Modeling Social Media Content With Word Vectors for Recommendation
Speaker (s):
DING Ying
PhD Candidate
School of Information Systems
Singapore Management University
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Date:
Time:
Venue:
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December 4, 2015, Friday
9:50am - 10:30am
Meeting Room 4.4, Level 4
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902
We look forward to seeing you at this research seminar.
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About the Talk
In social media, recommender systems are becoming more and more important. Different techniques have been designed for recommendations under various scenarios, but many of them do not use user-generated content, which potentially reflects users opinions and interests. Although a few studies have tried to combine user-generated content with rating or adoption data, they mostly rely on lexical similarity to calculate textual similarity. However, in social media, a diverse range of words is used. This renders the traditional ways of calculating textual similarity ineffective. In this work, we apply vector representation of words to measure the semantic similarity between text. We design a model that seamlessly integrates word vectors into a joint model of user feedback and text content. Extensive experiments on datasets from various domains prove that our model is effective in both recommendation and topic discovery in social media.
This a pre-conference talk for 7th International Conference on Social Informatics (SocInfo 2015).
About the Speaker
DING Ying is a PhD candidate in School of Information Systems, Singapore Management University, under the supervision of Assistant Professor JIANG Jing. He works in the area of text mining and recommender systems. His primary research interest focuses on using advanced techniques in natural language processing and machine learning to improve the performance of existing recommender systems. He has also done some exploration in text summarization, topic modelling and social media mining.
