A personalized recommendation procedure for Internet shopping support

The rapid growth of e-commerce has caused product overload where the customer is no longer able to effectively choose the products he/she is exposed to. To overcome the product overload of Internet shoppers, several recommender systems have been developed. Recommendation systems track past actions of a group of customers to make a recommendation to individual members of the group. We introduce a personalized recommendation procedure by which we can get further recommendation effectiveness when applied to Internet shopping malls. The suggested procedure is based on Web usage mining, product taxonomy, association rule mining, and decision tree induction. We applied the procedure to a leading Internet shopping mall in Korea for performance evaluation, and some experimental results are provided. The experimental results show that choosing the right level of product taxonomy and the right customers increases the quality of recommendations.     

Decision-making in the aisles: informing, overwhelming or nudging supermarket shoppers?

Recent findings suggest that while shopping people apply ‘fast and frugal’ heuristics: short-cut strategies where they ignore most product information and instead focus on a few key cues. But rather than supporting this practice, mobile phone shopping apps and recommender systems overwhelm shoppers with information. This paper examines the amount and structure of product information that is most appropriate for supermarket shoppers, finding that in supermarkets, people rapidly make decisions based on one or two product factors for routine purchases, often trading-off between price and health. For one-off purchases, shoppers can be influenced by reading customer star ratings and reviews on a mobile phone app. In order to inform decision-making or nudge shoppers in supermarkets, we propose using embedded technologies that provide appropriate feedback and make key information salient. We conclude that rather than overwhelming shoppers, future shopping technology design needs to focus on information frugality and simplicity.     

An agent-based recommender system for developing customized families of products

Electronic markets and web-based content have improved traditional product development processes by increasing the participation of customers and applying various recommender systems to satisfy individual customer needs. Agent-based systems based on agents’ roles and tasks can provide appropriate tools to solve product design problems by recommending design knowledge and information. This paper introduces an agent-based recommender system to support designing families of products based on customers’ preferences in dynamic electronic market environments. In the proposed system, a market-based learning mechanism is applied to determine the customers’ preferences for recommending appropriate products to customers of the product family. We demonstrate the implementation of the proposed recommender system using a multi-agent framework. Through simulated experiments, we illustrate that the proposed recommender system can help determine the preference values of products for customized recommendation and market segment design in various electronic market environments.     

基于遗传算法的超市最短导购路径推荐

大型超市里商品数目的繁多、空间布局的复杂往往容易让消费者迷失在室内,花费大量时间进行商品搜寻。提出基于遗传算法的超市导购路径推荐方法,通过对超市的空间布局结构离散化建模,生成分别用节点和无向边表示商品区域和区域之间可行走路线的平面图;结合消费者的采购清单,根据商品所在的货架位置将商品与具体的区域做出匹配,用遗传算法优化生成一条联结超市入口、要采购的商品区域以及结账柜台的最短路线。Matlab的仿真结果显示,该方法简单、高效,能够快速为消费者推荐出最短路线,供其购物参考。     

Extended latent class models for collaborative recommendation

With the advent of the World Wide Web, providing just-in-time personalized product recommendations to customers now becomes possible. Collaborative recommender systems utilize correlation between customer preference ratings to identify "like-minded" customers and predict their product preference. One factor determining the success of the recommender systems is the prediction accuracy, which in many cases is limited by lacking adequate ratings (the sparsity problem). Recently, the use of latent class model (LCM) has been proposed to alleviate this problem. In this paper, we first study how the LCM can be extended to handle customers and products outside the training set. In addition, we propose the use of a pair of LCMs (called dual latent class model-DLCM), instead of a single LCM, to model customers' likes and dislikes separately for enhancing the prediction accuracy. Experimental results based on the EachMovie dataset show that DLCM outperforms both LCM and the conventional correlation-based method when the available ratings are sparse.     

