###### Abstract  
In the field of personalized recommendation, user-generated content (UGC) such as videos, images, and product comments are becoming increasingly important, since they implicitly represent the preferences of users. The vectorized representation of a commodity with multisource and heterogeneous UGC is the key for sufficiently mining the preference information to make a recommendation. Existing studies have mostly focused on using one type of UGC, e.g., images, to enrich the representation of a commodity, ignoring other contents. When more UGC are fused, complicated models with heavy computation cost are often designed. Motivated by this, we proposed a low-computational-power model for vectorizing multisource and recommendation UGC to achieve accurate commodity representations. In our method, video description keyframes, commodities' attribute text, and user comments were selected as the model's input. A multi-model fusion framework including feature extraction, vectorization, fusion, and classification based on MobileNet and multilayer perceptrons was developed. In this UGC fusion framework, feature correlations between images and product comments were extracted to design the loss function to improve the precision of vectorized representation. The proposed algorithm was applied to an actual representation of a commodity described by UGC, and the effectiveness of the proposed algorithm was demonstrated by the classification accuracy of the commodity represented. commodity; vectorized representation; user-generated content; multisource and heterogeneous; multimodel fusion; loss function +
Footnote †: journal:  
as user comments, videos, and images. In addition, short videos are becoming increasingly popular for providing users with more comprehensive information. Videos, text, and images, provided by merchants or buyers, are all part of the user-generated content. These data are of great value for descriptions of a commodity and personalized recommendations. Fusing this information to represent a product is one of the hot topics in personalized recommendation over recent years. For the same product, UGC comes from multiple users (merchants and buyers) and are expressed as videos, images, attributes, comments, ratings, and other forms, which are multisource and heterogeneous. Such a characteristic makes the vectorized expression of UGC more comprehensive, whereas the heterogeneity increases the difficulty of extraction and fusion of the data features. How to effectively and accurately express a product in a vector with the multisource and heterogeneous UGC has become a key step in personalized recommendations. Feature extraction and fusion of the multisource heterogeneous UGC are first required to achieve vectorized representation of commodities in a unified vector space [1; 2; 3]. In the field of personalized recommendations, there are few studies on the fusion of representations of UGC, especially concerning the video information of the products. In addition, the consistency of heterogeneous information from multiple sources has been considered in many studies, and this consistency is used in the design of the fusion strategy. Considering the consistency of heterogeneous information, it is necessary to integrate the differences and complementarity of UGC to realize the representation of a product from multiple perspectives. Accordingly, we proposed a fusion strategy based on the diversity and consistency of UGC to achieve accurate representations of products. Images containing global and local information were extracted as representative key video frames based on a user-personalized keyframe extraction algorithm [4]. Taking positive, neutral, and negative comments into account, we also used the comments from multiple users and attribute text of the product. Based on this UGC, an algorithm for feature extraction for fusing images and text was designed.