标题: 整合顾客个人化与卖方获利性之商品推荐系统
The Recommender Systems Integrating Customers' Personalization and Sellers' Profitability
作者: 陈穆臻
CHEN MU-CHEN
国立交通大学交通运输研究所
关键字: 商品推荐系统;顾客关系管理;资料探勘;协同过滤;Recommender System;Customer Relationship Management;Data Mining;CollaborativeFiltering
公开日期: 2008
摘要: 目前既有的推荐系统(Recommender Systems)进行商品推荐时,主要考虑商品的
购买机率(product purchase probability),而忽略了企业应用推荐系统的同时亦希望追
求获利增加。因此,本三年计画探讨以企业(卖方)与顾客(买方)为不同出发点考
量之数种推荐系统。此外,亦有学者应用资料探勘(Data Mining)技术之关联法则
(Association Rules)由购物篮(Market Basket)中探勘强物项(Frequent Itemsets),以进行
商品推荐。然而,此方法亦仅透过强物项考虑顾客个人化(Customers’ Personalization)
与产品被顾客购买之频率,而未考虑卖方之获利性(Sellers’ Profitability)。
由卖方的角度而言,系依据整体产品购买机率(overall product purchase probability)
与产品获利性(product profitability)进行推荐;由买方的角度而言,系依据个别顾客的
偏好(individual customer ’s preferences)推荐。本计画结合买方观点及卖方观点建构商
品推荐系统。在同时考虑买方观点及卖方观点情况下,建构以协同过滤(Collaborative
Filtering)为基础与关联法则为基础之商品推荐系统,必须考虑多准则以推荐商品。所
以,本计画亦应用资料包络分析(Data Envelopment Analysis; DEA)发展多准则资料探
勘技术,并应用于商品推荐系统。本计画同时以模拟资料与企业真实资料验证所发展
之推荐系统并与传统方法进行比较分析。推荐准确率(recommendation accuracy)与由
交叉销售所得利润(profit from cross-selling)为评估指标比较不同观点的推荐系统。
依据上述,本三年计画期望分析比较六种推荐系统,并且发展整合之商品推荐系
统以应用于实务上。此考量个人化推荐及/或商品获利性之六种推荐系统包含:
1. Convenience perspective recommender system (CPRS);
2. Convenience plus profitability perspective recommender system (CPPRS); (本计画发展
之系统)
3. Collaborative filtering perspective recommender system (CFRS);
4. Hybrid perspective recommender system (HPRS); (本计画发展之系统)
5. Association rule perspective recommender system (APRS);
6. Multi-criteria association rule perspective recommender system (MCARS). (本计画发展
之系统)
Companies need to shift from the old world of mass production where “standardized products,
homogeneous markets, and long product life and development cycles were the rule” to the new world
where “variety and customization supplant standardized products”. This three-year project attempts
to develop and analyze several recommender systems based on the perspectives of enterprises (sellers)
and customers (buyers). From the sellers’ perspective, recommendations are made based on the
overall product purchase probability and the product profitability; from the buyers’ perspective,
recommendations are made based on ind ividual customers preferences and personalization. From the
perspective of customers, recommender systems merely try to suggest suitable products to satisfy the
needs of customers. However, enterprises employ recommender systems not only to satisfy
customers’ needs but also to profit more. Therefore, the value (profit margin) of products should also
be taken into consideration in developing recommender systems. The application of collaborative
filtering recommender systems in the physical context of retailing will be analyzed and discussed in
this project.
In existing some recommender systems, association rule mining is employed to recommend
products. However, due to the simultaneous consideration of the overall product purchase probability,
the product profitability, customer preference and personalization, recommendations are made with
multiple criteria. The recommendation approaches need be developed to resolve the issue of
multi-criteria. In the previous studies regarding the discovery of subjectively interesting association
rules, most approaches require manual input or interaction by asking users to explicitly indicate what
kinds of rules are interesting and uninteresting. This project aims at using a non-parametric approach,
Data Envelopment Analysis (DEA), to estimate the efficiency (interestingness or usefulness) of
association rules with multiple criteria.
Six recommender systems are compared in terms of recommendation accuracy and/or profit
from cross-selling. They are as follows:
1. Convenience perspective recommender system (CPRS);
2. Convenience plus profitability perspective recommender system (CPPRS);
3. Collaborative filtering perspective recommender system (CFRS);
4. Hybrid perspective recommender system (HPRS);
5. Association rule perspective recommender system (APRS)
6. Multi-criteria association rule perspective recommender system (MCARS).
CPPRS, HPRS and MCARS are novel perspectives, which additionally take sellers’
profitability measures into consideration. In the MCARS, DEA will be adopted to estimate the
efficiency (interestingness or usefulness) of association rules with multiple criteria. In terms of
recommendation accuracy and/or profit from cross-selling, comparisons can be made between CPRS
and CPPRS, between CPRS and CFRS, between CFRS and HPRS, among CPRS, CFRS and HPRS,
among CPPRS, CFRS and HPRS, and among APRS and MCARS. Furthermore, an integrated
recommender system will be developed for real-world applications with supportive company in this
project.
官方说明文件#: NSC95-2416-H009-034-MY3
URI: http://hdl.handle.net/11536/101973
https://www.grb.gov.tw/search/planDetail?id=1591652&docId=272978
显示于类别:Research Plans