Recommendation System Based on Complete Personalization

TL;DR: In this work, similarity based targeting has been combined with baseline approach and latent factor models and has been treated with adaptive regularization allowing complete personalization with respect to both users and items.

Abstract: Current recommender systems are very inefficient. There are many metrics that are used to measure the effectiveness of recommender systems. These metrics often include conversion rate and click through rate. Recently, these rates are in low single digit (less than 10%). In other words, for more than 90% of times, the model that the targeting system is based on, produces noise. The belief in this work is that the main problem leading to getting such unsatisfactory outcomes is the modeling problem. Much of the modeling problem could be represented and exemplified in treating users and items as member of clusters(segments). In this work, we consider full personalization of recommendation systems. We aim at personalization of users and contents simultaneously. Recommendations using baseline approach are inaccurate and targeting based on similarity-based recommendation (collaborative filtering) suffer from many disadvantages such as the neglect of interactive correlation. In this work, similarity based targeting has been combined with baseline approach and latent factor models and has been treated with adaptive regularization allowing complete personalization with respect to both users and items.