Popularity bias is the idea that a recommender system will unduly favor
popular artists when recommending artists to users. As such, they may
contribute to a winner-take-all marketplace in which a small number of artists
receive nearly all of the attention, while similarly meritorious artists are
unlikely to be discovered. In this paper, we attempt to measure popularity bias
in three state-of-art recommender system models (e.g., SLIM, Multi-VAE, WRMF)
and on three commercial music streaming services (Spotify, Amazon Music,
YouTube). We find that the most accurate model (SLIM) also has the most
popularity bias while less accurate models have less popularity bias. We also
find no evidence of popularity bias in the commercial recommendations based on
a simulated user experiment.

本文通过对三种最先进的推荐系统模型以及三种商用音乐流媒体服务的研究，尝试测量流行度偏见，发现准确度最高的模型（SLIM）也有最大的流行度偏见，而准确度较低的模型则有较少的流行度偏见。通过模拟用户实验，我们没有发现商业推荐存在流行度偏见的证据。

探究音乐推荐模型和商业流媒体服务中的受欢迎度偏差

Exploring Popularity Bias in Music Recommendation Models and Commercial  Steaming Services

Many approaches to semantic image hashing have been formulated as supervised
learning problems that utilize images and label information to learn the binary
hash codes. However, large-scale labeled image data is expensive to obtain,
thus imposing a restriction on the usage of such algorithms. On the other hand,
unlabelled image data is abundant due to the existence of many Web image
repositories. Such Web images may often come with images tags that contain
useful information, although raw tags, in general, do not readily lead to
semantic labels. Motivated by this scenario, we formulate the problem of
semantic image hashing as a weakly-supervised learning problem. We utilize the
information contained in the user-generated tags associated with the images to
learn the hash codes. More specifically, we extract the word2vec semantic
embeddings of the tags and use the information contained in them for
constraining the learning. Accordingly, we name our model Weakly Supervised
Deep Hashing using Tag Embeddings (WDHT). WDHT is tested for the task of
semantic image retrieval and is compared against several state-of-art models.
Results show that our approach sets a new state-of-art in the area of weekly
supervised image hashing.

利用用户生成的标签信息的词向量语义嵌入来进行约束，我们提出一种名为 WDHT （Weakly Supervised Deep Hashing using Tag Embeddings）的方法，用于语义图像检索，在弱监督图像哈希领域取得新的最佳效果。