AI

Category-Driven Approach for Local Related Business Recommendations

Abstract

When users search online for a business, the search engine may present them with a list of related business recommendations. We address the problem of constructing a useful and diverse list of such recommendations that would include an optimal combination of substitutes and complements. Substitutes are similar potential alternatives to the searched business, whereas complements are local businesses that can offer a more comprehensive and better rounded experience for a user visiting the searched locality. In our problem setting, each business belongs to a category in an ontology of business categories. Two businesses are defined as substitutes of one another if they belong to the same category, and as complements if they are otherwise relevant to each other. We empirically demonstrate that the related business recommendation lists generated by Google’s search engine are too homogeneous, and overemphasize substitutes. We then use various data sources such as crowdsourcing, mobile maps directions queries, and the existing Google’s related business graph to mine association rules to determine to which extent do categories complement each other, and establish relevance between businesses, using both category-level and individual business-level information. We provide an algorithmic approach that incorporates these signals to produce a list of recommended businesses that balances pairwise business relevance with overall diversity of the list. Finally, we use human raters to evaluate our system, and show that it significantly improves on the current Google system in usefulness of the generated recommendation lists.