Titlebook: Web Information Systems Engineering – WISE 2018; 19th International C Hakim Hacid,Wojciech Cellary,Rui Zhou Conference proceedings 2018 Spr

惡化

書目名稱Web Information Systems Engineering – WISE 2018影響因子(影響力)




書目名稱Web Information Systems Engineering – WISE 2018影響因子(影響力)學(xué)科排名




書目名稱Web Information Systems Engineering – WISE 2018網(wǎng)絡(luò)公開度




書目名稱Web Information Systems Engineering – WISE 2018網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Web Information Systems Engineering – WISE 2018被引頻次




書目名稱Web Information Systems Engineering – WISE 2018被引頻次學(xué)科排名




書目名稱Web Information Systems Engineering – WISE 2018年度引用




書目名稱Web Information Systems Engineering – WISE 2018年度引用學(xué)科排名




書目名稱Web Information Systems Engineering – WISE 2018讀者反饋




書目名稱Web Information Systems Engineering – WISE 2018讀者反饋學(xué)科排名

步兵

Integrating Collaborative Filtering and Association Rule Mining for Market Basket Recommendationional association rule recommendation algorithms cannot generate association rules from cold commodity items under big data environment. An implicit semantic model based on historical transaction data of all users is constructed to represent potential features of commodities and measure similarities

Parley

Unified User and Item Representation Learning for Joint Recommendation in Social Networkity of user-user matrix and user-item matrix issue create severe challenges, causing collaborative filtering methods to degrade significantly in their recommendation performance. Moreover, the factors those affect users’ preference for items and friends are complex in social networks. For example, u

生氣的邊緣

SARFM: A Sentiment-Aware Review Feature Mapping Approach for Cross-Domain Recommendation an effective approach to help improve quality of recommendations and to alleviate the problems of cold-start and data sparsity in recommendation systems. However, existing works on cross-domain algorithm mostly consider ratings, tags and the text information like reviews, and don’t take advantage o

案發(fā)地點(diǎn)

Integrating Collaborative Filtering and Association Rule Mining for Market Basket Recommendationional association rule recommendation algorithms cannot generate association rules from cold commodity items under big data environment. An implicit semantic model based on historical transaction data of all users is constructed to represent potential features of commodities and measure similarities

fulmination

Geographical Proximity Boosted Recommendation Algorithms for Real Estates to provide users with their personalized property recommendations to alleviate information overloading. Unlike the recommendation problems in traditional domains, the real estate recommendation has its unique characteristics: users’ preferences are significantly affected by the locations (e.g. sch

Capture

Unified User and Item Representation Learning for Joint Recommendation in Social Networkity of user-user matrix and user-item matrix issue create severe challenges, causing collaborative filtering methods to degrade significantly in their recommendation performance. Moreover, the factors those affect users’ preference for items and friends are complex in social networks. For example, u

OATH

Cross-domain Recommendation with Consistent Knowledge Transfer by Subspace Alignmenton methods is data sparsity, due to the limited number of observed user interaction with the products/services. Cross-domain recommender systems are developed to tackle this problem through transferring knowledge from a source domain with relatively abundant data to the target domain with scarce dat

CIS

Geographical Proximity Boosted Recommendation Algorithms for Real Estates to provide users with their personalized property recommendations to alleviate information overloading. Unlike the recommendation problems in traditional domains, the real estate recommendation has its unique characteristics: users’ preferences are significantly affected by the locations (e.g. sch

食料

Cross-domain Recommendation with Consistent Knowledge Transfer by Subspace Alignmenton methods is data sparsity, due to the limited number of observed user interaction with the products/services. Cross-domain recommender systems are developed to tackle this problem through transferring knowledge from a source domain with relatively abundant data to the target domain with scarce dat