カンファレンス (国際) ModelRecycling: Predicting user’s interest with connected predictive models
Yoshiki Matsune (Ritsumeikan University), Kota Tsubouchi, Nobuhiko Nishio (Ritsumeikan University)
2020 IEEE International Conference on Big Data (IEEE BigData 2020)
Many different services acquire feedback from users, and many predictive models of users interests have been made for recommending items by using data acquired from different services. Here, although we would be able to get deep insights about a user’s interest by connecting predictive models, interactions among services and potential synergies of their predictive models have been neglected. Moreover, in the era of big data, it should be relatively easy to develop a recommendation system that works across services by connecting predictive models and using existing technologies. However, the effect and conditions under which such the recommendation system works effectively have not been analyzed in detail. We have developed ModelRecycling FrameWork (MRFW) as an implementation of our idea of connecting predictive models to make recommendations across services. We experimentally evalu- ated the performance of MRFW by using an online questionnaire survey. The results show that MRFW significantly outperforms predictive models that make recommendations without consid- ering the interaction between services or synergies of predictive models. We also clarified the conditions under which MRFW works effectively by comparing its performance with that of the individual models on which it is based in order to make use of the results of analysis for designing the method that connects predictive models.