基于客户喜好的双向个性化推荐算法

为了解决现有推荐算法仅考虑同类产品间单向推荐所缺乏的灵活性,提升产品的销量及用户的购物体验,提出一种基于客户喜好的双向个性化推荐算法,不仅可以为客户精准推荐产品,还可以为商家推荐潜在客户.首先,基于产品购买网络中客户及其邻居的购买信息,扩展客户购买信息;其次设计客户产品喜好权重计算办法,分析客户的购买喜好,并在客户喜好的指导下为客户提供个性化的产品推荐;最后,基于商家提供的样本客户,挖掘与样本客户相似的客户构成社区,为商家提供潜在客户推荐以及精准客户维护.在真实数据集上的实验验证了算法的有效性.该算法从客户和商家两个维度出发实现了产品与客户的双向推荐,为个性化推荐领域的研究提供有益的帮助.     

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.     

An optimum design selection approach for product customization development

Consumer preferences and information on product choice behavior can be of significant value in the development processes of innovative products. In this paper, product customization evaluation and selection model is introduced to support imprecision inherent of qualitative inputs from customers and designers in the decision making process. Focusing on customer utility generation, an optimum design selection approach based on fuzzy set decision-making is proposed, where design attributes priority is identified from customer preferences using an analytical hierarchy process. A multi-attribute analysis diagram is developed to visualize the preference of each attribute from the expert’s group decision. Conjoint analysis is used in the product customization to focus on customer utility generation in terms of multiple criteria. The use of the decision-making method is illustrated with a case example that highlights the utility of the proposed method.     

Integrating AHP and data mining for product recommendation based on customer lifetime value

Product recommendation is a business activity that is critical in attracting customers. Accordingly, improving the quality of a recommendation to fulfill customers’ needs is important in fiercely competitive environments. Although various recommender systems have been proposed, few have addressed the lifetime value of a customer to a firm. Generally, customer lifetime value (CLV) is evaluated in terms of recency, frequency, monetary (RFM) variables. However, the relative importance among them varies with the characteristics of the product and industry. We developed a novel product recommendation methodology that combined group decision-making and data mining techniques. The analytic hierarchy process (AHP) was applied to determine the relative weights of RFM variables in evaluating customer lifetime value or loyalty. Clustering techniques were then employed to group customers according to the weighted RFM value. Finally, an association rule mining approach was implemented to provide product recommendations to each customer group. The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a typical collaborative filtering (CF) method.     

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.     

Ontology-based personalized couple clustering for heterogeneous product recommendation in mobile marketing

Plethora of cellular phones has been increasingly driving the spread of e-commerce mechanisms running on mobile devices. For instance, mobile marketing fulfills the wireless delivery (to the devices of mobile users) of the recommended product information and even one-to-one recommendations. One-to-one recommendation not only reduces the time that customers have to expend to for attaining appropriate products, but also is a method to engender customer values and develop the long-term customer relationships. This paper presents a one-to-one recommendation mechanism that iteratively takes as inputs the audio customer messages (together with product information) and produces personalized product analogy structures (that subsequently drive the generation of personalized heterogeneous product recommendations) based on the coupled clustering algorithm. The personalized product analogy structures also evolve as the messages (of the correspondent customer) grow. We have implemented the mechanism with J2EE Web Service that has produced fairly promising evaluation results.     

Session-based item recommendation in e-commerce: on short-term intents,reminders, trends and discounts

Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the capability of recommender systems to help users discover items they presumably do not know yet and which match their long-term preference profiles. In reality, however, recommendations can be helpful for customers also for other reasons, for example, when they remind them of items they were recently interested in or when they point site visitors to items that are currently discounted. In this work, we first adopt a systematic statistical approach to analyze what makes recommendations effective in practice and then propose ways of operationalizing these insights into novel recommendation algorithms. Our data analysis is based on log data of a large e-commerce site. It shows that various factors should be considered in parallel when selecting items for recommendation, including their match with the customer’s shopping interests in the previous sessions, the general popularity of the items in the last few days, as well as information about discounts. Based on these analyses we propose a novel algorithm that combines a neighborhood-based scheme with a deep neural network to predict the relevance of items for a given shopping session.     

