Most existing recommender systems leverage the primary feedback data only, such as the purchase records in E-commerce. In this work, we additionally integrate view data into …
Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR …
J Ding, G Yu, Y Li, X He, D Jin - ACM Transactions on Information …, 2020 - dl.acm.org
Most existing recommender systems leverage the primary feedback only, despite the fact that users also generate a large amount of auxiliary feedback. These feedback usually …
Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide …
J Ma, J Wen, M Zhong, W Chen, X Li - Data Science and Engineering, 2019 - Springer
Unlike traditional video recommendations, micro-video inherits the characteristics of social platforms, such as social relation. A large amount of micro-videos showing explosive growth …
S Karabatak, M Alanoğlu - Atatürk Üniversitesi Kazım Karabekir …, 2022 - dergipark.org.tr
This study aimed to reveal the relationships between faculty members' gender and Internet usage time (daily usage time and years of use), their use of online environments, their digital …
Z Tu, Y Li, P Hui, L Su, D Jin - IEEE Transactions on Mobile …, 2019 - ieeexplore.ieee.org
The diversity of personal interest and preference of mobile users results in a wide spectrum of mobile app usage, and it is important to predict such app preference in order to provide …
Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain …
The abundance of user generated content on social media provides the opportunity to build models that are able to accurately and effectively extract, mine and predict users' interests …