Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes
In preference learning, the algorithm observes pairwise relative judgments (preference) between items as training data for learning an ordering of all items. This is an important learning problem for applications where absolute feedback is difficult to elicit, but pairwise judgments are readily available (e.g., via implicit feedback ). While it was already shown that active learning can effectively reduce the number of training pairs needed, the most successful existing algorithms cannot generalize over items or queries. Considering web search as an example, they would need to learn a separate relevance score for each document-query pair from scratch. To overcome this inefficiency, we propose a link-based active preference learning method based on Gaussian Processes (GPs) that incorporates dependency information from both feature-vector representations as well as relations. Specifically, to meet the requirement on computational efficiency of active exploration, we introduce a novel incremental update method that scales as well as the non-generalizing models. The proposed algorithm is evaluated on datasets for information retrieval, showing that it learns substantially faster than algorithms that cannot model dependencies.