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Intell. Syst. Technol."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>\n            Social networking services provide platforms for users to perform two kinds of behaviors: consumption behavior (e.g., recommending items of interest) and social link behavior (e.g., recommending potential social links). Accurately modeling and predicting users\u2019 two kinds of behaviors are two core tasks in these platforms with various applications. Recently, with the advance of neural networks, many neural-based models have been designed to predict a single users\u2019 behavior, i.e., social link behavior or consumption behavior. Compared to the classical shallow models, these neural-based models show better performance to drive a user\u2019s behavior by modeling the complex patterns. However, there are few works exploiting whether it is possible to design a neural-based model to jointly predict users\u2019 two kinds of behaviors to further enhance the prediction performance. In fact, social scientists have already shown that users\u2019 two kinds of behaviors are not isolated; people trend to the consumption recommendation of friends on social platforms and would like to make new friends with like-minded users. While some previous works jointly model users\u2019 two kinds of behaviors with shallow models, we argue that the correlation between users\u2019 two kinds of behaviors are complex, which could not be well-designed with shallow linear models. To this end, in this article, we propose a neural joint behavior prediction model named\n            <jats:italic>Neural Joint Behavior Prediction Model (NJBP)<\/jats:italic>\n            to mutually enhance the prediction performance of these two tasks on social networking platforms. Specifically, there are two key characteristics of our proposed model: First, to model the correlation of users\u2019 two kinds of behaviors, we design a fusion layer in the neural network to model the positive correlation of users\u2019 two kinds of behaviors. Second, as the observed links in the social network are often very sparse, we design a new link-based loss function that could preserve the social network topology. After that, we design a joint optimization function to allow the two behaviors modeling tasks to be trained to mutually enhance each other. Finally, extensive experimental results on two real-world datasets show that our proposed method is on average 7.14% better than the best baseline on social link behavior while 6.21% on consumption behavior prediction. Compared with the pair-wise loss function on two datasets, our proposed link-based loss function improves at least 4.69% on the social link behavior prediction and 4.72% on the consumption behavior prediction.\n          <\/jats:p>","DOI":"10.1145\/3406540","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T10:29:52Z","timestamp":1601029792000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["A Joint Neural Model for User Behavior Prediction on Social Networking Platforms"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9629-7798","authenticated-orcid":false,"given":"Junwei","family":"Li","sequence":"first","affiliation":[{"name":"Hefei University of Technology, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le","family":"Wu","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richang","family":"Hong","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Ge","sequence":"additional","affiliation":[{"name":"The University of Arizona, Tucson, AZ"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Kuaishou Technology Company Limited, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,9,25]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601412"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0908800106"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3133083"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974348.10"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.","author":"van den Berg Rianne","year":"2018","unstructured":"Rianne van den Berg , Thomas N. 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