基于多级客户模型的个性化推荐机制

个性化是未来Web智能系统的一大特征．为了实现商品的个性化推荐，提出了一种新的基于多级客户模型的推荐系统机制，它由数据准备、模型学习、推荐集的生成和智能过滤四个子过程构成．该机制借助于多级客户模型从客户的购物需求、偏爱特征和消费能力三方面捕获客户的实际需求，从而实现了一种深层次的个性化推荐，改善了推荐效果．     

Evaluating customer aid functions of online stores with agent-based models of customer behavior and evolution strategy

With competitive pressure growing in online markets, many Internet stores provide various customer aid functions such as personalized pages to help customers shop more effectively and efficiently. Evaluating such customer aid functions is usually costly because it requires full or partly-working systems and many human testers. In order to address this problem, this research presents a novel approach to evaluating customer aid functions with agent-based models of customer behavior and evolution strategies. Agent-based modeling is used to imitate users’ rational behavior at Internet stores with regard to browsing and collecting product information. It is assumed that users evolve their browsing skill and strategy over time, to maximize the efficiency and effectiveness of their shopping, and hence, evolution strategy, an optimization method, is combined with the agent-based model to find the rational behavior of each user. The rational behavior is then used to simulate the virtual shopping of users and to evaluate the performances of target customer aid functions. Several experiments were performed to illustrate the use of the approach, where the personalized recommendation page of a virtual online DVD rental store is evaluated in comparison with more general functions such as listing most popular products or sorting categories. The results show that a personalized page might not always be the best customer aid function for all users compared to the simpler ones.     

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.     

Customized purchase supporting expert system: UNIK-SES

An expert system that assists the customized men's wear purchasing process is developed. In the past, configuration systems have adopted either a rule- or a constraint-based representation. To overcome the limitation of the representation, an integrated representation of the Constraint and Rule Satisfaction Problems (CRSP) is adopted. Since the general purpose tool UNIK-CRSP provides concurrent, integrated and interactive reasoning, UNIK-CRSP is very suitable for assisting the personalized customer product configuration process in a natural manner. Using UNIK-CRSP, the domain-specific tool UNIK-SES is developed with the additional features of candidate products selection and user interfaces. This approach should be useful for customer support in electronic marketing on the information superhighway.     

基于数据挖掘的移动广告个性化推荐研究

移动互联网目前已经成为当今最具潜力的市场之一,与此同时,移动广告作为一种新兴的网络营销模式也得以迅速发展起来。诸多企业已经把目标投放在移动广告市场这一领域。当前,移动广告主要利用传统的广告定位策略,通过应用程序展示触发客户需求并以此来进行传播,从而导致移动用户被动地接受一些并不感兴趣的广告内容,严重干扰了用户的正常使用,甚至会泄露用户的个人隐私。文中从分析用户个人的行为和如何定位用户的实时位置出发,研究基于移动互联网下广告个性化推荐策略,提出研究模型,开发基于用户位置和兴趣的实时广告推荐服务系统。     

A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet

Selecting tourist attractions to visit at a destination is a main stage in planning a trip. Although various online travel recommendation systems have been developed to support users in the task of travel planning during the last decade, few systems focus on recommending specific tourist attractions. In this paper, an intelligent system to provide personalized recommendations of tourist attractions in an unfamiliar city is presented. Through a tourism ontology, the system allows integration of heterogeneous online travel information. Based on Bayesian network technique and the analytic hierarchy process (AHP) method, the system recommends tourist attractions to a user by taking into account the travel behavior both of the user and of other users. Spatial web services technology is embedded in the system to provide GIS functions. In addition, the system provides an interactive geographic interface for displaying the recommendation results as well as obtaining users’ feedback. The experiments show that the system can provide personalized recommendations on tourist attractions that satisfy the user.     

基于电子商务的个性化推荐系统研究

在电子商务环境中,实现个性化服务,理解用户兴趣就成了提供个性化服务的关键任务。因此,建立用户兴趣模型和构建推荐库就成为个性化推荐系统的实现基础。论文通过网络爬虫获取到相关的网页,进行预处理后,采用SVM（支持向量机）分类文档建立推荐库。通过对用户访问路径、搜索关键字等分析,获取用户兴趣,采用向量空间模型表示用户兴趣,利用机器学习构建用户兴趣模型。在推荐库和用户兴趣模型的基础上,加入推荐引擎,实现了基于电子商务的个性化推荐系统。     

基于协同过滤算法的微信小程序智能助手

随着信息技术和网络的不断迅猛发展,互联网的信息资源急剧增长.信息过载问题促进了个性化推荐技术发展.协同过滤算法通过在用户和信息之间建立联系,被广泛应用于电子商务各个领域.本文提出通过利用微信小程序来获取用户的个性化信息数据,并且通过协同过滤算法,为用户设计的微信小程序智能助手,能够为用户推荐符合用户个性化的生活服务信息.在本文中,介绍了智能助手的设计方法,并详细介绍了系统的功能和个性化推荐功能的实现.     

