【精品文档】26中英文双语计算机专业外文文献翻译成品：基于消费者行为建模的网页内容推荐系统

此文档是毕业设计外文翻译成品（ 含英文原文+中文翻译），无需调整复杂的格式！下载之后直接可用，方便快捷！本文价格不贵,也就几十块钱！一辈子也就一次的事！外文标题：Web Content Recommender System based on Consumer Behavior Modeling外文作者：ACM Fong ， B Zhou ， SC Hui ， GY Hong ， TA Do文献出处:IEEE Transactions on Consumer Electronics , 2018 , 57 (2) :962-969(如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做)英文1595单词， 8495字符(字符就是印刷符)，中文2689 汉字。Web Content Recommender System based on Consumer Behavior ModelingA. C. M. Fong, Senior Member, IEEE, Baoyao Zhou, S. C. Hui, Senior Member, IEEE,Guan Y. Hong, and The Anh DoAbstract Web surfing has become a popular activity for many consumers who not only make purchases online, but also seek relevant information on products and services before they commit to buy. The authors propose a web recommender that models user habits and behaviors by constructing a knowledge base using temporal web access patterns as input. Fuzzy logic is applied to represent real-life temporal concepts and requested resources of periodic pattern-based web access activities. The fuzzy representation is used to construct a knowledge base of the users web access habits and behaviors, which is used to provide timely personalized recommendations to the user. The proposed approach is applicable to delivery of recommendations on consumers portable devices because compute-intensive processing is performed offline and in advance. With the increasing availability and popularity of web- enabled consumer mobile devices, it is believed that the CE world of tomorrow will be increasingly web-oriented. Experiments conducted to evaluate the performance of the proposed approach have shown very good results1.Index Terms Consumer behavior modeling, personalization, web content recommender, consumer internet application.I. INTRODUCTIONThe web has become an increasingly popular medium for consumer to exchange or find ideas, opinions, experiences on products and services. Many consumers go further than online information sharing and actually perform purchases on the web. Increasing availability and popularity of portable web- enabled handheld devices looks set to fuel further growth in the volume of consumer web traffic.In the traditional web information dissemination model, consumers are familiar with pulling content from the web via mechanisms such as search engines, or simply by typing the URI/URL if they happen to know it. Researchers have long tried to make web search more efficient on handheld mobile devices e.g. 1, which generally have limited processing power and screen size compared to personal computers.II.Related WorkThis section presents a survey of work pertinent to personalized web content recommendation and consumers web access pattern mining based on periodicity information. This section therefore serves to lay the foundation for further discussion of the proposed approach in Section III.A.Personalized Web Content RecommendationPersonalized web content recommendation aims at minimizing ambiguity and unwanted information that is presented to the consumer, thereby reducing the effect of information overload that is often encountered by web surfers. According to a survey presented in 10, traditional web content recommender systems could be classified into Content-based, Collaborative and Hybrid, which is combination of the two. However, these systems tend to rely heavily