NDLI: Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction

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Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction

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Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction

Content Provider	ACM Digital Library
Author	Sun, Aixin Hu, Longke Liu, Yong
Abstract	Rating prediction is to predict the preference rating of a user to an item that she has not rated before. Using the business review data from Yelp, in this paper, we study business rating prediction. A business here can be a restaurant, a shopping mall or other kind of businesses. Different from most other types of items that have been studied in various recommender systems (e.g., movie, song, book), a business physically exists at a geographical location, and most businesses have geographical neighbors within walking distance. When a user visits a business, there is a good chance that she walks by its neighbors. Through data analysis, we observe that there exists weak positive correlation between a business's ratings and its neighbors' ratings, regardless of the categories of businesses. Based on this observation, we assume that a user's rating to a business is determined by both the intrinsic characteristics of the business and the extrinsic characteristics of its geographical neighbors. Using the widely adopted latent factor model for rating prediction, in our proposed solution, we use two kinds of latent factors to model a business: one for its intrinsic characteristics and the other for its extrinsic characteristics. The latter encodes the neighborhood influence of this business to its geographical neighbors. In our experiments, we show that by incorporating geographical neighborhood influences, much lower prediction error is achieved than the state-of-the-art models including Biased MF, SVD++, and Social MF. The prediction error is further reduced by incorporating influences from business category and review content.
Starting Page	345
Ending Page	354
Page Count	10
File Format	PDF
ISBN	9781450322577
DOI	10.1145/2600428.2609593
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Rating prediction Yelp Matrix factorization Recommendation
Content Type	Text
Resource Type	Article

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