Recommendation based on user experiences
نویسنده
چکیده
Latent factor model is one common technique to build recommendation systems. Standard latent factor model however does not take into account the order in which each individual user makes the ratings. Modeling the shift in user behaviour over time will not only allow making better recommendations to users, but also discover their hidden categories, “level of experiences” or “progression stages”. In this project, we apply the standard latent factor technique to a movie review data set and then apply the new technique to model user experience on this data set. We find that the improvement in prediction depends on the time span of the dataset. But more importantly, we can use the model to draw interesting insight from the discovered “user experiences” guiding user to the next level of experiences.
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