Online advertising represents a growing part of the revenues of major Internet service providers such as Google and Yahoo. A commonly used strategy is to place advertisements (ads) on the search result pages according to the users' submitted queries. Relevant ads are likely to be clicked by a user and to increase the revenues of both advertisers and publishers. However, bid phrases defined by ad-owners are usually contained in limited number of ads. Directly matching user queries with bid phrases often results in finding few appropriate ads. To address this shortcoming, query expansion is often used to increase the chances to match the ads. Nevertheless, query expansion on top of the traditional inverted index faces efficiency issues such as high time complexity and heavy I/O costs. Moreover, precision cannot always be improved, sometimes even hurt due to the involvement of additional noise. In this paper, we propose an efficient ad search solution relying on a block-based index able to tackle the issues associated with query expansion. Our index structure places clusters of similar bid phrases in corresponding blocks with their associated ads. It reduces the number of merge operations significantly during query expansion and allows sequential scans rather than random accesses, saving I/O costs. We adopt flexible block sizes according to the clustering results of bid phrases to further optimize the index structure for efficient ad search. The pre-computation of such clusters is achieved through an agglomerative iterative clustering algorithm. Finally, we adapt the spreading activation mechanism to return the top-k relevant ads, improving search precision. The experimental results of our prototype, AdSearch, show that we can indeed return a larger number of relevant ads without sacrificing execution speed.
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