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.     

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.     

顾客偏好的动态挖掘算法

基于顾客偏好随时间变化的特性，采用聚类、关联规则等技术，对顾客偏好进行动态挖掘．通过追踪顾客购买序列，最终产生Top N产品推荐，旨在提高推荐系统的推荐质量．然后选取协同过滤算法作对照，并采用MovieLens站点提供的测试数据集．通过对召回率和精度两项指标的分析，表明该动态挖掘算法具有较高的推荐准确度和全面性．     

Mining shopping behavior in the Taiwan luxury products market

The rapid growth of Taiwan’s economy has been accompanied by the country’s developing market for luxury products. To successfully establish the new market demand chain for the luxury industry in Taiwan, it is essential to understand customer preferences. Thus, this study uses an association rules approach and clustering analysis for data mining to mine knowledge among luxury product-buying customers in Taiwan. The results of knowledge extraction from data mining, illustrated as knowledge patterns, rules and knowledge maps, are used to make recommendations for future developments in the luxury products industry.     

Extracting knowledge of customers’ preferences in massively multiplayer online role playing games

Due to fierce competition in game markets, to identify customers’ true needs is one of the crucial factors in online game industry. Traditionally, game producers heavily rely on game testers, who are primarily responsible for analyzing computer games, finding software defects and being a part of quality control process, to achieve this goal. But, it is not often reliable. To ensure the investment can be returned, game producers need an effective approach to discover frequently shifted customer preferences in time. Recently, Kano model and data mining techniques have been successfully applied to recognize customers’ preferences and implement customer relationship management tasks, respectively. However, in traditional Kano analysis, only basically statistical analysis techniques are used, and they are insufficient to provide advanced knowledge to enterprisers. Therefore, in order to discover the relationship between/among quality elements in Kano model and to extract knowledge related to customer preferences, this study proposes a knowledge acquisition scheme that integrates several data mining techniques including association rule discovery, decision tree, and self-organizing map neural network, into traditional Kano model. An actual case of customer satisfaction survey regarding massively multiplayer online role playing game has been provided to demonstrate the effectiveness of our proposed scheme.     

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.     

Customized products recommendation based on probabilistic relevance model

Product customization is attracting more attentions in industry as a viable strategy to better meet customer requirements and gain more profit. However the vast number of product variants in product customization process often makes it difficult for consumers to make purchase decisions, a phenomenon referred to as information overload. In this paper we take a two-prong approach to tackle the issue of information overload in customized products recommendation. Basically, the method answers two questions, namely, which products to recommend and in what order to present the recommendations. Firstly, a probability relevance model is deployed to calculate the probability of relevance for each end product. Then a probability ranking principle is exploited to present the recommendations. The approach also takes customer flexibility into consideration and thus mitigates the effect of inconsistent specifications from customers. It does not require any prior knowledge about an active customer’s preference and can accommodate the new customers challenge facing by recommendation approaches. Analytical results show that the method is optimal in terms of customer’s utility and product recommendation efficiency. Numerical experiments are also conducted to test the presented approach.     

Discovering Frequent Closed Partial Orders from Strings

Mining knowledge about ordering from sequence data is an important problem with many applications, such as bioinformatics, Web mining, network management, and intrusion detection. For example, if many customers follow a partial order in their purchases of a series of products, the partial order can be used to predict other related customers' future purchases and develop marketing campaigns. Moreover, some biological sequences (e.g., microarray data) can be clustered based on the partial orders shared by the sequences. Given a set of items, a total order of a subset of items can be represented as a string. A string database is a multiset of strings. In this paper, we identify a novel problem of mining frequent closed partial orders from strings. Frequent closed partial orders capture the nonredundant and interesting ordering information from string databases. Importantly, mining frequent closed partial orders can discover meaningful knowledge that cannot be disclosed by previous data mining techniques. However, the problem of mining frequent closed partial orders is challenging. To tackle the problem, we develop Frecpo (for frequent closed partial order), a practically efficient algorithm for mining the complete set of frequent closed partial orders from large string databases. Several interesting pruning techniques are devised to speed up the search. We report an extensive performance study on both real data sets and synthetic data sets to illustrate the effectiveness and the efficiency of our approach     

A recommender system to avoid customer churn: A case study

A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of three months. The data of the first nine weeks is used as the training data, and that of the last month as the testing data. The results of the experiment are found to be very useful for making strategy recommendations to avoid customer churn.     

