Abstract: Abstract: The Bayesian Personalized Ranking algorithm is one of the most representative algorithms for implicit feedback problems， but both the assumption of independence between users and the assumption of individuals’ pairwise preferences for two items proposed in the BPR algorithm are too restrictive. The GBPR algorithm redefines the individual preferences of users， using group preferences formed by like-minded users instead of individual preferences to relax the assumption of independence among users. The DPR algorithm takes the partial order pair as the basic unit to optimize the difference between preferences rather than the difference of preferences to relax the assumption of an individual’s pairwise preference for two items. Based on the above research， this paper proposes an e-GDPR algorithm to further enhance the user’s ability to predict preferences for items. The algorithm can make full use of user information （such as gender， consumption level） and commodity information （such as category） in the data set， introduce group preference into the DPR algorithm， divide users into groups according to consumption level and gender， randomly sample to form more representative user groups， and no longer use random sampling directly when sampling.Instead， a triad sample consisting of two randomly selected commodities belonging to the same category is considered to be more reliable than a triad sample consisting of randomly selected commodities. Then the implicit feedback preference quantification model is introduced to calculate the user’s personal preference， which can fully consider the user’s preference behind various implicit operation types. Finally， a recommendation experiment is carried out on the Jingdong e-commerce data set， and the experimental results show that the e-GDPR algorithm can achieve better recommendation results compared with the baseline algorithm.

Key words: Key words：Bayesian personalized ranking, recommendation algorithm, implicit feedback, sampling methods, group preference