UCLA

Yelp provides a valuable platform to share massive restaurant information, but it is difficult for its users to distinguish a relevant one among others. People are overwhelmed by multifarious information and unable to efficaciously glean germane information. Thus, it’s necessary to build a recommendation model which can filter and prioritize information, efficiently recommending appropriate restaurants to Yelp’s users, so the users can make correct decisions. Meanwhile, it can also help businesses to target their potential customers more accurately by sending similar-preference recommendations. This research explores different preferences and topics from Yelp restaurant reviews to understand characteristics of each user and restaurant, and then applies four practical algorithms to provide the most precise and personalized restaurant recommendations for the users.