Multi-graph Attention Fusion Network for Paper Recommendation Considering Group Information in Scientific Social Networks

In scientific social networks, group information has become an important auxiliary information to enhance the performance of paper recommendation, as many researchers prefer to obtain interested papers by joining groups. However, the existing paper recommendation methods failed to make full use of group information. In this paper, a paper recommendation method considering group information with multi-graph attention fusion network (GI-MGAF) is proposed. Specifically, in the graph construction layer, we construct researcher-paper bipartite graph, group-researcher bipartite graph and group-paper bipartite graph. In the information propagation layer, graph attention networks (GAT) are used to learn the node representations on the constructed bipartite graphs. In the information fusion layer, the researcher-level attention and paper-level attention are developed to respectively fuse the representations of researchers and papers. Experiments were conducted on the real world CiteULike dataset and the results demonstrate the effectiveness of the proposed GI-MGAF method

Biases in scholarly recommender systems: impact, prevalence, and mitigation

We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market