User Model-Based Intent-Aware Metrics for Multilingual Search Evaluation
This work addresses the problem of evaluating multilingual search for users and developers, but it is incremental as it builds on existing intent-aware and diversification approaches.
The paper tackles the lack of appropriate offline metrics for evaluating multilingual web search quality by proposing a novel intent-aware user behavior model, which overcomes limitations of existing models and produces metrics that better correlate with online user satisfaction metrics.
Despite the growing importance of multilingual aspect of web search, no appropriate offline metrics to evaluate its quality are proposed so far. At the same time, personal language preferences can be regarded as intents of a query. This approach translates the multilingual search problem into a particular task of search diversification. Furthermore, the standard intent-aware approach could be adopted to build a diversified metric for multilingual search on the basis of a classical IR metric such as ERR. The intent-aware approach estimates user satisfaction under a user behavior model. We show however that the underlying user behavior models is not realistic in the multilingual case, and the produced intent-aware metric do not appropriately estimate the user satisfaction. We develop a novel approach to build intent-aware user behavior models, which overcome these limitations and convert to quality metrics that better correlate with standard online metrics of user satisfaction.