Abstract: A recommendation system implements a linkage (connectivity) score learning algorithm for user-item interaction bipartite graphs that is combined with a lightweight iterative degree update process in the bipartite graph where the degrees used in the scoring formula are updated several times to exploit local graph structures without any node (user/item) modeling. In the linkage score learning algorithm, for user u1 and item i2, the predicted linkage score between them is the sum over all sub-scores of each 3-step linkage path between u1 and i2. The linkage score learning algorithm pre-defines 6 learnable candidate parameter values, selects the best combination of parameters, and predicts a set of linkage scores that can be used for recommendation systems. The linkage score learning algorithm addresses the problem of link prediction by predicting new links in a graph that do not already exist in training data.

Abstract: A recommendation system implements a linkage (connectivity) score learning algorithm for user-item interaction bipartite graphs that is combined with a lightweight iterative degree update process in the bipartite graph where the degrees used in the scoring formula are updated several times to exploit local graph structures without any node (user/item) modeling. In the linkage score learning algorithm, for user u1 and item i2, the predicted linkage score between them is the sum over all sub-scores of each 3-step linkage path between u1 and i2. The linkage score learning algorithm pre-defines 6 learnable candidate parameter values, selects the best combination of parameters, and predicts a set of linkage scores that can be used for recommendation systems. The linkage score learning algorithm addresses the problem of link prediction by predicting new links in a graph that do not already exist in training data.