Building Recommendation Systems Using the Algorithms KNN and SVD

Abstract

<p>Recommendation systems are used successfully to provide items (example:<br />movies, music, books, news, images) tailored to user preferences.<br />Among the approaches proposed, we use the collaborative filtering approach<br />of finding the information that satisfies the user by using the<br />reviews of other users. These ratings are stored in matrices that their<br />sizes increase exponentially to predict whether an item is interesting<br />or not. The problem is that these systems overlook that an assessment<br />may have been influenced by other factors which we call the cold start<br />factor. Our objective is to apply a hybrid approach of recommendation<br />systems to improve the quality of the recommendation. The advantage<br />of this approach is the fact that it does not require a new algorithm<br />for calculating the predictions. We we are going to apply the two Kclosest<br />neighbor algorithms and the matrix factorization algorithm of<br />collaborative filtering which are based on the method of (singular value<br />decomposition).</p>

Research on Personalized Recommendation Algorithms Based on User Profile

“…Yet, they still require more extensive user profile analysis. For instance, Badr et al [6] developed a recommendation system using K-nearest neighbors and singular value decomposition, but it suffered from low efficiency due to the inability to capture all item features. Collaborative filtering algorithms struggle to extract deep user demands and interest preferences from deep-level interaction data, resulting in a significant bias between recommended content and user needs, yielding suboptimal results [7].…”