Service customization under capacity constraints: an auction-based model

In mass customization, companies strive to enhance customer value by providing products and services that are approximate to customers’ needs. A company’s strategy of allocating its limited capacity to meeting diverse customer requirements directly impact customer perceived value in terms of available options, cost, and schedule. Proposed in this paper is an auction-based mass customization model for solving the problem of service customization under capacity constraints. The proposed model integrates customers’ customization decision making with the allocation of company’s capacity through multilateral negotiation between the company and its customers. The negotiation is conducted through a combinatorial iterative auction designed to maximize the overall customer value given limited capacity. The auction is incentive-compatible in the sense that customers will follow the prescribed myopic best-response bidding strategy. Experimental results indicate that customization solutions computed by the proposed model are very close to the optimal one. Revenue performance is also adequate when there is sufficient competition in the market.     

Willing to pay for quality personalization? Trade-off between quality and privacy

Online personalization presents recommendations of products and services based on customers’ past online purchases or browsing behavior. Personalization applications reduce information overload and provide value-added services. However, their adoption is hindered by customers’ concerns about information privacy. This paper reports on research undertaken to determine whether a high-quality recommendation service will encourage customers to use online personalization. We collected data through a series of online experiments to examine the impacts of privacy and quality on personalization usage and on users’ willingness to pay and to disclose information when using news and financial services. Our findings suggest that under certain circumstances, perceived personalization quality can outweigh the impact of privacy concerns. This implies that service providers can improve the perceived quality of personalization services being offered in order to offset customer privacy concerns. Nevertheless, the impact of perceived quality on personalization usage is weaker for customers who have experienced privacy invasion in the past. The results show that customers who are likely to use online personalization are also likely to pay for the service. This finding suggests that, despite privacy concerns, there is an opportunity for businesses to monetize high-quality personalization.     

Understanding collaborative filtering parameters for personalized recommendations in e-commerce

Collaborative Filtering (CF) is a popular method for personalizing product recommendations for e-Commerce and customer relationship management (CRM). CF utilizes the explicit or implicit product evaluation ratings of customers to develop personalized recommendations. However, there has been no in-depth investigation of the parameters of CF in relation to the number of ratings on the part of an individual customer and the total number of ratings for an item. We empirically investigated the relationships between these two parameters and CF performance, using two publicly available data sets, EachMovie and MovieLens. We conducted three experiments. The first two investigated the relationship between a particular customer’s number of ratings and CF recommendation performance. The third experiment evaluated the relationship between the total number of ratings for a particular item and CF recommendation performance. We found that there are ratings thresholds below which recommendation performance increases monotonically, i.e., when the numbers of customer and item ratings are below threshold levels, CF recommendation performance is affected. In addition, once rating numbers surpass threshold levels, the value of each rating decreases. These results may facilitate operational decisions when applying CF in practice.     

Selecting prospects for cross-selling financial products using multivariate credibility

Insurance policies or credit instruments are financial products that involve a long-term relationship between the customer and the company. For many companies a possible way to expand its business is to sell more products to preferred customers in its portfolio. Data on the customers’ past behaviour is stored in the company’s database and these data can be used to assess whether or not more products should be offered to a specific customer. In particular, data on past claiming history, for insurance products, or past information on defaulting, for banking products, can be useful for determining how the client is expected to behave in other financial products. This study implements a method for using historical information of each individual customer, and the portfolio as a whole, to select a target group of customers to whom it would be interesting to offer more products. This research can help to improve marketing to existing customers and to earn higher profits for the company.     

Applying fuzzy multiple attributes decision making for product configuration

Mass customization (MC) is an emergent concept in industry intended to provide customized products through flexible processes in high volumes and at reasonably low costs. The method of configuration is one of important ways to realize quickly product customization. But, in business, particularly through the Internet, a customer normally develops in his mind some sort of ambiguity, given the choice of similar alternative products. This paper proposes a new approach to product configuration by applying the theory of fuzzy multiple attribute decision making (FMADM), which focus on uncertain and fuzzy requirements the customer submits to the product supplier. The proposed method can be used either in the product data management system or e-commerce websites, with which it is easy for customers to get his preferred product according to the utility value with respect to all attributes. Finally, the digital camera is taken as an example to further verify the validity and the feasibility of the proposed method.     

