Learning Vertex Representations for Bipartite Networks

Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages

The preoperative platelet–lymphocyte ratio neutrophil–lymphocyte ratio: which is better as a prognostic factor in oral squamous cell carcinoma?

Objective: Recent studies have shown that the presence of systemic inflammation and platelet status correlate with poor survival in various cancers. The aim of this study was to evaluate the prognostic value of the preoperative platelet–lymphocyte ratio (PLR) and the neutrophil–lymphocyte ratio (NLR) in patients with oral squamous cell carcinoma (OSCC) undergoing surgery. Methods: In this study, 306 patients with OSCC who had surgery were enrolled. The optimal cutoff value of PLR and NLR was determined by receiver operating characteristic (ROC) curve analysis. The prognostic significance of both markers was determined by uni- and multivariate analysis. Results: The results showed that high NLR and PLR were classified using a cutoff value of 2.7 and 135, respectively, based on ROC curve analysis. Only PLR was associated with decreased disease-free survival [hazard ratio (HR) = 2.237; 95% confidence interval (CI): 1.401–3.571; p = 0.001] and overall survival [HR = 2.022; 95% CI: 1.266–3.228; p = 0.003] by both uni- and multivariate analysis. Conclusion: The preoperative PLR is superior to NLR as an independent indicator in predicting disease-free survival and overall survival in patients who undergo oral cancer resection for OSCC

π-Net: A parallel information-sharing network for shared-account cross-domain sequential recommendations

Sequential Recommendation (SR) is the task of recommending the next item based on a sequence of recorded user behaviors. We study SR in a particularly challenging context, in which multiple individual users share a single account (shared-account) and in which user behaviors are available in multiple domains (cross-domain). These characteristics bring new challenges on top of those of the traditional SR task. On the one hand, we need to identify different user behaviors under the same account in order to recommend the right item to the right user at the right time. On the other hand, we need to discriminate the behaviors from one domain that might be helpful to improve recommendations in the other domains. We formulate the Shared-account Cross-domain Sequential Recommendation (SCSR) task as a parallel sequential recommendation problem. We propose a Parallel Information-sharing Network (πNet) to simultaneously generate recommendations for two domains where user behaviors on two domains are synchronously shared at each timestamp. π-Net contains two core units: a shared account filter unit (SFU) and a cross-domain transfer unit (CTU). The SFU is used to address the challenge raised by shared accounts; it learns user-specific representations, and uses a gating mechanism to filter out information of some users that might be useful for another domain. The CTU is used to address the challenge raised by the cross-domain setting; it adaptively combines the information from the SFU at each timestamp and then transfers it to another domain. After that, π-Net estimates recommendation scores for each item in two domains by integrating information from both domains. To assess the effectiveness of π-Net, we construct a new dataset HVIDEO from real-world smart TV watching logs. To the best of our knowledge, this is the first dataset with both shared-account and cross-domain characteristics. We release HVIDEO to facilitate future research. Our experimental results demonstrate that π-Net outperforms state-of-the-art baselines in terms of MRR and Recall