on user ratings. Non-intrusiveness has been identified as an important attribute in the information gathering process for subsequent web content recommendation 10. Web usage mining, which is performed by the system in the background and transparent to the user, therefore represents an important way forward in this area of research.The fundamental requirement of an effective personalized web content recommendation system is to present the most relevant suggestions to the user in a timely manner. Thus, both context and temporal information is important. In this regard, context information can be very effective in disambiguation. For example, a biologist who wants to read articles about “mouse” will likely be interested in the rodent. On the other hand, a consumer looking for computer accessories will likely be interested in the pointing device. Context information will help resolve this type of ambiguity.Currently systems tend to lack focus on temporal information. Timeliness in the recommendation of relevant resources is also important in many situations. For example, a user may have a tendency of reading financial news and traffic information between 9am and 10am on weekdays (or even more specifically on Mondays and Tuesdays), but may tend to read entertainment news and weather information during the same time period on Saturdays.III.Consumer Behavior Knowledge BaseThe process of constructing the user behavior knowledge base begins with semantically enhanced web usage logs as input. The raw log data are then preprocessed to remove unnecessary information and to enhance the quality of the data for subsequent processing. Finally, one can proceed to construct the knowledge base using fuzzy relations as the basis. As mentioned in Section I, fuzzy representation can better describe real-life situations than a standard crisp binary representation.A.Semantically Enhanced Web Usage LogsIn the current Web environment, web usage logs (including the web server logs, browser logs and proxy logs) record access requests from users to one or multiple websites as a sequence of requested URLs with timestamps. In this research, the focus is on web server logs. However, the URLs recorded in web usage logs contain little semantic information about the web content accessed by users. This makes it difficult to be used for understanding users actual access behaviors, interests and intentions. Some form of semantic enhancement is therefore required to make the web log data really useful.To overcome this problem, the periodic association access patterns should be filtered to retain only those interesting patterns, which are more important for describing the periodic web access behavior of the user. This is easily done by pruning the periodic association access patterns with low Sup and Conf values. In the experiments, it was found that the thresholds for these value should be about 0.1 - 0.15, depending on the actual application.VI.ConclusionThe authors have proposed an approach for constructing a user behavior knowledge base, which uses fuzzy logic to represent real-life temporal concepts and meaningful resources for periodic pattern-based web access activities. Bother objective and subjective tests have been conducted. The experimental results have shown that the proposed approach can achieve effective periodic web personalization.By performing compute-intensive