基于随机森林模型的电信运营商外呼推荐系统

在电信运营商领域,外呼推荐是一种重要的推荐产品和服务的途径。实现了一种基于运营商大数据的自动外呼推荐系统,该系统能够挖掘用户的行为特征并且使用机器学习的方法预测用户对于被推荐产品的接受可能性。传统推荐系统使用的模型算法为矩阵分解、大规模稀疏特征分类、神经网络等。采用随机森林算法的主要原因是随机森林具有并行化程度高、训练速度快、生成的决策树可解释等诸多优点,适合于基于电信业数据的推荐系统。该外呼推荐系统基于Hadoop、Impala和Spark等大数据处理平台及工具,使用随机森林分类器作为核心算法,将用户最近的行为特征回归为接受外呼推荐产品的可能性。在线测试表明使用该系统与当前部署的人工随机外呼相比,能够提升约41%的用户接受率;同时,根据模型算法输出特征的重要性,进一步给出了两类用户的特征分析。     

一种动态混合QoS语义的Web服务个性化推荐模型

服务推荐是服务计算中的主要问题之一,当前大多针对功能属性进行推荐,而在Web服务的QoS属性方面考虑较少,并且不支持动态变化的QoS属性。基于动态混合QoS的语义Web服务个性化推荐模型,把语义Web技术引入Web服务中,在QoS监控器下,有效监测Web服务的QoS属性变化并动态更新Web服务的QoS属性。根据建立的用户兴趣模型,向用户推荐具有个性化的Web服务。此外,在个性化推荐系统中使用最广泛的协同过滤推荐技术基础上,对数据进行了一系列的预处理填充,而且充分考虑了不同时间的项目评分对推荐的影响。结合用户兴趣度和用户评分的相似性计算方法,并通过不同的权值来表示它们的重要程度,综合计算目标用户的最近邻居集合,最终对用户u产生推荐。该系统在一定程度上提高了服务推荐的效率和准确度并满足用户查询需求。     

Finding users preferences from large-scale online reviews for personalized recommendation

Along with the growth of Internet and electronic commerce, online consumer reviews have become a prevalent and rich source of information for both consumers and merchants. Numerous reviews record massive consumers’ opinions on products or services, which offer valuable information about users’ preferences for various aspects of different entities. This paper proposes a novel approach to finding the user preferences from free-text online reviews, where a user-preference-based collaborative filtering approach, namely UPCF, is developed to discover important aspects to users, as well as to reflect users’ individual needs for different aspects for recommendation. Extensive experiments are conducted on the data from a real-world online review platform, with the results showing that the proposed approach outperforms other approaches in effectively predicting the overall ratings of entities to target users for personalized recommendations. It also demonstrates that the approach has an advantage in dealing with sparse data, and can provide the recommendation results with desirable understandability.     

A novel approach of making better recommendations by revealing hidden desires and information curation for users of internet of things

One of the most significant disadvantages of the Internet of Things (IoT) is the overload of information. More information makes it harder to find valuable information. Recommendation systems identify the most suitable items for a given user. The recommended result is only valid if the system users know what they want, and clearly and explicitly convey their needs to the system. Because the role of a recommendation system is to calculate the similarity between the given request and each item, and to rank the similarity, the requests and identity of items should be clear to obtain correct results. However, in most situations in which recommendations are made, requests are implicit and ambiguous. A good recommendation system should make a reliable list of items, even with ambiguous requests. This paper proposes a model of generating recommendations for implicit requests. The model employs two methods that reveals the desire of the requestor and uses content curation with a customized layout to display the recommendations. The first method for revealing the requestor’s desire is to specify the implicit request by combining the user’s customized preference with the collective intelligence. The second method for employing content curation is to arrange the recommendation for users to accept spontaneously. To persuade users, the recommendations are transformed into a layout based on a personalized cognitive bias. Through these processes, reliable and beneficial recommendations can be provided to any user even if their requests are implicit or unclear.

     

Multistakeholder recommendation: Survey and research directions

Recommender systems provide personalized information access to users of Internet services from social networks to e-commerce to media and entertainment. As is appropriate for research in a field with a focus on personalization, academic studies of recommender systems have largely concentrated on optimizing for user experience when designing, implementing and evaluating their algorithms and systems. However, this concentration on the user has meant that the field has lacked a systematic exploration of other aspects of recommender system outcomes. A user-centric approach limits the ability to incorporate system objectives, such as fairness, balance, and profitability, and obscures concerns that might come from other stakeholders, such as the providers or sellers of items being recommended. Multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article outlines the multistakeholder perspective on recommendation, highlighting example research areas and discussing important issues, open questions, and prospective research directions.

