TPRM: A Topic-based Personalized Ranking Model for Web Search
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Abstract (arXiv)
Ranking models have achieved promising results, but it remains challenging to design personalized ranking systems to leverage user profiles and semantic representations between queries and documents. In this paper, we propose a topic-based personalized ranking model (TPRM) that integrates user topical profile with pretrained contextualized term representations to tailor the general document ranking list. Experiments on the real-world dataset demonstrate that TPRM outperforms state-of-the-art ad-hoc ranking models and personalized ranking models significantly.
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References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2017A Deep Relevance Matching Model for Ad-hoc Retrievalcited by 126
- 2017End-to-End Neural Ad-hoc Ranking with Kernel Poolingcited by 85
- 2019A Deep Look into Neural Ranking Models for Information Retrievalcited by 79
- 2019CEDR: Contextualized Embeddings for Document Rankingcited by 77
- 2019Understanding the Behaviors of BERT in Rankingcited by 49
- 2019Context Attentive Document Ranking and Query Suggestioncited by 15
- 2019Personalizing Search Results Using Hierarchical RNN with Query-aware Attentioncited by 7
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