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.

Practical Diversified Recommendations on YouTube with Determinantal Point Processes

“…For instance, Dang et al [10] proposes to return a result list that has per-topic coverage that is proportional to that topic's popularity. As another example, Perez et al [32] uses categories of businesses to ensure recommendation results for a local business recommendation problem has sufficient topical coverage. In [23], Kwon and Adomavicius argue that users essentially want a multi-criteria rating system, in which they can specify which aspects of the recommendation they want.…”

“…Concisely, one way of achieving diversity is avoiding redundancy, which is particularly important for recommender systems [5,30,32,43,45]. For instance, in their seminal work in 2005, Ziegler et al [45] minimize the similarity between recommended items using a greedy algorithm with a taxonomy of books.…”

“…The score of an item under consideration is proportional to its relevance minus a penalty term that measures its similarity to previously selected items. Other explicit notions of redundancy are studied in [32], which uses a decay function on pairwise similarities. More recently, Nassif et al [30] describe an approach using submodular optimization to diversify music recommendation.…”

“…In order to mitigate the redundancy problem, at first, we introduced heuristics in the spirit of [32,45] to the policy layer, such as a requirement that an individual uploader can contribute no more than n items to any user's feed. While this rule is somewhat effective, our experience is that it interacts quite poorly with the underlying recommendation system.…”

CCSMR: A Combinatorial Category Space-Based Model for Recommendation

“…In addition, each item may belong to multiple categories, so it is difficult to find a representative or the most import one without clear and sufficient information. For these reasons, the item category attribute has been widely used in various types of recommendation algorithms, such as user/item-based collaborative filtering (CF) models [11], [12], matrix factorization (MF) models [13], [14] and random-walk based recommendation model [15].…”