Discovering patterns of online purchasing behaviour and a new‐product‐launch strategy

Spurred by rapid development of computers and Internet technology, online shopping is gradually overtaking in‐store shopping, because of advantages such as convenience, more choice of products or services etc. Online stores must devote a great deal of time and resources to locating and attracting new customers. Growing a customer base requires first understanding customers and then providing the products or services they need, thus encouraging customers to purchase more. This paper develops a system to analyse customers’ purchasing behaviour and track shifts in their interests. Customers’ purchasing behaviour is measured using proposed standard product loyalty status and standard brand loyalty status. Using these metrics, together with the preference map established for each customer, a marketing specialist can easily locate potential customers to target when a company launches a new product. The new‐product‐launch strategy proposed in this paper can be used to create a list of potential customers for a product being launched under a variety of conditions. A prototype system has been built to test the feasibility of the proposed new‐product‐launch strategy. The result shows almost 40% of potential customers respond to the recommendation positively.     

基于客服对话的产品和服务质量分析

现有的产品和服务质量分析常通过问卷调查或利用商品评论,但存在问卷收集难度大、商品评论中存在无效数据等问题。客服对话作为顾客与商家之间沟通的桥梁,包含了丰富的顾客意见,覆盖了从产品到服务的多个方面,然而现在利用客服对话分析产品和服务质量的研究还较少。提出了一种基于客服对话的产品和服务质量分析方法,首先结合产品特征和服务蓝图,确定产品和服务质量的评价要素,并结合重要性?满意度分析（IPA）法定义评价要素的重要性和满意度指标;然后对客服对话进行主题提取和情感分析,定量分析产品和服务的重要性和满意度。以某消毒除菌产品淘宝旗舰店的真实客服对话为例应用了该方法,建立了18个评价要素,并基于90余万条真实的历史客服对话对评价要素的重要性和满意度进行了量化,从而分析旗舰店产品和服务的质量。最后通过对专业客服的问卷调研,验证了所提方法的有效性。     

The moderating role of customer–technology contact on attitude towards technology-based services

Previous studies in information systems research and service marketing treat customer behaviour towards technology-based services (TBS) homogeneously. However, recent studies recognize that users have different attitude towards different technologies even if these technologies used to support the same service. Drawing on literature from service marketing (i.e. customer contact theory), information systems (unified theory of technology acceptance), and organizational behaviour (task complexity theory), this study proposes a construct that classifies TBS according to the level of customer–technology interaction they require, namely the customer–technology contact (CTC). The moderating effect of this construct on the relationship between individual characteristics – that is technology readiness and attitude towards TBS – is examined through an empirical study. Technology-based retail services scenarios, with different levels of technology contact, are presented to supermarket shoppers (n=600). Results show that CTC, as a unique service attribute, moderates the effect of personality traits to customers’ attitude. The current study introduces this new service attribute that is applicable to ubiquitous computing services, application and design.     

Modelling and Forecasting Customer Navigation in Intelligent Retail Environments

Understanding shopper behaviour is one of the keys to success for retailers. In particular, it is necessary that managers know which retail attributes are important to which shoppers and their main goal is to improve the consumer shopping experience. In this work, we present sCREEN (Consumer REtail ExperieNce), an intelligent mechatronic system for indoor navigation assistance in retail environments that minimizes the need for active tagging and does not require metrics maps. The tracking system is based on Ultra-wideband technology. The digital devices are installed in the shopping carts and baskets and sCREEN allows modelling and forecasting customer navigation in retail environments. This paper contributes the design of an intelligent mechatronic system with the use of a novel Hidden Markov Models (HMMs) for the representation of shoppers’ shelf/category attraction and usual retail scenarios such as product out of stock or changes on store layout. Observations are viewed as a perceived intelligent system performance. By forecasting consumers next shelf/category attraction, the system can present the item location information to the consumer, including a walking route map to a location of the product in the retail store, and/or the number of an aisle in which the product is located. Effective and efficient design processes for mechatronic systems are a prerequisite for competitiveness in an intelligent retail environment. Experiments are performed in a real retail environment that is a German supermarket, during business hours. A dataset, with consumers trajectories, timestamps and the corresponding ground truth for training as well as evaluating the HMM, have been built and made publicly available. The results in terms of Precision, Recall and F1-score demonstrate the effectiveness and suitability of our approach, with a precision value that exceeds the 76% in all test cases.     

