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 multimodal 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 useru0027s preferences, which are rarely used for recommendation models. In this work, we propose a novel model to incorporate visual factors into predictors of peopleu0027s preferences, namely MFVMLP, based on the recent developments of neural collaborative filtering (NCF). Our experiments conduct Amazonu0027s public dataset for experimental validation and root mean square error (RMSE) as evaluation metrics. To some extent, experimental result on a realworld dataset demonstrates that our model can boost the recommendation performance.

A Novel Hybrid Deep Recommendation System to Differentiate Userxe2x80x99s Preference and Itemxe2x80x99s Attractiveness Abstract   With the fast development of online Ecommerce Websites and mobile applications, usersxe2x80x99 auxiliary information as well as productsxe2x80x99 textual information can be easily collected to form a vast amount of training data. Therefore, research efforts are urgently needed to make customized recommendations using such large but sparse data. Deep recommendation model is a natural choice for this research issue. However, most existing approaches try to investigate either userxe2x80x99s auxiliary information such as age and zipcode, or itemxe2x80x99s textual information such as product descriptions, reviews or comments. Therefore, it is desired to see whether userxe2x80x99s auxiliary information and itemxe2x80x99s textual information could be modeled simultaneously. This paper proposes a novel approach which is essentially a hybrid probabilistic matrix factorization model. Particularly, it has two sub components. One component tries to predict userxe2x80x99s rating scores by capturing userxe2x80x99s personal preferences extracted from auxiliary information. Another component tries to model itemxe2x80x99s textual attractiveness to different users via a proposed attention based convolutional neural network. We then propose a global objective function and optimize these two sub components under a unified framework. Extensive experiments are performed on five realworld datasets, i.e., ML100K, ML1M, ML10M, AIV and Amazon sub dataset. The promising experimental results have demonstrated the superiority of our proposed approach when compared with both baseline models and stateoftheart deep recommendation approaches, i.e., PMF, CDL, CTR, ConvMF, ConvMF and DAttn with respect to RMSE criterion.