集成AHP和数据挖掘的个性推荐系统研究

提出了一个基于层次分析和数据挖掘的个性推荐系统。运用层次分析法来评价顾客生命周期价值中每一个RFM变量的重要程度,根据加权的RFM来对顾客进行聚类分析,通过关联规则挖掘从顾客簇中抽出频繁购买模式,根据簇中关联规则向顾客推荐相关商品。实验表明性能优于相等权重的聚类方法和不进行聚类直接从所有顾客中进行关联规则挖掘的方法。     

A novel method for determining the key customer requirements and innovation goals in customer collaborative product innovation

Customer collaborative production innovation (CCPI) has become a worldwide new product design trend. The essential step to implement CCPI is to clear customer requirements and innovation goals for products. Based on the integration of traditional competitive priority ratings of customer requirements method for quality function deployment and grey relational analysis, this paper proposes a novel hybrid competitive priority ratings of customer requirements method for CCPI to identify the key customer requirements and innovation goals for a product. The method takes the heterogeneity of customers into consideration and allows different types of customers to assess customer requirements in their preferred or familiar formats which reflect their uncertainty degree. The proposed hybrid competitive priority ratings of customer requirements method represents a general approach for CCPI, does not require any transformation of multiform customers’ assessments that would cause information loss or information distortion. Its potential applications in determining the key customer requirements and innovation goals for CCPI are illustrated with a case study of smart phone development.     

Rank order-based recommendation approach for multiple featured products

Recommendation methods, which suggest a set of products likely to be of interest to a customer, require a great deal of information about both the user and the products. Recommendation methods take different forms depending on the types of preferences required from the customer. In this paper, we propose a new recommendation method that attempts to suggest products by utilizing simple information, such as ordinal specification weights and specification values, from the customer. These considerations lead to an ordinal weight-based multi-attribute value model. This model is well suited to situations in which there exist insufficient data regarding the demographics and transactional information on the target customers, because it enables us to recommend personalized products with a minimal input of customer preferences. The proposed recommendation method is different from previously reported recommendation methods in that it explicitly takes into account multidimensional features of each product by employing an ordered weight-based multi-attribute value model. To evaluate the proposed method, we conduct comparative experiments with two other methods rooted in distance-based similarity measures.     

Influences of customer preference development on the effectiveness of recommendation strategies

Most previous studies on recommendation agents have been restricted to the problems of uncovering customer preferences during the process of understanding customers. However, studies on consumer psychology have indicated that customer preferences are often unstable and developed over time. Therefore, we assert that it is necessary to observe the degree to which customer preferences are developed since effectiveness of recommendations is affected by customers’ preference development. This study presents a scheme to identify the status of customers’ preference development and analyzes the influences of customer preference development on the effectiveness of various recommendation strategies.     

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.     

Product recommendation with latent review topics

Online customer reviews complement information from product and service providers. While the latter is directly from the source of the product and/or service, the former is generally from users of these products and/or services. Clearly, these two information sets are generated from different perspectives with possibly different sets of intentions. For a prospective customer, both these perspectives together provide a complementary set of information and support their purchase decisions. Given the different perspective and incentive structure, the information from these two source sets tends to be necessarily biased, clearly with the high probability of negative information omission from that provided by the product/service providers. Moreover, customers oftentimes face information overload during their attempts at deciphering existing online customer reviews. We attempt to alleviate this through mining hidden information in online customer reviews. We use a variant of the Latent Dirichlet Allocation (LDA) model and clustering to generate equivalent options that the customer could then use in their purchase decisions. We illustrate this using online hotel review data.     

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.     

Does reliable information matter? Towards a trustworthy co-created recommendation model by mining unboxing reviews

Online review forums provide customers with powerful platforms to express opinions and influence business trends, while allowing firms to collaborate and co-create value with customers. However, information overload due to the huge amount of reviews posted daily complicates the efforts of consumers to locate reliable information when making a purchase decision. Therefore, this study develops a trustworthy co-created recommendation model. The proposed model mines unboxing reviews, calculates the trust scores of the reviewers, and then generates the recommended products by combing this information with customer preferences using a multi-criteria decision-making method. An illustrative example of mobile phones demonstrates the recommendation procedure of the proposed model. The proposed model is evaluated via an empirical experiment to examine the satisfaction of study participants by using a seven-point Likert scale. An analysis of the structural equation modelling results indicates that three factors (i.e. confidence in decision quality, enhanced problem-solving ability, and satisfaction with resource expenditure) significantly and positively affect the purchase decision-making process. Moreover, the proposed model outperforms a baseline model in all four factors, ultimately increasing user satisfaction. In addition to its theoretical framework for co-creating value with customers to develop a trustworthy co-created recommendation model, as supported by various theories of trust, the proposed model provides further insights into the role of customer reviews in designing recommendation models, as well as the extent to which such models impact user decisions.