A Personalized Word of Mouth Recommender Model

Word of mouth (WOM) has a powerful effect on consumer behavior. Manually collecting WOM is very time-consuming in the era of the Internet. An automatic WOM recommender model is useful for both marketers and consumers. There are many different product features and thus many consumer choices. Each individual consumer has different preferences and these preferences may be changed deliberately or unwittingly. However, most existing WOM recommender models do not adapt to user preferences. This study proposes a conceptual WOM recommender model, which contains WOM collecting, document processing, recommending and user preference processing phases. More specifically, the self-organizing map (SOM) is used to store and abstract user preferences. This proposed WOM model makes recommendations to consumers or users according to their adaptive preferences.