Personalized Product Ranking in E-commerce with User-Item Network Models
Personalisation of product rankings in e-commerce is needed because different users have different interests, demands and browsing conditions. A user-item network model can be employed to represent clicks, favourites, additions to shopping carts, ratings and purchases for personalised Top-k ranking in this paper. This review connects PageRank and Personalised PageRank with graph collaborative filtering, multi-behaviour graph learning, temporal self-supervision and industrial pre-ranking systems. According to the above research, different types of behaviour, the order of interaction and time information, graph construction methods, negative sampling, etc., can affect the ranking results. However, there are still many problems such as scarce and noisy implicit feedback, cold start, popularity bias, preference drift, scalability, privacy, fairness and lack of interpretability. In the future, research will continue to be carried out in the field of combining dynamic heterogeneous graphs, multi-behavioural objectives, causal and self-supervised learning, privacy-preserving computation and graph foundation models. In short, the user-item network model applies linear algebra, probability theory, graph propagation and representation learning to solve the problem of personalised e-commerce ranking. It is convenient to conduct a comparison of the models and decide which one to use.