Shopping for Products You Don’t Know You Need

Recommendation engines today suggest one product to another, e.g., an accessory to a product. However, intent to buy often precedes a user’s appearance in a commerce vertical: someone interested in buying a skateboard may have earlier searched for “varial heelflip”, a trick performed on a skateboard. This paper considers how a search engine can provide early warning of commercial intent. The naive algorithm of counting how often an interest precedes a commercial query is not sufficient due to the number of related ways of expressing an interest. Thus, methods are needed for finding sets of queries where all pairs are related, what we call a “query community”, and this is the technical contribution of the paper. We describe a random model by which we obtain relationships between search queries and then prove general conditions under which we can reconstruct query communities. We propose two complementary approaches for inferring recommendations that utilize query communities in order to magnify the recommendation signal beyond what an individual query can provide. An extensive series of experiments on real search logs shows that the query communities found by our algorithm are more interesting and unexpected than a baseline of clustering the query-click graph. Also, whereas existing query suggestion algorithms are not designed for making commercial recommendations, we show that our algorithms do succeed in forecasting commercial intent. Query communities increase both the quantity and quality of recommendations.