Category-Driven Approach for Local Related Business Recommendations
Venue
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, ACM, New York, NY (2015), pp. 73-82
Publication Year
2015
Authors
Yonathan Perez, Michael Schueppert, Matthew Lawlor, Shaunak Kishore
BibTeX
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.
