To combat the ease of online shopping in pajamas, offline mall owners focus increasingly on driving satisfaction and improving retention by identifying customers’ preferences. However, most of these studies are based on customers’ offline consuming history only. Benefiting from the internet, we can also get customers’ online behaviors, such as the search logs, web browsing logs, online shopping logs, and so on. Might these seemingly irrelevant information from two different modalities (i.e. online and offline) be somehow interrelated? How can we make use of the online behaviors and offline actions jointly to promote recommendation for offline retailing?
In this study, we formulate this task as a cross-modality recommendation problem, and present its solution via a proposed probabilistic graphical model, called Online-to-Offline Topic Modeling (O2OTM). Specifically, this method explicitly models the relationships between online and offline topics so that the likelihood of both online and offline behaviors is maximized. Then, the recommendation is made only based on the pairs of online and offline topics, denoted by (t, l), with high values of lift, such that the existence of the online topic t greatly increases the response on the corresponding offline topic l compared with the average response for the population without the online topic t. Furthermore, we evaluate this solution in both live and retrospect experiments. The real-world deployment of this model for the anniversary promotion campaign of a famous shopping mall in Beijing shows that our approach increases the occurred customer purchases per promotion message by 29.75% compared with the baseline. Also, our model finds some interesting interpretable relationships between the online search topics and offline brand topics.
Filed under: Recommender Systems