Qidi Xu
Fumin Shen
ABSTRACT
TV series correlation computing is one of the most important tasks of personalized online streaming services. With the relevance of TV series and viewer feedback, we can calculate the TV series correlation table based on the viewer's implicit feedback which does not perform well for the newly added "cold start" TV series. In this paper, we aim to improve correlation computing within the cold-start phase. We propose a framework named Time-aware Session Embedding (TSE), with Item Embedding in Session and Time Decay Factor for a multimodal recommendation. We apply an lower- dimensional vector as item embedding and calculate their factor considering the time decay. The framework performed well in the Content-based Video Relevance Prediction Challenge and we get the first place in this competition.
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Index Terms
- Time-aware Session Embedding for Click-Through-Rate Prediction
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