Deep Reinforcement Learning-Based Multi-Object Adaptive Route Planning for Traveling Recommender Systems

In travel recommendation systems, the most important business demand is to make adaptive route planning and navigation decision based on computational intelligence. Existing researches mostly lacked the ability to adaptively suggest route planning schemes for users, according to contextual features. To deal with the problem, this paper proposes a deep reinforcement learning-based multi-object adaptive route planning method for travel recommendation systems. In particular, the technical framework utilizes both value iterative network (VIN) module and positioning module to construct a specific DRL algorithm for this purpose. It inputs the environment map of the agent into VIN module, obtains the value distribution corresponding to the map by means of convolution iteration, and extracts features by means of self-positioning. Such a technical framework is expected to improve generalization ability of navigation strategies. Empirically, this work selects some popular scenic spots in a certain region as the research object for case analysis, and collects road network traffic data and tourist scenic spot parameter data through two public travel service platforms. The experimental results show that the suburban tourism route planning model based on deep reinforcement learning scene perception is reasonable and feasible, and can effectively improve tourist satisfaction.