Q&R: A two-stage approach toward interactive recommendation

K Christakopoulou, A Beutel, R Li, S Jain… - Proceedings of the 24th …, 2018 - dl.acm.org
Recommendation systems, prevalent in many applications, aim to surface to users the right
content at the right time. Recently, researchers have aspired to develop conversational …

[PDF][PDF] Improving Implicit Recommender Systems with View Data.

J Ding, G Yu, X He, Y Quan, Y Li, TS Chua, D Jin, J Yu - IJCAI, 2018 - ijcai.org
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 …

Sampler design for bayesian personalized ranking by leveraging view data

J Ding, G Yu, X He, F Feng, Y Li… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for
optimizing recommendation models. It is widely known that the performance of BPR …

Improving implicit recommender systems with auxiliary data

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 …

Editable User Profiles for Controllable Text Recommendations

S Mysore, M Jasim, A McCallum… - Proceedings of the 46th …, 2023 - dl.acm.org
Methods for making high-quality recommendations often rely on learning latent
representations from interaction data. These methods, while performant, do not provide …

MMM: multi-source multi-net micro-video recommendation with clustered hidden item representation learning

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 …

FACULTY MEMBERS'DIGITAL FOOTPRINT EXPERIENCES AND DIGITAL FOOTPRINT AWARENESS

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 …

Personalized mobile App recommendation by learning user's interest from social media

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 …

SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding

S Najafipour, S Hosseini, W Hua… - … on Knowledge and …, 2020 - ieeexplore.ieee.org
Linking authors of short-text contents has important usages in many applications, including
Named Entity Recognition (NER) and human community detection. However, certain …

Extracting, mining and predicting users' interests from social media

F Zarrinkalam, S Faralli, G Piao… - … and Trends® in …, 2020 - nowpublishers.com
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 …