Mining affective needs of automotive industry customers for building a mass-customization recommender system

Mass customization systems aim to receive customer preferences in order to facilitate personalization of products and services. Current online configuration systems are unable to efficiently identify real customer affective needs because they offer an excess variety of products that usually confuse customers. On the other hand, mining affective customer needs may result in recommender systems, which can enhance existing configuration systems by recommending initial configurations according to customer affective needs. This paper introduces a mass customization recommender system that exploits data mining techniques on automotive industry customer data aiming at revealing associations between user affective needs and the design parameters of automotive products. One key novelty of the presented approach is that it deploys the Citarasa engineering, a methodology that focuses on the provision of the appropriate characterizations on customer data in order to associate them with customer affective needs. Based on the application of classification techniques we build a recommendation engine, which is evaluated in terms of user satisfaction, tool’s effectiveness, usefulness and reliability among other parameters.     

Consumption universes based supermarket layout through association rule mining and multidimensional scaling

The success of retail business is influenced by its fast response and its ability in understanding consumers’ behaviors. Analysis of transaction data is the key for taking advantage of these new opportunities, which enables supermarkets to understand and predict customer behavior, has become a crucial technique for effective decision-making and strategy formation. We propose a methodological framework for the use of the knowledge discovery process and its visualization to improve store layout. This study examines the layout strategy in relation to supermarket retail stores and assists managers in developing better layout for supermarkets. We use the buying association measure to create a category correlation matrix and we apply the multidimensional scale technique to display the set of products in the store space. This is a new approach to supermarket layout from industrial categories to consumption universes that is consumer-oriented store layout approach through a data mining approach. This framework is useful for both academia and retail industry. For industry professionals, it may be used to guide development of successful layout. Retailers can utilize the proposed model to dynamically improve their in-store conversion rate. As the empirical study, a practical application proceeded for Migros Turk, a leading Turkish retailing company.     

Intelligent agent-based systems for personalized recommendations in Internet commerce

The prosperity of electronic commerce has changed the traditional trading behaviors and more and more people are willing to conduct Internet shopping. However, the exponentially increasing information provided by the Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer's satisfaction and loyalty. One way to overcome such a problem is to build personalized recommender systems to retrieve product information that really interests the customers. For products that people may purchase relatively often, such as books and CDs, recommender systems can be built to reason about a customer's personal preferences from his purchasing history and then provide the most appropriate information services to meet his needs. On the other hand, for those commodities a general customer does not buy frequently, for example computers and home theater systems, more appropriate are the kinds of recommender systems able to retrieve optimal products based on the customer's current preferences obtained from the iterative system–customer interactions. This paper presents the above two kinds of recommender systems we have developed for supporting Internet commerce. Experimental results show the promise of our systems.     

Design by customer: concept and applications

Customer satisfaction can be increased by reducing the gap between what customer really needs (customer requirements) and what manufacturer can provide (product specifications). The approach of Design for Customer where products are generated by translating customer needs into product specifications (in mass production system) or into product variety (in mass customization system) is not able to give optimum satisfaction to all customers. Some customers are still forced to relax their requirements and to accept predefined product in the assortment. This study proposes a new concept of Design by Customer to increase customer satisfaction by opening maximum possible channel for customers to involve in value creation so that they are no longer only searching for goods but they can also, when necessary, involve in production cycle to specify their own design. In order to ensure the viability of the proposed concept, the integration of multi customer involvement decoupling point, product attribute analysis, crowdscreening and new manufacturing strategy are introduced in this paper. Real products of resin-based table clocks are used as practical example to verify the concept applicability and to demonstrate its merit.     

