Titlebook: On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE; OTM Confederated Int Robert Meersman,Zahir Tari Conference procee

arbovirus

Certainty

MEN

Shoujian Yu,Zhongming Han,Jiajin Le data severely impedes the further improvement of POI recommendation. Existing works jointly analyse user check-in behaviors (i.e., positive samples) and POI distribution to tackle this issue. However, introducing user multi-modal behaviors (e.g., online map query behaviors), as a supplement of user

Armada

混合

Jan L. G. Dietz,Nathalie Habingted to recognize that in practice user interaction sequences exhibit multiple user intentions. However, they still suffer from two major limitations: (1) negligence of the dynamic evolution of individual intentions; (2) improper aggregation of multiple intentions. In this paper we propose a novel .u

碳水化合物

Stefanie Rinderle,Manfred Reichert,Peter Dadamr, these methods tend to be a black box that cant not provide any explanation for users. To obtain the trust of users and improve the transparency, recent research starts to focus on the explanation of recommendations. Explainable Bayesian Personalized Ranking (EBPR) leverages the relevant item to p

留戀

索賠

幻影

Jean Ferrié,Nicolas Vidot,Michelle Cart data severely impedes the further improvement of POI recommendation. Existing works jointly analyse user check-in behaviors (i.e., positive samples) and POI distribution to tackle this issue. However, introducing user multi-modal behaviors (e.g., online map query behaviors), as a supplement of user

良心

Cécile Le Pape,Stéphane Gan?arski,Patrick Valduriezations. However, in many scenarios, there are a large number of cold-start users with limited user-item interactions. To address this challenge, some studies utilize auxiliary information to infer users’ interests. But with the increasing awareness of personal privacy protection, it is difficult to