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Researcher profile

Yanyan Lan

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback

    2022 · arXiv (Cornell University)

    Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents. Existing PRF …

  2. Learning Hierarchical Representation Model for NextBasket Recommendation

    2015

    Next basket recommendation is a crucial task in market basket analysis. Given a user's purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items …

  3. A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

    2016 · Proceedings of the AAAI Conference on Artificial Intelligence

    Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot …

  4. Modeling Document Novelty with Neural Tensor Network for Search Result Diversification

    2016

    Search result diversification has attracted considerable attention as a means to tackle the ambiguous or multi-faceted information needs of users. One of the key problems in search result diversification is novelty, that is, how to …

  5. Reinforcement Learning to Rank with Markov Decision Process

    2017

    One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures such as normalized discounted cumulative gain~(ND CG). Existing methods usually …

  6. A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

    2015 · arXiv (Cornell University)

    Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot …