论文标题
Modurec：具有功能和时间调制的推荐系统
Modurec: Recommender Systems with Feature and Time Modulation
论文作者
论文摘要
推荐系统的当前ART算法状态主要基于协作过滤，该算法利用用户评分来发现数据中的潜在因素。不幸的是，这些算法无法有效利用其他功能，这可以帮助解决两个有充分识别的协作过滤的问题：冷启动（不足的数据可用于新用户或产品）和概念转移（评级的分配随时间变化）。为了解决这些问题，我们提出了Modurec：一种基于自动编码器的方法，该方法使用功能调制机制结合了所有可用信息，该机制在几个领域都证明了其有效性。虽然时间信息有助于减轻概念转移的影响，但是当很少的数据可用时，用户和项目功能的组合会提高预测性能。我们在Movielens数据集上显示，与基于标准的自动编码器的方法和其他协作过滤方法相比，这些修改会产生最新的设置。
Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which can help solve two well identified problems of collaborative filtering: cold start (not enough data is available for new users or products) and concept shift (the distribution of ratings changes over time). To address these problems, we propose Modurec: an autoencoder-based method that combines all available information using the feature-wise modulation mechanism, which has demonstrated its effectiveness in several fields. While time information helps mitigate the effects of concept shift, the combination of user and item features improve prediction performance when little data is available. We show on Movielens datasets that these modifications produce state-of-the-art results in most evaluated settings compared with standard autoencoder-based methods and other collaborative filtering approaches.
