1. A Graph Combination With Edge Pruning‐Based Approach for Author Name Disambiguation

    • Motivation: Author name disambiguation
    • Research gap: First, the namesake problem in which two or more authors with the same name publishes in a similar domain. Second, the diverse topic problem in which one author publishes in diverse topical domains with a different set of coauthors.
    • Proposed method: we initially propose a method named ATGEP for AND that addresses the namesake issue.

  2. Allocation of e-car charging: Assessing the utilization of charging infrastructures by location

    • Motivation: The availability of charging infrastructure
    • Research question: This paper aims to examine the distribution of the allocation of future charges to the various types of charging stations in order to provide a starting point for the evaluation of the need for charging infrastructure, i.e. its number, design and cost-effectiveness.
    • Proposed method: a new approach to derive the allocation of charging processes is applied by using demographic variables and decision rules.

  3. Recommendations and privacy in the arXiv system: A simulation experiment using historical data

    • Motivation: Recommendation and privacy.
    • Research gaps: Recommender systems may accelerate knowledge discovery in many fields. However, their users may be competitors guarding their ideas before publication or for other reasons.
    • Research work: We describe a simulation experiment to assess user privacy against targeted attacks, modeling recommendations based on co‐access data.

  4. Dynamic ridesharing with variable-ratio charging-compensation scheme for morning commute

    • Motivation: Ridesharing, Morning commute
    • Research gap: variable-ratio charging-compensation scheme (VCS)
    • Research work: The optimal VCS without imposing road pricing when the ridesharing platform minimizes the disutility or maximizes its profit is analyzed.

  5. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

    • Motivation: explainable
    • Research gap: the latent features make CF algorithms difficulty to explain the recommendation results to the users. with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features.
    • Proposed method: we propose the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keep a high prediction accuracy.