sellers employ a large numbers of review spammers to write fake reviews systematically and strategically to mislead potential consumers. In order to detect such organized spammer groups, this paper proposed a group spam detection algorithm that comprehensively considered the network structure and the behavior characteristics of reviewers. In implementation, this algorithm first obtained the closeness between reviewers based on the relevance of ratings and review time, and constructed a reviewer relationship graph. Secondly, based on the constructed reviewer relationship graph, it used label propagation method to detect the community and got a set of candidate groups. Finally, it restored the corresponding bipartite graphs of the candidate groups, and then found the final spammers on each bipartite graph by taking contrast suspiciousness as a metric. Experimental results based on real datasets demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the ubiquitous network spammers on e-commerce platform, this paper proposed an algorithm considering network structure and time characteristics to detect the spammer groups in the comment network. The algorithm consisted of four steps: a) mining the target products that were vulnerahle to attack by the spammers based on the analysis of the structural characteristics of the comment network; b) this paper proposed an algorithm for mining the suspicious period when the spammer groups attacked the target product inspired by the "co-bursting phenomenon" ; c) this paper constructed the induced subgraph of target products-reviewers based on the data of target product in suspicious period, and applied hierarchical agglomerative clustering algorithm to generate candidate spammer groups on the subgraph; d) in order to filter out the normal users who shopped and commented during the suspicious period, this paper proposed a spammer groups purification method, and then classified the purified groups based on the behavior characteristics of the reviewers. The experimental results based on real data sets show that the proposed algorithm can accurately and efficiently detect the network spammer groups active on e-commerce websites. [ABSTRACT FROM AUTHOR]