A case study of recommendation algorithms

Recommender systems are very popular in online service providers. Among all sorts of recommender systems, the top-N recommendation for online shopping systems has drawn increasing attention from researchers. Most existing papers about recommendation algorithms use public datasets as their experiment data, e.g. Netflix, Movie lens. These datasets, containing the users' ratings of movies, have been carefully tweaked. Thus, these datasets are very suitable for algorithm study. However, in real applications, such as online shopping websites, whose data may not be tweaked or without any explicit rating information in it but is still used for recommender systems. Fortunately, we are invited by an American retargeting company, to study the effects of recommendation algorithms on their datasets and try to find a good strategy for selecting algorithms with respect to particular websites. In this paper, several typical recommendation algorithms - popularity based model, item similarity-based model, SVD model, and bipartite graph model are studied. The filtering step of the popularity based model is also applied to other models for further comparison. Experiments are performed with these methods on four different browsing history datasets from this retargeting company to help us in obtaining advantages and disadvantages of each approach. Experimental results show that there is no "perfect" or dominating model for all datasets. Nevertheless, we have found a somewhat "perfect" strategy in our selection.