Recommendations and User-Profiling from Implicit Feedbacks

Recommendations and Feedbacks The vast majority of B2C services are quickly discovering the strategic importance of solid recommendation engines to improve the conversion rates and an establish a stronger fidelity with the customers. The most common strategies are based [3] on the segmentation of users according to their personal features (age range, gender, interests, social interactions, and so on) or to the ratings they gave to specific items. The latter approach normally relies on explicit feedbacks (e.g. a rating from 0 to 10) which summarize the overall experience. Unfortunately, there are drawbacks to both cases. Personal data are becoming harder to retrieve and the latest regulations (i.e. GDPR) allow the user to interact with a service without the collection of data. Moreover, a reliable personal profile must be built using many attributes that are often hidden and can only be inferred using predictive models. Conversely, implicit feedbacks are easy to […]