LibRec 每周精选：近期推荐系统论文及进展

本周的精选内容如下：

• 【博文】推荐系统对市场营销的启示：https://martechtoday.com/roi-recommendation-engines-marketing-205787?utm_content=buffer46e22&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

• Improving with use (positive feedback loop)

• Improving cart value (profit)

• Improving engagement and delight (retention)

• 【竞赛】RecSys 2018 Challenge: Music Recommendation,
  https://recsys-challenge.spotify.com/details, 但是似乎只开放给大学研究人员？

• 【论文】Review-based Recommendation
  

• Multi-Pointer Co-Attention Networks for Recommendation, Yi Tay et al., arXiv:1801.09251v1

• TransRev: Modeling Reviews as Translations from Users to Items, Alberto Garcia-Duran et al., arXiv:1801.10095v1

• 【论文】Image/Text-based Recommendation

• Visually Explainable Recommendation, Xu Chen et al., arXiv:1801.10288v1

• DKN: Deep Knowledge-Aware Network for News Recommendation, Hongwei Wang et al., arXiv:1801.08284v1

• 【论文】其它推荐论文

• Offline A/B testing for Recommender Systems, Alexandre Gilotte et al., arXiv:1801.07030v1

• Reinforcement Learning based Recommender System using Biclustering Technique, Sungwoon Choi et al., arXiv:1801.05532v1

• 【Slides】总结了近期深度学习在推荐系统中的应用：Deep Learning for recommendations: a first try.  https://www.slideshare.net/moustaki/deep-learning-for-recommender-systems-86752234?utm_content=buffer8e818&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

• 【提问】如何把图片转成数字向量输入到神经网络中呢？https://www.reddit.com/r/MachineLearning/comments/7u44yn/d_how_do_you_even_turn_an_image_into_a_vector_of/

• 【博客】协同过滤与奇异值分解：https://hackernoon.com/introduction-to-recommender-system-part-1-collaborative-filtering-singular-value-decomposition-44c9659c5e75

• 【工具】ScaR: Scalable Recommendation-as-a-service, http://scar.know-center.tugraz.at/index.html

注：图片来源于网络。