A two-stage personalized recommendation based on multi-objective teaching-learning-based optimization with decomposition

Due to its successful application in information filtering and knowledge retrieval systems in the era of big data, personalized recommender system plays a significant role in meeting personalized demands of people from big data and has become a hot research hotspot. Traditional recommender systems only guarantee the recommendation accuracy and the recommendation diversity will be lost. In this paper, we propose a two-stage personalized recommendation (TSPR) algorithm based on an improved collaborative filtering (ICF) and multi-objective teaching-learning-based optimization (MOTLBO/D) with decomposition. Firstly, ICF made use of each user' preference and social neighborhood information to obtain a candidate recommendation list for each target user. In the proposed ICF method, the jump relationship between users and their unrated items was mined to describe the characteristic behavior information of users more fully. Moreover, MOTLBO/D was adopted to simultaneously optimize accuracy and diversity of recommendation list of items for each target user. In the proposed MOTLBO/D algorithm, the learner representation strategy is designed based on attributes of the recommendation problem, and the learners are updated by an improved TLBO procedure. Finally, the simulation results on two Movielens datasets show that TSPR is effective and efficient in personalized recommender systems. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a two-stage personalized recommendation algorithm based on improved collaborative filtering and multi-objective optimization is proposed, aiming to enhance the accuracy and diversity of recommendations. Experimental results demonstrate the effectiveness and efficiency of the algorithm in personalized recommender systems.