Direct mail promotion mechanisms and their application in supermarkets

In this study, we consider the problem of selecting supermarket loyalty program members to receive physical direct mail and promotional electronic direct mail (i.e., direct email). To help marketers choose the target members for physical direct mails, we modify the customer’s preference index of our original model to predict members’ repurchase rates for a physical supermarket’s members. Based on members’ predicted repurchase rates, marketers can design proper marketing strategies for different types of supermarket member to improve marketing effectiveness. In addition, because members can only spend a short amount of time reading direct emails before choosing the products that they like, a recommender system based on a simple combination method is introduced. The system determines the most suitable combination of commodity types under the condition that a customized direct email can include only a small, fixed number of such types. In this study, member transaction records from a well-known Taiwanese supermarket were used as the test data. This supermarket’s marketing department reviewed all the experimental results and confirmed that our approach is not only superior to the current approach employed by the supermarket but also useful in designing appropriate direct-mail marketing strategies for selected supermarket members. Our approach is also suitable for direct email sent by the supermarket.

     

广域推荐:社会网络与协同过滤(英文)

商务企业应用数据挖掘技术向潜在客户推荐产品。大多数推荐系统聚焦研究兴趣于特定的领域,如电影或书籍。使用用户相似度或产品相似度的推荐算法通常可以达到较好效果。然而,当面临其他领域问题时,推荐常变得非常困难,因为数据过于稀疏,难以仅基于购买历史发现用户或产品间的相似性。为解决此问题,提出使用社会网络数据,通过对历史的观察提高产品推荐有效性。利用人工协同过滤和基于社会网络的推荐算法的最新进展进行领域推荐工作。研究显示社会网络的应用对于产品推荐具有很强的指导作用,但是,高的推荐精度需以牺牲召回率为代价。数据的稀疏性意味着社会网络并不总是可用,在这种情况下提出一种解决方案,很好地利用了社会网络的有效信息。     

Feature-based recommendations for one-to-one marketing

Most recommendation systems face challenges from products that change with time, such as popular or seasonal products, since traditional market basket analysis or collaborative filtering analysis are unable to recommend new products to customers due to the fact that the products are not yet purchased by customers. Although the recommendation systems can find customer groups that have similar interests as target customers, brand new products often lack ratings and comments. Similarly, products that are less often purchased, such as furniture and home appliances, have fewer records of ratings; therefore, the chances of being recommended are often lower. This research attempts to analyze customers' purchasing behaviors based on product features from transaction records and product feature databases. Customers' preferences toward particular features of products are analyzed and then rules of customer interest profiles are thus drawn in order to recommend customers products that have potential attraction with customers. The advantage of this research is its ability of recommending to customers brand new products or rarely purchased products as long as they fit customer interest profiles; a deduction which traditional market basket analysis and collaborative filtering methods are unable to do. This research uses a two-stage clustering technique to find customers that have similar interests as target customers and recommend products to fit customers' potential requirements. Customers' interest profiles can explain recommendation results and the interests on particular features of products can be referenced for product development, while a one-to-one marketing strategy can improve profitability for companies.     

Mining changes in customer buying behavior for collaborative recommendations

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences.In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users.     

Joint optimisation of maintenance and production policies with subcontracting and product returns

This paper, an extension of our previous research, deals with the problem of jointly optimizing maintenance, production and inventory costs considering subcontracting and product returns. The manufacturing system, which fails randomly, has to satisfy a random product demand during a finite planning horizon under a required service level. The portion of products returned by the customers that are still in saleable condition are collected in the principle store from which customer demand is filled, while the portion that are non-conformal are collected in a second store and then remanufactured by a subcontractor. This study is validated by a real industrial case presented in this paper.     

An empirical study on Web-based services and customer loyalty

E-business success is tied to the ability to foster customer loyalty. Businesses that deliver superior value derived from excellent services and quality products are likely to win customer loyalty. This paper examines Web-based services and the effects of three sets of factors: pre-purchase, transaction-related, and post-purchase services on customer loyalty (measured as repeat purchase intention from a given Web-based store) in a business-to-consumer environment. Based on the study's results, pre-purchase services that support search and evaluation of products replete in e-commerce systems have limited effect on customer loyalty. Among transaction-related services, transparency of the billing mechanism positively impacts customer loyalty. Customers shun any hidden costs associated with product acquisition. Post-purchase services consisting of support of order tracking, on-time delivery, and customer support positively influence customer loyalty. These findings imply that Web-based stores need to pay more attention to post-purchase services in their strategy to retain customers. This is what will keep customers satisfied and willing to continue the relationship with a company over the long term.