A Hybrid Method Based on HITS for Literature Recommendation

Abstract

In this paper we propose a hybrid method of literature recommendation in the academic community. First, we refer the objective recommendation based on HITS algorithm by constructing a directed graph according to the literature citation relation and then select the articles considering the authority and hub score of each article synthetically and add them to the recommendation list. This can narrow the recommendation scope and give a more authoritive recommendation. Second, the subjective recommendation is based on collaborative filtering by comparing the ratings of other similar users for the objects in recommendation list. The difference is we discover the similar user by clustering them. And the experiment shows the method can provide better recommendation results and is timesaving.

A novel hybrid publication recommendation system using compound information

“…So far, plenty of work has been done in paper recommendation, which can be roughly divided into three branches, i.e., (1) citation network-based methods [12,13,27], (2) content-based methods [3,14,26] (3) co-author networkbased methods [21,23,29] and hybrid methods [24,37,40].…”