Multiobjective recommendation optimization via utilizing distributed parallel algorithm.

With the development of information technologies, various big data problems are emerging. The recommendation problem can be seen as a big data problem. Traditionally, for a recommender system (RS), only the recommendation precision is considered. However, reflecting another aspect of RS, recommendation diversity is also important. In this paper, we adopt a multiobjective recommendation model to simultaneously consider recommendation precision and diversity, specifically, precision, novelty and coverage of recommendation are involved. To tackle the multiobjective recommendation optimization problem (MROP), based on distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm (DPCCMOEA), a novel multiobjective evolutionary algorithm (MOEA), DPCCMOEA for RSs (DPCCM0EA-RecSys) is proposed. On the basis of cooperative coevolution (CC) framework, all users are allocated to several groups and are optimized simultaneously. Optimization strategy specific for RSs is put forward, the individual integration approach is explored and different grouping techniques are compared and analyzed. Compared to state-of-the-art cooperative coevolutionary MOEAs: cooperative coevolutionary generalized differential evolution 3 (CCGDE3), multiobjective evolutionary algorithm based on decision variable analyses (M0EA/DVA) and DPCCMOEA, DPCCMOEA-RecSys can achieve better optimization results; compared to serial algorithms: CCGDE3 and MOEA/DVA, DPCCMOEA and DPCCMOEA-RecSys significantly reduce the time consumption. (C) 2017 Elsevier B.V. All rights reserved.