Top-N Recommendation with High-Dimensional Side Information via Locality Preserving Projection

In this paper,we leverage high-dimensional side information to enhance top-N recommendations. To reduce the impact of the curse of high dimensionality, we incorporate a dimensionality reduction method, Locality Preserving Projection (LPP), into the recommendation model. A joint learning model is proposed to achieve the task of dimensionality reduction and recommendation simultaneously and iteratively. Specifically, item similarities generated by the recommendation model are used as the weights of the adjacency graph for LPP while the projections are used to bias the learning of item similarity. Employing LPP for recommendation not only preserves locality but also improves item similarity. Our experimental results illustrate that the proposed method is superiorover state-of-The-Art methods.