本文类似于 comment + rating 的文章，区别在于本文用用户的tweets文本代替了comment等其他关于用户的描述性问题。
所以：
（1）Tweets 指代用户的文本数据（其领域可能不同于评分领域，如微博发布的数据和在豆瓣上给电影打的分数）
（2）文中off-topic 和on-topic 的概念是相对的，文中定义on-topic的概念为，tweets中出现了相关item的名字，如评价了某部电影的好坏
（3）在解决了以上问题后，模型部分就显得比较简单，基本上是MF加SDAE，中间矩阵做一些变化，共享等，如上图2所示。
值得学习的：作者如何帮简单的学习过程，用图2表达出来，看起来还不错。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Your Tweets Reveal What You Like: Introducing Cross-media Content Information into Multi-domain Recommendation | |||||||||||
| 文献作者 | Weizhi Ma | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| content; cnn; rating predition; combination; SDAE; 简单学习问题绘图 | |||||||||||
| 摘要描述 | |||||||||||
| Cold start is a challenging problem in recommender systems. Many previous studies attempt to utilize extra information from other platforms to alleviate the problem. Most of the leveraged information is on-topic, directly related to users’ preferences in the target domain. Thought to be unrelated, users’ off-topic content information (such as user tweets) is usually omitted. However, the off-topic content information also helps to indicate the similarity of users on their tastes, interests, and opinions, which matches the underlying assumption of Collaborative Filtering (CF) algorithms. In this paper, we propose a framework to capture the features from user’s off-topic content information in social media and introduce them into Matrix Factorization (MF) based algorithms. The framework is easy to understand and flexible in different embedding approaches and MF based algorithms. To the best of our knowledge, there is no previous study in which user’s off-topic content in other platforms is taken into consideration. By capturing the cross-platform content including both on-topic and off-topic information, multiple algorithms with several embedding learning approaches have achieved significant improvements in rating prediction on three datasets. Especially in cold start scenarios, we observe greater enhancement. The results confirm our suggestion that off-topic cross-media information also contributes to the recommendation. | |||||||||||