behavior analysis, modeling and knowledge base construction in advance, future application may include real-time recommendation on portable web-enabled devices that are becoming increasingly popular among consumers of electronics products, e.g. cellular phones that have limited processing power relative to a personal computer would be well suited to this kind of asymmetric approach. In the future, consumers who choose to use this system on their web- enabled mobile devices will be able spend less time and effort searching what they want, but have more of what they are likely to want recommended to them at the appropriate times.References1W. Lee, S. Kang; S. Lim, M.-K. Shin and Y.-K. Kim, “Adaptive hierarchical surrogate for searching web with mobile devices”, IEEE Trans. Consumer Electron., vol. 53, no. 2, pp. 796 - 803, 2007.2Y.B. Fernandez, J.J.P. Arias, M.L. Nores, A.G. Solla and M.R. Cabrer, “AVATAR: an improved solution for personalized TV based on semantic inference,” IEEE Trans. Consumer Electron., vol. 52, no. 1, pp. 223 - 231, 2006.3A. Martinez, J. Arias, A. Vilas, J. Garcia Duque, M. Lopez Nores, “Whats on TV tonight? An efficient and effective personalized recommender system of TV programs,” IEEE Trans. Consumer Electron., vol. 55, no. 1, pp. 286 - 294, 2009.4S. Lee, D. Lee and S. Lee, “Personalized DTV program recommendation system under a cloud computing environment,” IEEE Trans. Consumer Electron., vol. 56, no. 2, pp. 1034 - 1042, 2010.5H. Lee; S. Lee, H. Kim, H. Bahn, “Personalized recommendation schemes for DTV channel selectors”, IEEE Trans. Consumer Electron., vol. 52, no. 3, pp. 1064 - 1068, 2006.6H. Shin, M. Lee and E. Kim, “Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating,” IEEE Trans. Consumer Electron., vol. 55, no. 3, pp. 1417 - 1423, 2009.7S. Park, J. Jeong, H. Jo, J. Lee and E. Seo, “Development of behavior- profilers for multimedia consumer electronics,” IEEE Trans. Consumer Electron., vol. 55, no. 4, pp. 1929 - 1935, 2009.8J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan, “Web usage mining: discovery and applications of usage patterns from web data”, ACM SIGKDD Explorations, 1(2), 2000, pp. 12-23.9J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu, “Mining access patterns efficiently from web logs”，Proc. 4th Pacific-Asia Conf. Knowledge Discovery and Data Mining, Kyoto, Japan, 2000, pp. 396-407.10G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions”, IEEE Trans. Knowl & Data Eng, 17(6), 2005, pp. 734-749.11B. Ozden, S. Ramaswamy, and A. Silberschatz, “Cyclic Association Rules”, In ICDE 98: Proc. 14th International Conference on Data Engineering, pp. 412421, Orlando, Florida, USA, 1998.12S. Ramaswamy, S. Mahajan, and A. Silberschatz, “On the Discovery of interesting patterns in association rules”, In VLDB 98: Proc. 24th International Conference on Very Large Data Bases, pp.368-379, New York, USA, Aug. 1998.13Y. Li, P. Ning, X. S. Wang, and S. Jajodia, “Discovering calendar-based temporal association rules”, In TIME 01: Proc. 8th International Symposium on Temporal Representation and Reasoning, pp. 111-118, Civdale del Friuli, Italy, June 200114Y. Li, P. Ning, X. S. Wang, and S. Jajodia, “Discovering calendar-based temporal association rules”, Data & Knowledge Engineering, 44(2), pp. 193-218, 2003.基于消费者行为建模的网页内容推荐系统摘要对许多消费者而言，网上冲浪已经成为其普遍的活动，不仅可以在网上购物，而且还在购买之前还可寻求有关产品和服务的信息。