The Application of Deep Learning Intelligent Robots in the Design and Implementation of Information Retrieval Systems

Abstract

Traditional information retrieval algorithms ignore user needs and are unable to obtain user gaze coordinates and gaze times, resulting in low retrieval accuracy. The author proposes a new interactive information retrieval algorithm for this purpose. Divide eye tracking technology evaluation indicators, visually process eye movement information, obtain user gaze coordinates and gaze time, and calculate the influence coefficients of each gaze area and each point in the area. Weighted visual words are accumulated to get a visual word list with the weight of the associated area, and visual word list and Rocchio algorithm are combined to build a hidden Relevance feedback retrieval model in semantic space to judge information retrieval preferences. Intelligent robot is introduced, and Jensen Shannon divergence is used to calculate the Kullback–Leibler divergence distance between the probability distributions of document sets, calculate the similarity matching, and complete the interactive information retrieval. The simulation results prove that, due to the introduction of intelligent robot strategy in this method, the amount of irrelevant retrieval information is reduced by matching user needs and retrieval results. Therefore, the amount of messages generated by information retrieval is significantly lower than that of the two literature methods, without adding additional network load. The network system is in a stable operation state, and users can quickly grasp their own required information, and the retrieval speed is also improved to a certain extent. It is proved that the proposed algorithm has high retrieval accuracy, can effectively reduce network load and achieve high-quality human-computer interactive information retrieval.