Recommending Relevant Papers to Conference Participants: a Deep Learning Driven Content-based Approach.

Federico Rios, Paolo Rizzo, Francesco Puddu, Federico Romeo, Andrea Lentini, Giuseppe Asaro, Filippo Rescalli,Cristiana Bolchini,Paolo Cremonesi

User Modeling, Adaptation, and Personalization (UMAP)(2022)

引用 0|浏览8
暂无评分
摘要
We introduce a novel system for personalized recommendations to conference attendees, to highlight the papers in the program that best match the attendee’s interests. To this end, we extend traditional structure-agnostic recommender system techniques through the use of deep learning, to exploit the rich semantic and topological information given by the abstracts of the papers and the citation relationship. The ultimate goal is twofold: i) to help attendees single out from a rich program the papers they most likely would like to see presented, and ii) to perform a tailored advertisement of an upcoming event to past attendees by catching their attention with specific contributions in the program of the conference.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要