Personalized Recommender System becomes an important research field in Electronic Commerce, and the main goal of current recommendation models is provide Best-Service to users. But, from enterprise’s viewpoint, the Max-Earning strategy is necessary to improve the benefit of enterprise. To solving this problem, knapsack model is applied to describe the commonly used Top-N recommend mechanism firstly. Then, the enterprise’s earnings are described as a constraint in knapsack model, a product recommended algorithm is proposed at the basis of optimization of knapsack problem. Experimental results show the proposed algorithm has similar performance with CF model when earning requirement and amount of recommended products is lower. So, both user’s value and enterprise’s value are improved through the proposed algorithm.
Gao, Linqi, "A Product Recommendation Algorithm Based on Knapsack Optimization" (2012). Eleventh Wuhan International Conference on e-Business. 36.