基于偏好的客户模糊聚类方法

在电子商务环境下,如何按照顾客的购买兴趣进行聚类分析并为其提供个性化服务,是电子商务应用中研究的热点课题之一时.顾客的浏览行为及兴趣进行了研究,提出了利用偏好度的方法来度量顾客的兴趣度,在此基础上给出了基于偏好的客户群聚类算法.在该算法中,依据Web日志数据计算顾客偏好度,建立偏好度矩阵,再利用模糊聚类方法对顾客进行聚类.并用实例说明了具体的聚类过程.     

A cognitive approach for agent-based personalized recommendation

There is an increasing need for various e-service, e-commerce and e-business sites to provide personalized recommendations to on-line customers. This paper proposes a new type of personalized recommendation agents called fuzzy cognitive agents. Fuzzy cognitive agents are designed to give personalized suggestions based on the user’s current personal preferences, other user’s common preferences, and expert’s domain knowledge. Fuzzy cognitive agents are able to represent knowledge via extended fuzzy cognitive maps, to learn users’ preferences from most recent cases and to help customers make inferences and decisions through numeric computation instead of symbolic and logic deduction. A case study is included to illustrate how personalized recommendations are made by fuzzy cognitive agents in e-commerce sites. The case study demonstrates that the fuzzy cognitive agent is both flexible and effective in supporting e-commerce applications.     

Product development with data mining techniques: A case on design of digital camera

Many enterprises have been devoting a significant portion of their budget to product development in order to distinguish their products from those of their competitors and to make them better fit the needs and wants of customers. Hence, businesses should develop product designing that could satisfy the customers’ requirements since this will increase the enterprise’s competitiveness and it is an essential criterion to earning higher loyalties and profits. This paper investigates the following research issues in the development of new digital camera products: (1) What exactly are the customers’ “needs” and “wants” for digital camera products? (2) What features is more importance than others? (3) Can product design and planning for product lines/product collection be integrated with the knowledge of customers? (4) How can the rules help us to make a strategy during we design new digital camera? To investigate these research issues, the Apriori and C5.0 algorithms are methodologies of association rules and decision trees for data mining, which is implemented to mine customer’s needs. Knowledge extracted from data mining results is illustrated as knowledge patterns and rules on a product map in order to propose possible suggestions and solutions for product design and marketing.     

Mining demand chain knowledge of life insurance market for new product development

Demand chain management (DCM) can be defined as “extending the view of operations from a single business unit or a company to the whole chain. Essentially, demand chain management focuses not only on generating drawing power from customers to purchase merchandises on the supply chain; but also on exploring satisfaction, participation, and involvement from customers in order for enterprises to understand customer needs and wants. Thus, customers have changed their position in the demand chain to assume a leading role in bringing more benefit for enterprises. This article investigates what functionalities best fit the consumers’ needs and wants for life insurance products by extracting specific knowledge patterns and rules from consumers and their demand chain. By doing so, this paper uses the a priori algorithm and clustering analysis as methodologies for data mining. Knowledge extraction from data mining results is illustrated as market segments and demand chain analysis on life insurance market in Taiwan in order to propose suggestions and solutions to the insurance firms for new product development and marketing.     

A hybrid recommendation technique based on product category attributes

Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers’ preferences and desires. Extracting users’ preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users’ unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user’s preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.     

Analyzing social choice and group ranking of online games for product mix innovation

With the increasing populations of the smartphones and mobile devices these years, the majority of people have played the mobile or online games on their smartphones. As such users have rapidly increased, this research is to propose an innovative service that can automatically recommend the users the appropriate online games as their optional references. In order for market competition, the most online games’ enterprises need to develop and sell the newer online game products or versions continuously for the customers who like to play online games. Now that the types and markets of online games have had the more diverse along with the sustainable development of online game products over time. This research is to propose an integrative analysis from a large amount of data -users’ preference sequences. Firstly, the research utilizes a sequence recommendation technology that can be used to analyze their preference sequences of online game’s types based on other similar users’ sequence preference data. In other words, these recommended online game’s types that can be generated according to the relationships between the query user’s preferences and users themselves. All sequences from numerous data are accumulated to be the inferences of the query users’ preferences. Secondly, the research also utilizes data mining methodology to explore the possibilities of different product mix of game types from numerous user data. For the main methodology, this research uses a ‘partial user ranking algorithm’ to analyze group ranking of online games based on similar users. The purpose is to explore how the players can be recommended based on the other users’ priorities, as well as the product mix of game types which can be the potential business trends come to markets. Furthermore, this study contributes to a research implication for online game industry, and the results also could be the business references of game combinations and product design.     

