# title
What Are You Known For? Learning User Topical Profiles with Implicit and Explicit Footprints

# abstract
2017 Copyright held by the owner/author(s). User interests and expertise are valuable but o.en hidden resources on social media. For example, Twi.er Lists and LinkedIn's Skill Tags provide a partial perspective on what users are known for (by aggregating crowd tagging knowledge), but the vast majority of users are untagged; their interests and expertise are essentially hidden from important applications such as personalized recommendation, community detection, and expert mining. A natural approach to overcome these limitations is to intelligently learn user topical pro.les by exploiting information from multiple, heterogeneous footprints: for instance, Twitter users who post similar hashtags may have similar interests, and YouTube users who upvote the same videos may have similar preferences. And yet identifying "similar" users by exploiting similarity in such a footprint space o.en provides con.icting evidence, leading to poor-quality user pro.les. In this paper, we propose a unified model for learning user topical pro.les that simultaneously considers multiple footprints. We show how these footprints can be embedded in a generalized optimization framework that takes into account pairwise relations among all footprints for robustly learning user pro.les. .rough extensive experiments, we .nd the proposed model is capable of learning high-quality user topical pro.les, and leads to a 10-15% improvement in precision and mean average error versus a crosstriadic factorization state-of-The-Art baseline.

# publicationOutlet
SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL

# nameOfConference
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

# publisher.label
Association for Computing Machinery (ACM)

# tags[]
12 Responsible Consumption and Production

# editorList[]
Kando, N., Sakai, T., Joho, H., Li, H., Vries, A., & White, R. W.