本文中，作者提出了一种网络推荐系统，它通过使用当时的网络访问模式作为输入来构建知识数据库以模拟用户的习惯和行为。应用模糊逻辑来表示实际当时的概念并请求周期性基于模式的Web访问活动的资源。模糊表示用于构建用户网络访问习惯和行为的知识数据库，用于向用户提供及时的个性化推荐。其所提出的方法适用于为消费者的便携式设备提供建议，因为计算机密集型处理过程是离线的和事先进行的。随着网络消费移动设备的可用性和普及程度越来越高，人们相信未来的CE世界将越来越面向网络。为了评估所提出方法的性能表现而进行的实验已经显示出非常令人满意的结果。索引词 - 消费者行为建模，个性化，网页内容推荐，消费者互联网应用。1引言对消费者而言，网络日益成为其交流或寻找产品和服务想法、意见和经验的的流行媒介。 许多消费者分享的信息比网上信息更实际，并实际上就在网上进行购买。 便携式网络手持设备的可用性和普及度的增加似乎将推动消费者网络流量的进一步增长。在传统的Web信息传播模式中，消费者熟悉通过诸如搜索引擎之类的机制从网络上获取内容，或者只需输入网址链接URI / URL（如果他们碰巧知道它）。 研究人员一直试图使手持移动设备上的网络以使搜索更高效，例如， 1，与个人电脑相比，它的处理能力和屏幕尺寸一般都很有限。2相关工作本节介绍了基于周期性信息的个性化Web内容推荐和消费者Web访问模式挖掘相关工作的调查。 因此，本节为在第三节进一步讨论拟议的方法打下基础。个性化网页内容推荐个性化网络内容推荐旨在最大程度地减少呈现给消费者模糊和不必要的信息，从而降低网民经常遇到的信息过载的影响。 根据文献10中的一项调查，传统的网络内容推荐系统可以分为基于内容的，协作的和混合的，这两者相结合。 但是，这些系统往往严重依赖于用户评分。 无干扰性已被确定为后续网络内容推荐信息收集过程中的重要属性10。 网络使用信息的挖掘，由系统在后台执行，并对用户透明，因此它代表了这一研究领域的重要发展方向。有效的个性化网页内容推荐系统的基本要求是及时向用户提供最相关的建议。因此，情境和时间信息都很重要。在这方面，背景信息在消除模糊信息方面非常有效。例如，想要阅读关于“老鼠”的文章的生物学家可能会对啮齿动物感兴趣。另一方面，寻找电脑配件的消费者可能对定点设备感兴趣。前后的信息将有助于解决此类模糊的信息。目前一些系统往往缺乏对时间信息的关注。相关资源建议的及时性在许多情况下也很重要。例如，用户可能倾向于在平日上午9点至上午10点（甚至更具体地在星期一和星期二）阅读金融新闻和交通信息，但可能倾向于在周六的相同时间段内阅读娱乐新闻和天气信息。3消费者行为信息数据库构建用户行为信息数据库的过程始于语义增强的Web使用日志作为输入。然后对原始日志数据进行预处理以去除不必要的信息并提高后续处理的数据质量。最后，可以用模糊关系作为基础来构建信息数据库。正如第一部分提到的，模糊表示可以比标准的简洁二进制表示能更好地描述真实情况。A.语义增强的Web使用情况日志在当前的Web环境中，Web使用情况日志（包括Web服务器日志，浏览器日志和代理日志）将用户访问一个或多个网站的访问请求记录为具有时间戳的所请求的URL序列。在这项研究中，重点是Web服务器日志。但是，Web使用日志中记录的URL包含的关于用户访问的Web内容的语义信息很少。这对理解用户的实际访问行为、兴趣和意图变得很难。因此，需要某种形式的语义增强功能才能使Web日志数据真正有用。B.信息数据库的建设上述语义增强的Web使用日志现在可被用作消费者行为信息数据库构建过程的输入。图1总结了知识库构建过程中进一步处理的四个关键步骤。针对传统（非语义增强）Web服务器日志中19中讨论的类似预处理任务、数据清理、用户标识和会话标识进行了修改。特别是，在输入的Web使用情况日志中，并非所有记录对于挖掘目的都是必需的或有用的。根据所选资源属性集合，根据“语义标注”字段中的主题，清除不必要的条目非常重要。当输入日志中的条目涉及至少一个其他所有记录中的资源属性时，将从Web使用日志中丢弃，其中包括图片文件、脚本和其他无效文档。此外，包含不成功请求条目的网页使用日志也被丢弃。为了克服这个问题，应该对周期性关联访问模式进行过滤，以仅保留那些有趣的模式，这对于描述用户的周期性Web访问行为更为重要。 这很容易通过修剪具有低Sup和Conf值的周期性关联访问模式来完成。 在实验中，发现这些值的阈值范围应为约0.1-0.15，这取决于其实际应用。4 结论在本文中，作者提出了一种构建用户行为知识数据库的方法，该方法使用模糊逻辑来表示基于周期性模式的Web访问活动的真实时间概念及有意义的资源。并进行了客观和主观性的测试。实验结果表明，该方法可以实现网络个性化推荐有效性和周期性。通过预先进行计算密集型行为的分析、建模和知识库的构建，未来的应用可以包括对在电子产品的消费者中变得越来越流行的便携式网络使能设备进行实时推荐，例如，具有相对于个人计算机的处理能力有限的蜂窝电话而言，它将非常适合这种非对称方法。未来，选择在其网络移动设备上使用此系统的消费者将能够花费更少的时间和精力来搜索他们想要的内容，并可以在适当的时候获得更多可能需要推荐给他们的内容。参考文献1W. Lee, S. Kang; S. Lim, M.-K. Shin and Y.-K. Kim, “Adaptive hierarchical surrogate for searching web with mobile devices”, IEEE Trans. Consumer Electron., vol. 53, no. 2, pp. 796 - 803, 2007.2Y.B. Fernandez, J.J.P. Arias, M.L. Nores, A.G. Solla and M.R. Cabrer, “AVATAR: an improved solution for personalized TV based on semantic inference,” IEEE Trans. Consumer Electron., vol. 52, no. 1, pp. 223 - 231, 2006.3A. Martinez, J. Arias, A. Vilas, J. Garcia Duque, M. Lopez Nores, “Whats on TV tonight? An efficient and effective personalized recommender system of TV programs,” IEEE Trans. Consumer Electron., vol. 55, no. 1, pp. 286 - 294, 2009.4S. Lee, D. Lee and S. Lee, “Personalized DTV program recommendation system under a cloud computing environment,” IEEE Trans. Consumer Electron., vol. 56, no. 2, pp. 1034 - 1042, 2010.5H. Lee; S. Lee, H. Kim, H. Bahn, “Personalized recommendation schemes for DTV channel selectors”, IEEE Trans. Consumer Electron., vol. 52, no. 3, pp. 1064 - 1068, 2006.6H. Shin, M. Lee and E. Kim, “Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating,” IEEE Trans. Consumer Electron., vol. 55, no. 3, pp. 1417 - 1423, 2009.7S. Park, J. Jeong, H. Jo, J. Lee and E. Seo, “Develop