     

基于个性化线路定制的景区导游APP设计与实现

计算机和互联网技术日新月异,为旅游产业提供了新的发展思路。基于中国互联网信息中心发布的旅游数据进行分析,得出借助计算机和互联网技术实现个性化定制旅游将成为未来旅游市场的主流方式。文章从用户特点出发,以景区内游玩线路的定制为切入点,着重阐述线路定制对个性化旅游的重要性,最后设计并实现了一款基于线路定制的景区导游APP,该APP通过模块化设计思想,将系统分为定位模块、线路定制模块、线路查询模块、推荐模块等,引入最优路径、最短路径算法思路来满足游客的个性化需求。文章的思想对未来旅游产业的转型有着一定探索和指导作用。     

Combining user preferences and user opinions for accurate recommendation

Recommendation systems represent a popular research area with a variety of applications. Such systems provide personalized services to the user and help address the problem of information overload. Traditional recommendation methods such as collaborative filtering suffer from low accuracy because of data sparseness though. We propose a novel recommendation algorithm based on analysis of an online review. The algorithm incorporates two new methods for opinion mining and recommendation. As opposed to traditional methods, which are usually based on the similarity of ratings to infer user preferences, the proposed recommendation method analyzes the difference between the ratings and opinions of the user to identify the user’s preferences. This method considers explicit ratings and implicit opinions, an action that can address the problem of data sparseness. We propose a new feature and opinion extraction method based on the characteristics of online reviews to extract effectively the opinion of the user from a customer review written in Chinese. Based on these methods, we also conduct an empirical study of online restaurant customer reviews to create a restaurant recommendation system and demonstrate the effectiveness of the proposed methods.     

面向Internet的个性化智能信息检索

文中提出了一个面向Ｉｎｔｅｒｎｅｔ的个性化智能信息检索系统,该系统的研究内容属目前智能信息检索领域的重要研究课题。系统采用分布式智能体Ａｇｅｎｔ技术、相关反馈学习算法和基于多用户个性化模式的层次智能信息滤波算法,并采用多模块之间尖线交互机制和个性化检索与集中游览相结合的检索方式等方法,有效地解决了现有系统在交互方式、自适应用户兴趣和信息源的变化、高效并行检索等方面的不足,该系统普通适用于Ｉｎｔｅｒ     

Agent-based architecture for context-aware and personalized event recommendation

In order to offer context-aware and personalized information, intelligent processing techniques are necessary. Different initiatives considering many contexts have been proposed, but users preferences need to be learned to offer contextualized and personalized services, products or information. Therefore, this paper proposes an agent-based architecture for context-aware and personalized event recommendation based on ontology and the spreading algorithm. The use of ontology allows to define the domain knowledge model, while the spreading activation algorithm learns user patterns by discovering user interests. The proposed agent-based architecture was validated with the modeling and implementation of eAgora? application, which was illustrated at the pervasive university context.     

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.     

User-adapted travel planning system for personalized schedule recommendation

Recently, the Internet has made a lot of services and products appear online provided by many tourism sectors. By this way, many information such as timetables, routes, accommodations, and restaurants are easily available to help travelers plan their travels. However, how to plan the most appropriate travel schedule under simultaneously considering several factors such as tourist attractions visiting, local hotels selecting, and travel budget calculation is a challenge. This gives rise to our interest in exploring the recommendation systems with relation to schedule recommendation. Additionally, the personalized concept is not implemented completely in most of travel recommendation systems. One notable problem is that they simply recommended the most popular travel routes or projects, and cannot plan the travel schedule. Moreover, the existing travel planning systems have limits in their capabilities to adapt to the changes based on users’ requirements and planning results. To tackle these problems, we develop a personalized travel planning system that simultaneously considers all categories of user requirements and provides users with a travel schedule planning service that approximates automation. A novel travel schedule planning algorithm is embedded to plan travel schedules based on users’ need. Through the user-adapted interface and adjustable results design, users can replace any unsatisfied travel unit to specific one. The feedback mechanism provides a better accuracy rate for next travel schedule to new users. An experiment was conducted to examine the satisfaction and use intention of the system. The results showed that participants who used the system with schedule planning have statistical significant on user satisfaction and use intention. We also analyzed the validity of applying the proposed algorithm to a user preference travel schedule through a number of practical system tests. In addition, comparing with other travel recommendation systems, our system had better performance on the schedule adjustment, personalization, and feedback giving.     

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.