Evaluating Vector-Space and Probabilistic Models for Query to Ad Matching
نویسندگان
چکیده
In this work, we evaluate variants of several information retrieval models from the classic BM25 model to Language Modeling approaches for retrieving relevant textual advertisements for Sponsored Search. Within the language modeling framework, we explore implicit query expansion via translation tables derived from multiple sources and propose a novel method for directly estimating the probability that an advertisement is clicked for a given query. We also investigate explicit query expansion using regular web search results for sponsored search using the vector space framework. We find that web-based expansions result in significant improvement in Mean Average Precision.
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