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2018 | OriginalPaper | Buchkapitel

NLPCC 2018 Shared Task User Profiling and Recommendation Method Summary by DUTIR_9148

verfasst von : Xiaoyu Chen, Jian Wang, Yuqi Ren, Tong Liu, Hongfei Lin

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

User profiling and personalized recommendation plays an important role in many business applications such as precision marketing and targeting advertisement. Since user data is heterogeneous, leveraging the heterogeneous information for user profiling and personalized recommendation is still a challenge. In this paper, we propose effective methods to solve two subtasks working in user profiling and recommendation. Subtask one is to predict users’ tags, we treat this subtask as a binary classification task, we combine users’ profile vector and social Large-scale Information Network Embedding (LINE) vector as user features, and use tag information as tag features, then apply a deep learning approach to predict which tags are related to a user. Subtask two is to predict the users a user would like to follow in the future. We adopt social-based collaborative filtering (CF) to solve this task. Our results achieve second place in both subtasks.

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Vorheriges Kapitel Ensemble of Neural Networks with Sentiment Words Translation for Code-Switching Emotion Detection

Nächstes Kapitel Overview of NLPCC 2018 Shared Task 1: Emotion Detection in Code-Switching Text

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Titel: NLPCC 2018 Shared Task User Profiling and Recommendation Method Summary by DUTIR_9148
verfasst von: Xiaoyu Chen
Jian Wang
Yuqi Ren
Tong Liu
Hongfei Lin
Verlag: Springer International Publishing
Buch: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-319-99500-7

Electronic ISBN: 978-3-319-99501-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-99501-4_38

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