Titlebook: Web and Big Data; 6th International Jo Bohan Li,Lin Yue,Toshiyuki Amagasa Conference proceedings 2023 The Editor(s) (if applicable) and The

Tortuous

Eir-Ripp: Enriching Item Representation for?Recommendation with?Knowledge Graphformation to the recommended items. Existing methods either use knowledge graph as an auxiliary information to mine users’ interests, or use knowledge graph to establish relationships between items via their hidden information. However, these methods usually ignore the interaction between users and

Iatrogenic

User Multi-behavior Enhanced POI Recommendation with?Efficient and?Informative Negative Sampling 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

粗俗人

使殘廢

Neighborhood Constraints Based Bayesian Personalized Ranking for?Explainable Recommendationr, 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

A保存的

Neighborhood Constraints Based Bayesian Personalized Ranking for?Explainable Recommendationr, 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

interrupt

Hierarchical Aggregation Based Knowledge Graph Embedding for?Multi-task Recommendationportant emerged frontier research direction, helps complement the available information of different tasks and improves recommendation performance effectively. However, the existing multi-task methods ignore high-order information between entities. At the same time, the existing multi-hop neighbour

聯(lián)合

Hierarchical Aggregation Based Knowledge Graph Embedding for?Multi-task Recommendationportant emerged frontier research direction, helps complement the available information of different tasks and improves recommendation performance effectively. However, the existing multi-task methods ignore high-order information between entities. At the same time, the existing multi-hop neighbour

GRILL

Mixed-Order Heterogeneous Graph Pre-training for?Cold-Start Recommendationon to sparse user-item interactions, which can be used to alleviate the cold-start problem. However, most existing models based on graph neural networks (GNNs) only consider the user-item interactions as supervision signals, making them unable to effectively exploit the side information. In this pap

踉蹌

Urologist

MISRec: Multi-Intention Sequential Recommendationted 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