TADI: Topic-aware Attention and Powerful Dual-encoder Interaction for Recall in News Recommendation
Submission Type: Regular Long Paper
Submission Track: Information Retrieval and Text Mining
Submission Track 2: NLP Applications
Keywords: news recommendation, recommendation
TL;DR: TADI: Topic-aware Attention and Powerful Dual-encoder Interaction for Recall in News Recommendation
Abstract: News recommendation is one of the widest commercialization in natural language processing research area, which aims to recommend news according to user interests. New recall plays an important role in news recommendation. It is to recall candidates from a very large news database. Recent researches of news recall mostly adopt dual-encoder architecture as it provides a much faster recall scheme, and they encode each word equally. However, these works remain two challenges: irrelevant word distraction and weak dual-encoder interaction. Therefore, we propose a model Topic-aware Attention and powerful Dual-encoder Interaction for Recall in news recommendation (TADI). To avoid irrelevant word distraction, TADI designs a Topic-aware Attention (TA) which weights words according to news topics. To enhance dual-encoder interaction, TADI provides a cheap yet powerful interaction module, namely Dual-encoder Interaction (DI). DI helps dual encoders interact powerfully based on two aux targets. After performance comparisons between TADI and state-of-the-arts in a series of experiments, we verify the effectiveness of TADI.
Submission Number: 3041
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