We present a novel component of a hybrid recommender system at LinkedIn, where item features are augmented by a virtual profile based on observed user-item interactions. A virtual profile is generated by representing an item in the user feature space and leveraging the overrepresented user features from users who interacted with the item. It is a way to think about Collaborative Filtering with content features. The core principle is that if the feature occurs with high probability for the users who interacted with an item (henceforth termed as relevant users) versus those who did not (henceforth termed as non-relevant users), then that feature is a good candidate to be included in the virtual profile of the item in question. However, this scheme suffers from the data imbalance problem because observed relevant users are usually an extremely small minority group compared to the whole user base. Feature selection in this skewed setting is prone to noise from the overwhelming non-relevant examples that belong to the majority group. To alleviate the problem, we propose a method to select the most relevant non-relevant examples from the majority group by segmenting users on certain intelligently selected feature dimensions. The resulting virtual profile from the method is called the segmented virtual profile. Empirical evaluation on a real-world large scale recommender system at LinkedIn shows that our strategies for segmentation yield significantly better results.

Some supervised-learning algorithms can make effective use of domain knowledge in addition to the input-output pairs commonly used in machine learning. However, formulating this additional information often requires an in-depth understanding of the specific knowledge representation used by a given learning algorithm. The requirement to use a formal knowledge-representation language means that most domain experts will not be able to articulate their expertise, even when a learning algorithm is capable of exploiting such valuable information. We investigate a method to ease this knowledge acquisition through the use of a graphical, human-computer interface. Our interface allows users to easily provide advice about specific examples, rather than requiring them to provide general rules; we leave the task of properly generalizing such advice to the learning algorithms. We demonstrate the effectiveness of our approach using the Wargus real-time strategy game, comparing learning with no advice to learning with concrete advice provided through our interface, as well as comparing to using generalized advice written by an AI expert. Our results show that our approach of combining a GUI-based advice language with an advice-taking learning algorithm is an effective way to capture domain knowledge.

We present a framework for knowledge transfer from one reinforcement learning task to a related task through advice-taking mechanisms. We discuss the importance of transfer in complex domains such as RoboCup soccer, and show how to use automatically generated advice to perform transfer.