An Item Recommendation Approach by Fusing Images based on Neural Networks
There are rich formats of information in the network, such as rating, text, image, and so on, which represent different aspects of user preferences. In the field of recommendation, how to use those data effectively has become a difficult subject. With the rapid development of neural network, researching on multi-modal method for recommendation has become one of the major directions. In the existing recommender systems, numerical rating, item description and review are main information to be considered by researchers. However, the characteristics of the item may affect the user's preferences, which are rarely used for recommendation models. In this work, we propose a novel model to incorporate visual factors into predictors of people's preferences, namely MF-VMLP, based on the recent developments of neural collaborative filtering (NCF). Our experiments conduct Amazon's public dataset for experimental validation and root mean square error (RMSE) as evaluation metrics. To some extent, experimental result on a real-world dataset demonstrates that our model can boost the recommendation performance.