摘要翻译:
传统的推荐系统只根据客户过去的购买、产品评级和人口统计数据进行推荐,而不考虑被推荐项目的盈利能力。在这项工作中,我们研究了一个问题,如何一个供应商可以直接结合项目的盈利能力,以其推荐,使其期望利润最大化,同时仍然提供准确的推荐。我们的方法使用任何传统推荐系统的输出,并根据项目的利润调整它们。我们的方法是参数化的,所以供应商可以控制多少建议合并利润可以偏离传统的建议。我们在两种情况下研究了我们的方法,并表明它比传统的建议实现了大约22%的利润。
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英文标题:
《Maximizing profit using recommender systems》
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作者:
Aparna Das, Claire Mathieu, Daniel Ricketts
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最新提交年份:
2009
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Computers and Society 计算机与社会
分类描述:Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
涵盖计算机对社会的影响、计算机伦理、信息技术和公共政策、计算机的法律方面、计算机和教育。大致包括ACM学科类K.0、K.2、K.3、K.4、K.5和K.7中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Information Retrieval 信息检索
分类描述:Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引,字典,检索,内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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英文摘要:
Traditional recommendation systems make recommendations based solely on the customer's past purchases, product ratings and demographic data without considering the profitability the items being recommended. In this work we study the question of how a vendor can directly incorporate the profitability of items into its recommender so as to maximize its expected profit while still providing accurate recommendations. Our approach uses the output of any traditional recommender system and adjust them according to item profitabilities. Our approach is parameterized so the vendor can control how much the recommendation incorporating profits can deviate from the traditional recommendation. We study our approach under two settings and show that it achieves approximately 22% more profit than traditional recommendations.
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PDF链接:
https://arxiv.org/pdf/0908.3633