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These models have been applied to various application domains where capturing event dynamics in continuous time is beneficial, such as education systems, social networks, and recommender systems. However, current MTPPs suffer from two major limitations, i.e., inefficient representation of event dynamic\u2019s influence on marker distribution and losing fine-grained representation of historical marker distributions in the modeling. Motivated by these limitations, we propose a novel model called\n            <jats:underline>M<\/jats:underline>\n            arked P\n            <jats:underline>o<\/jats:underline>\n            int Processes with\n            <jats:underline>M<\/jats:underline>\n            emory-\n            <jats:underline>E<\/jats:underline>\n            nhanced\n            <jats:underline>N<\/jats:underline>\n            eural Ne\n            <jats:underline>t<\/jats:underline>\n            works (MoMENt) that can capture the bidirectional interrelations between markers and event dynamics while providing fine-grained marker representations. Specifically, MoMENt is constructed of two concurrent networks: Recurrent Activity Updater (RAU) to capture model event dynamics and Memory-Enhanced Marker Updater (MEMU) to represent markers. Both RAU and MEMU components are designed to update each other at every step to model the bidirectional influence of markers and event dynamics. To obtain a fine-grained representation of maker distributions, MEMU is devised with external memories that model detailed marker-level features with latent component vectors. Our extensive experiments on six real-world user interaction datasets demonstrate that MoMENt can accurately represent users\u2019 activity dynamics, boosting time, type, and marker predictions, as well as recommendation performance up to 76.5%, 65.6%, 77.2%, and 57.7%, respectively, compared to baseline approaches. Furthermore, our case studies show the effectiveness of MoMENt in providing meaningful and fine-grained interpretations of user-system relations over time, e.g., how user choices influence their future preferences in the recommendation domain.\n          <\/jats:p>","DOI":"10.1145\/3649504","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T12:27:24Z","timestamp":1709209644000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["MoMENt: Marked Point Processes with Memory-Enhanced Neural Networks for User Activity Modeling"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8933-3279","authenticated-orcid":false,"given":"Sherry","family":"Sahebi","sequence":"first","affiliation":[{"name":"State University of New York at Albany, Albany, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4591-1105","authenticated-orcid":false,"given":"Mengfan","family":"Yao","sequence":"additional","affiliation":[{"name":"State University of New York at Albany, Albany, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3913-7836","authenticated-orcid":false,"given":"Siqian","family":"Zhao","sequence":"additional","affiliation":[{"name":"State University of New York at Albany, Albany, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4109-3501","authenticated-orcid":false,"given":"Reza","family":"Feyzi Behnagh","sequence":"additional","affiliation":[{"name":"State University of New York at Albany, Albany, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_3_2","unstructured":"Massil Achab Emmanuel Bacry St\u00e9phane Ga\u00efffas Iacopo Mastromatteo and Jean-Fran\u00e7ois Muzy. 2018. 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