We know what you want to buy: a demographic-based system for product recommendation on microblogs

By Wayne Xin Zhao, Yanwei Guo, Yulan He, Han Jiang, Yuexin Wu, Xiaoming Li. 2014

161 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wayne Xin Zhao
2. Ji-rong Wen
3. Yupeng Hou
4. Kun Zhou
5. Furu Wei
6. Mengchen Liu
7. Shixia Liu
8. Yaliang Li

In Information Systems

1. Wayne Xin Zhao
2. Ji-rong Wen
3. Yupeng Hou
4. Kun Zhou
5. Bolin Ding
6. Yaliang Li
7. Gao Cong
8. Edward Y. Chang

Citations in the corpus

• Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion (2020, methodology): “ach yielded better performance than several competitive baselines. As future work, we will consider using more kinds of external information to improve the performance of CRS, e.g., user demographics [36]. Besides, we will investigate how to make the utterance more persuasive and explainable for the”
• Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets (2015, methodology): “Users heavily express their needs and desires on Twitter, and tweets have been considered as an important source for mining user intents (Hollerit, Kröll, and Strohmaier 2013; Zhao et al. 2014).”
• An Uncertainty-Aware Approach for Exploratory Microblog Retrieval (2015, background): “Since the crowd in microblogs provides many individual comments/opinions that were not available before, businesses and organizations have begun to leverage microblogs to profile customers, derive brand perception, gauge citizen sentiments, and predict the stock market [23, 34, 41, 53].”
• Towards Universal Sequence Representation Learning for Recommender Systems (2022, background): “To our knowledge, in the literature of recommender systems, there are few studies that can conduct cross-platform recommendation [34].”
• Exploring demographic information in social media for product recommendation (2015, methodology): “We have previously shown that product recommendation can be casted as a learning to rank problem, and multiple additive regression trees (MART) yields better performance than listwise, pairwise or other pointwise algorithms [7].”
• Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks (2018, background): “As several representative kinds of context, temporal information [38], social media information [40, 41] and knowledge information [37] have been utilized in recommendation.”