Recommendation diversification using a weighted similarity measure in user based collaborative filtering

In real world e-commerce applications, users express partially their preference in aim of getting back automatically valuable recommendations. The importance of that process has known an increasing development due to the high number of available products under the trade which influences negatively user choice making. The collaborative filtering approach consists of mining users/items data to model the preferences in the form of common profiles. Since ratings prediction is computed by aggregating neighbors ratings, the predictions could be calculated by performing a based similarity neighborhood selection. In this paper, we propose a weighted similarity measure as an alternative to the conventional similarity metrics used in the collaborative filtering. The proposed weights have improved novelty, diversity metrics as well as recommendation accuracy metrics. We have compared our proposed model against memory user based collaborative filtering.