A Fuzzy-Based Personalized Recommender System for Local Businesses

Abstract

On-line reviewing systems have become prevalent in our society. User-provided reviews of local businesses have provided rich information in terms of users' preferences regarding businesses and their interactions in reviewing systems; however, little is known about how the reviewing behaviors of users can benefit businesses in terms of suggesting potential collaboration opportunities. In the current study, we aim to build a recommendation system for businesses to provide suggestions for business collaboration. Based on historical data from Yelp that shows two businesses being reviewed by the same users within a same season, we were able to identify businesses that might attract the same customers in the future, and hence provide them with a collaboration suggestion. Our results suggest that the evidence-two businesses sharing reviews from same users-can provide recommendations for businesses to pursue future collaborative marketing opportunities.

Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

“…Tsai provides recommendations for business collaboration and represents information through fuzzy vectors (Tsai, 2016).…”

Fuzzy Tools in Recommender Systems: A Survey

“…Here it is worthy to note the development of tag-based user profiling methods for improving recommendations [9], where user profiles are built through a folksonomy-based approach that evaluates items according to the membership degrees to various attribute values, which are then used to compute the fuzzy user profile. Additionally, in the last few years further works on the use of fuzzy tools for modelling specific items' features in CBRS have been developed [2,5,14,69,96,99,104,126].…”

Leveraging Interfaces to Improve Recommendation Diversity

“…As a result, an e cient system is required to deliver the relevant items with a high degree of accuracy, based on user preferences or behavior [24]. Many scholars have criticized highly accurate recommender algorithms that provide the user with a narrow set of choices or Late-Breaking Results, Demonstration and Theory, Opinion & Reflection Paper UMAP'17, July 9-12, 2017, Bratislava, Slovakia limited information exposure [5], which is a well-known overspeci cation problem in the domain of recommender systems [1,33].…”