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Smart Exploration Methods For Mitigating Item Cold-Start Problem In Collaborative Filtering Recommendation Systems

机译:协作过滤推荐系统中缓解项目冷启动问题的智能探索方法

摘要

Systems and methods for building a latent item vector and item bias for a new item in a collaborative filtering system are disclosed. The method includes dividing incoming users into intervals with each interval having a learning phase and a selection phase. The learning phase scores each incoming user according to a best estimate latent vector and bias and saves the highest score. In the selection each incoming user is scored and a user exceeding the highest score is selected. The best estimate latent vector and bias is then updated based on the user's vector and bias, and the user's interaction with the item. The updated best estimate latent vector is then used in further intervals for learning and selecting users.
机译:公开了用于在协同过滤系统中为新项目建立潜在项目向量和项目偏差的系统和方法。该方法包括将进入的用户划分为间隔,每个间隔具有学习阶段和选择阶段。学习阶段根据最佳估计潜在向量和偏差对每个传入用户进行评分,并保存最高评分。在选择中,对每个进入的用户进行评分,并选择一个得分最高的用户。然后,基于用户的矢量和偏差以及用户与商品的交互来更新最佳估计潜在矢量和偏差。然后,在进一步的间隔中使用更新的最佳估计潜在矢量来学习和选择用户。

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