Alleviating the New Item Cold-Start Problem by Combining Image Similarity

Cold-start scenarios in recommender systems are situations in which no historical behavior, like ratings or clicks, are known for certain users or items. Aiming at the cold-start problem caused by the addition of the new item in the recommender system, this paper proposed a collaborative filtering recommendation model (USPMF-CFIA) based on matrix factorization model, which combines the similarity of item image and category attributes. First, it used the matrix factorization model based on users' preference to predict and fill the missing rating items. Then, it used the VGG16 neural network to extract the features of the item images and combined category attributes to calculate the similarity between the new item and the historical items, then got the item's neighbors. Finally, the new item's score was predicted based on the similarity between the new item and the neighbors, and the top N items with high scores are recommended to the user. The experiment on the dataset provided by GroupLens proved that this model is more accurate.