Abstract: Aiming at the problem of low prediction accuracy of the traditional Slope One recommendation algorithm on sparse data set, this paper proposes a weighted Slope One algorithm based on graph embedding. This algorithm first establishes a correlation graph with time-aware user similarity as the edges’ weight, and obtains user eigen vectors based on the graph embedding of this graph. It then produces intra-class weighted Slope One recommendations using Canopy clustering. Additionally, to optimize the performance of the algorithm, we make an implementation based on the Spark computing framework. Experimental results demonstrate that, compared with the traditional weighted Slope One algorithm, the proposed algorithm has better recommendation effect and score prediction accuracy on both sparse data sets, explicit and implicit scoring data sets.

Key words: graph embedding, time factor, Canopy clustering, weighted Slope One, Spark