Abstract
Social networking sites have been growing at an unprecedented rate in recent years. User profiling and personalized recommendation plays an important role in social networking, such as targeting advertisement and personalized news feed. For NLPCC Task 8, there are two subtasks. Subtask one is User Tags Prediction (UTP), which is to predict tags related to a user. We consider UTP as a Multi Label Classification (MLC) problem and proposed a CNN-RNN framework to explicitly exploit the label dependencies. The proposed framework employs CNN to get the user profile representation and the RNN module captures the dependencies among labels. Subtask two, User Following Recommendation (UFR), is to recommend friends to the users. There are mainly two approaches: Collaborative Filtering (CF) and Most Popular Friends (MPF), and we adopted a combination of both. Our experiments show that both of our methods yield clear improvements in F1@K compared to other algorithms and achieved first place in both subtasks.
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Xie, Q., Wang, Y., Xu, Z., Yu, K., Wei, C., Yu, Z. (2018). First Place Solution for NLPCC 2018 Shared Task User Profiling and Recommendation. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_2
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DOI: https://doi.org/10.1007/978-3-319-99501-4_2
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