Multi-Feature Video Recommendation Based on Hypergraph Convolution for Mobile Edge Environment

Introduction

With the broad development of mobile edge computing, various types of edge devices are deployed on edge nodes, and more and more services can be obtained through these devices. However, when faced with a large number of services, users find it difficult to discover required services according to their preferences. Recommendation systems have therefore become an effective mitigation method, and accurate recommendation methods can push products/services in terms of users’ interests. When it comes to video recommendation, the attributes of videos are not as explicit as that of other products. Challenges of how to obtain features successfully by certain keywords from videos need to be addressed. As a result, video recommendation has become a research hotspot.

In mobile edge environment, there will be a lot of interactions between users. And these data contain a wealth of information, which is of great significance to helping feature extraction and video recommendation. Suppose a scenario to push videos to customers/users, we can make recommendations based on the characteristics of neighbors. Videos can be classified into different types such as romance, comedy and mystery. As shown in Figure 1, both users, Bob and Alice, like to watch comedy, then Bob can be defined as Alice’s neighbor. According to the preferences of neighbors, videos that have not been watched by Alice, such as mystery, can be recommended to her.

Figure 1.

Scenarios of user Interaction in mobile edge environment

Traditional video recommendation methods, such as content-based video recommendation (Dong et al., 2018; Ramadhan & Musdholifah, 2021; Subercaze et al., 2016), collaborative filtering video recommendation (Di Yu & Chen, 2020; Shen et al., 2020) and hybrid video recommendation (Pérez-Marcos et al., 2020; Yan et al., 2015; Zhou et al., 2019). Content-based recommendation methods use video content to predict the preferences of target users, recommending videos to users that are similar to what the user previously liked or watched. Collaborative filtering recommendation methods use user feedback information, such as previous ratings and viewing history, to predict user preferences. Hybrid recommendation methods combine user feedback and consumed video content to improve recommendations. Those methods only consider the simple interactive relationships that cannot mine users’ comprehensive interest representations (Stoica & Chaintreau, 2019; Xu et al., 2021; Yang et al., 2019; Yang et al., 2021). Some methods (Cai et al., 2022; Pingali et al., 2022) improve the performance of model by mining the information in the video data, but the excessive computational overhead makes it unsuitable for some low latency scenarios, such as mobile edge environments. In recent years, some studies have used deep learning to construct users' features. J. Chen et al. (2017) proposed two attention models to construct user’s features at component-level and item-level, and Chen et al. (2018) proposed an attention model to combine users’ features at category-level and item-level. However, such recommendation systems may lead to poor recommendation quality due to the following reasons:

• 1.

  Most traditional video recommendation methods regard the relationship between user and neighbor to be linear and ignore higher-order connectivity among users, and cannot obtain the user feature based on neighborhood well, which leads to poor recommendation quality.

• 2.

  Most traditional video recommendation methods mine users’ single-feature from single-source data, which are usually sparse and cannot obtain valid users’ interest representations.