Clustering of Goods and User Profiles for Personalizing in E commerce Recommender Systems Based on Real Implicit Data
The work is devoted to description of a hybrid approach to the preparation of data for electronic commerce recommender systems. The efficiency of recommendations is improved via use of various algorithms depending on what is known about the user, little or no information or it is not the first visit and the browsing history is available. In the first case our own Item Item CF method is used enabling to solve the problem of cold start. In the second case our own method User User CF is applied. Both methods are based on clustering of both explicit and implicit user data. This approach can increase the number of items in the basket in fewer clicks in comparison with the methods that do not use the implicit data. The approach applicability was confirmed via test on real data obtained from Thaisoap, an online store.
DAMDID/RCDL