Inferring Latent Triggers of Purchases with Consideration of Social Effects and Media Advertisements
This paper proposes a method for inferring from single source data the factors that trigger purchases. Here, single source data are the histories of item purchases and media advertisement views for each individual. We assume a sequence of purchase events to be a stochastic process incorporating the following three factors: (a) user preference, (b) social effects received from other users, and (c) media advertising effects. As our user purchase model incorporates the latent relationships between users and advertisers, it can infer the latent triggers of purchases. Experiments on real single source data show that our model can (a) achieve high prediction accuracy for purchases, (b) discover the key information, i.e. popular items, influential users, and influential advertisers, (c) estimate the relative impact of the three factors on purchases, and (d) find user segments according to the estimated factors.
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