POI Recommend for Deep Neural Network Based on Explicit and Implicit Feature Joint
Hailun Zhan, Zhiming Ding, Mengmeng Chang, Xinhui Liu · 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) · 2021
With the development of deep learning technology, a series of new recommendation algorithms are produced by using the idea of deep learning in the field of recommendation algorithm. These recommendation algorithms usually only mine user, item and context related features, and then combine the features, and use neural network for model training. However, it may be difficult for us to find a suitable feature expression for some features. At the same time, it is difficult for us to find all the feature expression forms for all kinds of features of a certain thing, which leads to the recommendation effect not reaching the expected level. To solve this problem, this paper proposes a deep neural network based on explicit and implicit feature joint (EIFJDNN) model, which is different from the features determined in Feature Engineering in the past. The features used in this model include explicit features and implicit features. In the recommendation of interest points, the access of interest points is affected by both time and space, we take on temporal and spatial features as explicit features, and other influencing factors as implicit features for recommendation. We designed FGMF algorithm, which is an improvement of GMF model. We use it to pretrain the model, then input the results into deep neural network for training, predict the score of each user on the points of interest, and then rank the scores to get the final recommendation list. Finally, we verified the recommendation performance of EIFJDNN model on Foursquare dataset, and the results show that the proposed model is superior to the existing work in hit rate and normalized discounted cumulative gain.