Merging visual features and temporal dynamics in sequential recommendation
Abstract With the development of social networking and mobile computing technologies, data analysis in the fashion field has increasingly focused on visual features. The main features currently used in the recommendation methods include non visual user attributes, item attributes, explicit ratings, and implicit feedbacks. How to understand visual features and integrate them with non visual features becomes the key to building a good recommender system. In this paper, we consider both non visual text data and visual image data and their time dynamics to build a large scale recommender system. An advanced visual Bayesian personalized ranking (aVBPR) model is proposed, which integrates three models. Factorized personalized Markov chains (FPMC) model is used to simulate users' sequence behaviors, intelligent field aware factorization machine (iFFM) model also put forward by us is used to predict users' preferences based on non visual features, and visual Bayesian personalized ranking (VBPR) model is used to analyze users' visual preferences. We design a learning algorithm based on AdaGrad method to optimize model aVBPR. The high complexity of the model does not affect the performance of the system by adopting multi thread technology in the implementation of the learning algorithm. Experimental results of two real world datasets Women's and Men's Clothing Accessories from Amazon demonstrate that our model can obtain better recommendation results than the recent popular models for Amazon datasets. Although the model is complicated, multi thread technique can be used to greatly improve the speed of the implementation.
Neurocomputing