Evaluation of Graph Based Recommendation with Other Common Collaborative Filtering Methods

Recommendation systems have come to be of great importance for the online retailer and they are incorporated in to many websites today. However, many different recommendation methods exist and the data collected with which to make recommendations varies from site to site. Thus, in this paper, we evaluate several collaborative filtering based methods, including one graph based method, on samples of data with varying sparseness. To do so, we investigate data from BeerAdvocate.com and draw conclusions about each models efficacy. We believe that this paper may have relevance particularly in the online retail and marketing spaces. 1.0 Introduction The greatest advantage Internet retailers have over traditional brick-and-mortar stores is the ability to sell more obscure items. “On Amazon.com, somewhere between 20 to 40 percent of unit sales fall outside of its top 100,000 ranked products” [1]. Traditional retailers are limited by what can be stocked on the shelves and therefore often resort to merely displaying the most popular items. On the other hand, online stores are not limited by shelf space and therefore can present a seemingly infinite list of products. Although this is a great advantage for the online retailer, deciding which items to display to the customer can be a difficult task. For an online shopper looking at a product that they may have never seen or heard of before, it is essential for the recommendation system to suggest those that the user will truly like in order for repeated business and for that customer to trust future recommendations. This challenge has given rise to the creation of advanced recommendation systems. Thus, in this paper, we evaluate several methods, including one graph-based method, on samples of data with varying degrees of sparseness and numbers of items. Recommendation systems are typically either content-based, using features of the items to recommend those that are similar, or collaborative filtering based where recommendations are made from patterns observed in users' previous interactions, ratings, or purchases of different items [2]. For our study, we chose to evaluate collaborative filtering methods on voting data of various beers from the website BeerAdvocate.com. 2.0 Data Set The data set for our study comes from the BeerAdvocate.com website where users can rate a beer from 1 to 5 on an increasing scale of preference. In total, our dataset consists of 1,586,614 reviews of 66,055 different beers from 33,388 active users. Although there exists data from the site of each user's written review and other features, we only look at votes, or what we also refer to as ratings, of beers by individual users. To evaluate our models, we select only samples of this data set as described in §5.0. 2.1 Votes Table 1 shows a summary of the votes, or ratings, throughout the entire dataset. Noticing that the mean and median vote for a beer lie around 4, we decide to evaluate our recommendation models later using 4 and above to indicate that the user preferred the beer. Although this assumption is seems somewhat arbitrary, it allows us to think of the beers as either good or bad for a particular user and gives us a way in which to evaluate a recommended beer for that user. Min. 1st Qu. Median Mean 3rd Qu. Max. 1.000 3.500 4.000 3.816 4.500 5.000 Table 1. Summary of votes throughout entire data set. 2.0 Methods In this paper, we evaluate a graphical recommendation model against three other common collaborative filtering methods. As a baseline, we also consider mean user rating for each beer. Across all of these models, we use the collaborative filtering idea of taking past user ratings, or votes, to predict and recommend items that the user would prefer. In this section, we describe each of these models. 3.1 Graph-Based Model