Incorporating customer personalization preferences in open product architecture design

Open product architecture is a key enabler for product personalization, as it allows the integration of personalized modules in a product architecture to satisfy individual customer needs and preference. A critical challenge for integrating personalized modules into a product architecture is determining the optimal assembly architecture when considering market expectations and manufacturing constraints. In this paper, an optimization method is proposed for determining the personalized product design architecture that incorporates individual customer preferences. First, a decision hierarchy is presented to describe the integrated design decisions of the product architecture, including product variety determination, module variant selection, and personalized module configuration. Next, a profit model is formulated as an overall performance metric that incorporates customer preferences and manufacturing cost. The systematic patterns and randomness of diverse customer preferences are modeled by combining conjoint analysis and market segmentation with a multivariate normal mixture model. Individual customer product utilities in the target market and their product purchase intent probability are estimated through Monte-Carlo simulation, which is incorporated into the profit calculation. Manufacturing limitations on processes and materials are included as they influence manufacturer’s planning on candidate module variants and production strategies of personalized modules. These models are used to determine a product family architecture that maximizes profit by optimally determining its offering of product variants, module combinations, and personalized module configuration through a genetic algorithm. The proposed method is demonstrated by a personalized bicycle architecture design example.     

Amazon.com recommendations: item-to-item collaborative filtering

Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.     

Dynamic modelling of customer preferences for product design using DENFIS and opinion mining

Previous studies mainly employed customer surveys to collect survey data for understanding customer preferences on products and developing customer preference models. In reality, customer preferences on products could change over time. Thus, the time series data of customer preferences under different time periods should be collected for the modelling of customer preferences. However, it is difficult to obtain the time series data based on customer surveys because of long survey time and substantial resources involved. In recent years, a large number of online customer reviews of products can be found on various websites, from which the time series data of customer preferences can be extracted easily. Some previous studies have attempted to analyse customer preferences on products based on online customer reviews. However, two issues were not addressed in previous studies which are the fuzziness of the sentiment expressed by customers existing in online reviews and the modelling of customer preferences based on the time series data obtained from online reviews. In this paper, a new methodology for dynamic modelling of customer preferences based on online customer reviews is proposed to address the two issues which mainly involves opinion mining and dynamic evolving neural-fuzzy inference system (DENFIS). Opinion mining is adopted to analyze online reviews and perform sentiment analysis on the reviews under different time periods. With the mined time series data and the product attribute settings of reviewed products, a DENFIS approach is introduced to perform the dynamic modelling of customer preferences. A case study is used to illustrate the proposed methodology. The results of validation tests indicate that the proposed DENFIS approach outperforms various adaptive neuro-fuzzy inference system (ANFIS) approaches in the dynamic modelling of customer preferences in terms of the mean relative error and variance of errors. In addition, the proposed DENFIS approach can provide both crisp and fuzzy outputs that cannot be realized by using existing ANFIS and conventional DENFIS approaches.     

On the making of service recommendations: An action theory based on utility,reputation, and risk attitude

In the current service-oriented economy, professional workforce and service personnel have to make not only reasonable but also personalized recommendations in response to individual customer’s query. These actions affect not only the likelihood that the customer takes the recommendations in a short-term but also the service provider’s reputation in a long run because often more risky recommendations may provide more utility but failures have a negative impact to reputation. As different customers have different risk attitudes, they have different trade-off between the service providers’ reputation and the recommendations’ utilities. Therefore, the classical decision model considering only the utility is inadequate. We reconsider the problem of making recommendations from multiple perspectives, including also reputation and risk attitude. Based on this model, we propose an action theory to explain how service providers can act effectively at the strategic, tactical, and operations level, particularly through personalized recommendations.     

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.