%0 Conference Paper %1 Rios_2022 %A Rios, Federico %A Rizzo, Paolo %A Puddu, Francesco %A Romeo, Federico %A Lentini, Andrea %A Asaro, Giuseppe %A Rescalli, Filippo %A Bolchini, Cristiana %A Cremonesi, Paolo %B Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization %D 2022 %I ACM %K academic-reference conference recommender umap2022 %P 52-57 %R 10.1145/3511047.3536413 %T Recommending Relevant Papers to Conference Participants: a Deep Learning Driven Content-based Approach %U https://doi.org/10.1145%2F3511047.3536413 %X 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.

@inproceedings{Rios_2022, abstract = {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.}, added-at = {2022-07-25T04:23:49.000+0200}, author = {Rios, Federico and Rizzo, Paolo and Puddu, Francesco and Romeo, Federico and Lentini, Andrea and Asaro, Giuseppe and Rescalli, Filippo and Bolchini, Cristiana and Cremonesi, Paolo}, biburl = {https://www.bibsonomy.org/bibtex/2e446acb32b3b2a90c330d2e9f9da2428/brusilovsky}, booktitle = {Adjunct Proceedings of the 30th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Recommending Relevant Papers to Conference Participants: a Deep Learning Driven Content-based Approach | Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3511047.3536413}, interhash = {f816b0db88ebf19cbfe749d1694b8048}, intrahash = {e446acb32b3b2a90c330d2e9f9da2428}, keywords = {academic-reference conference recommender umap2022}, month = jul, pages = {52-57}, publisher = {{ACM}}, timestamp = {2022-07-25T04:23:49.000+0200}, title = {Recommending Relevant Papers to Conference Participants: a Deep Learning Driven Content-based Approach}, url = {https://doi.org/10.1145%2F3511047.3536413}, year = 2022 }