No abstract

Motivated by Hema Freshs new-retail case, we noticed that an effective recommender system is a common way to attract the consumers’ purchasing behaviors and thus enlarge the profit of platform as well as retailers. With the aim of increasing the benefits of all parties in the platform, this paper focusing on not only increasing the effectiveness of the recommender platform but also the evaluation system of measuring the interests of consumer, retailers and platform. In this paper, the interests of the third-party platform are added into the evaluation system, the profit of the third-party platform as an evaluation index is taken and a 0–1 integer programming model is established which sets the profit of the platform as the objective function. The result of the proposed model and algorithm indicate that: (1) The relevance of products has a significant impact on platform recommendation when the consumers are selecting products. When the correlations of the products are high, the algorithms of selecting the products will have a lower capacity of 1% compared with the algorithm without products correlations. (2) The evaluation of the target products from the target consumers is quite different from the heterogeneity assumptions. When the consumer presentation is taken into consideration, it is hard to evaluate the consumer presence because of the strictly requirement of data for the platform recommendation system. (3) The proposed two-stage solution for the platform recommendation system is optimized in time and space complexity. Total optimization of the proposed method is 30% higher than the greedy algorithms. The two stages are combined together to obtain the approximate solution, and finally provide a reasonable and feasible recommendation for the third-party platform.

Purpose This paper aims to develop an optimal buyback promotion strategy for enterprises, including multibuyback strategy and self-buyback strategy, taking both the consumer's multichannel psychological acquisition attributes and remaining market into account.Design/methodology/approach Based on the game theory and Hotelling model, the authors formulate a new model to study the equilibrium of different buyback models, given the utility maximization of the consumers, the profit maximization and the constraint on nondecreasing market share of the enterprises, and the authors conduct comparative analysis.Findings Intuitively, enterprises buying back products of other brands would appeal to some consumers. However, the authors find that after implementing the multibuyback scheme, enterprises may not be able to seize competitors' markets or even lose their original customer base in the context considered in this article counterintuitive. In addition, the size of remaining market share and the consumer's multichannel psychological acquisition affect the choice of buyback promotion strategies. Moreover, after implementing multibuyback scheme, customers with old products subsidize those who receive additional discounts. Finally, the authors point out that the buyback strategy choices of companies with different goal-oriented are diverse.Practical implications This study has a very solid realistic background and provides guidance for enterprises to implement buyback promotion strategies. In addition, the authors unearth new influencing factors to provide a reasonable explanation for different buyback strategies in reality.Originality/value To the best of the authors’ knowledge, this study is one of the first to explore the multibuyback promotion strategy as a new buyback method, where the two influencing factors the authors have not been proposed so far.