| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 106 === It is reported that sales by e-commerce companies were greater than usual during shopping holidays and festivals. However, based on users’ browsing logs, we found that not all users visit e-commerce websites more often than they normally do during holidays. Therefore, the increase in sales may come from the purchase behaviors of a small number of users. If the e-commerce companies can systematically assess and analyze user behaviors, they might be able to apply customized marketing method to maximize the effectiveness of their sales strategies.
This study proposes a matrix factorization based multi-objective method, which effectively uses the latent variables that represent possible interactions among multiple targets. Compared with establishing separate models for each target, this method can effectively reduce the parameters that the model needs to learn, and can, therefore, achieve an effective training outcome even when training samples are limited. We use this method to simultaneously predict users’ behaviors on different types of web pages during shopping holidays and festivals. The results show that this method can outperform the single target training model most of the time.
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