Utilizing contextual ontological user profiles for personalized recommendations

As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.     

新的基于日志分析的用户个性化模型

作为个性化服务技术的核心,用户模型的质量关系到个性化服务的质量。目前的用户模型大多只考虑用户的显式信息或隐式信息,很少同时考虑两者,使得检索质量不如人意。提出了一种新的基于日志分析的用户个性化模型,结合了传统的显式建模和隐式建模的优点,把显式个性化信息和隐式个性化信息通过两层树状结构结合起来。模型同时考虑了用户历史信息的长短期划分,以及检索系统返回结果的顺序和用户对结果页面的点击顺序。实验结果表明,基于该用户模型的个性化检索效果与原有检索系统的检索效果相比有显著提高。     

User Modeling for Personalized City Tours

Several current support systems for travel and tourism are aimed at providing information in a personalized manner, taking users' interests and preferences into account. In this vein, personalized systems observe users' behavior and, based thereon, make generalizations and predictions about them. This article describes a user modeling server that offers services to personalized systems with regard to the analysis of user actions, the representation of assumptions about the user, and the inference of additional assumptions based on domain knowledge and characteristics of similar users. The system is open and compliant with major standards, allowing it to be easily accessed by clients that need personalization services.     

Collaborative Personalization of Image Enhancement

This paper presents methods for personalization of image enhancement, which could be deployed in photo editing software and also in cloud-based image sharing services. We observe that users do have different preferences for enhancing images and that there are groups of people that share similarities in preferences. Our goal is to predict enhancements for novel images belonging to a particular user based on her specific taste, to facilitate the retouching process on large image collections. To that end, we describe an enhancement framework that can learn user preferences in an individual or collaborative way. The proposed system is based on a novel interactive application that allows to collect user’s enhancement preferences. We propose algorithms to predict personalized enhancements by learning a preference model from the provided information. Furthermore, the algorithm improves prediction performance as more enhancement examples are progressively added. We conducted experiments via Amazon Mechanical Turk to collect preferences from a large group of people. Results show that the proposed framework can suggest image enhancements more targeted to individual users than commercial tools with global auto-enhancement functionalities.     

个性化服务技术研究

对个性化服务技术中的用户识别、用户描述文件、个性化推荐技术、个性化服务系统的体系结构及目前的研究方向进行了概述。从实现角度详细讨论了3种个性化推荐技术。个性化服务具有针对性,它的目的就是为了使用户更好地找到需要的信息,通过从用户访问网站的历史记录中得到用户的个人信息,利用个性化推荐的方法将信息推荐给用户。个性化推荐避免用户陷入信息的海洋,提高用户查询效率,使得用户可以得到他们真正想得到的信息,避免繁多的人工搜索。     

一种个性化查询扩展方法

针对当前主流web搜索引擎存在信息检索个性化效果差和信息检索的精确率低等缺点, 通过对已有方法的技术改进, 介绍了一种基于用户历史兴趣网页和历史查询词相结合的个性化查询扩展方法。当用户在搜索引擎上输入查询词时,能根据学习到的当前用户兴趣模型动态判定用户潜在兴趣和计算词间相关度,并将恰当的扩展查询词组提交给搜索引擎,从而实现不同用户输入同一查询词能返回不同检索结果的目的。实验验证了算法的有效性,检索精确率也比原方法有明显提高。     

一种基于用户行为的兴趣度模型

个性化推荐技术在电子商务系统中得到了广泛应用。针对现有的用户模型不能根据用户自身兴趣实现推荐的问题,提出了一种基于用户行为的兴趣度模型,分析用户的行为模式,结合用户的浏览内容,发现用户兴趣。在此基础上采用期望最大化算法实现用户聚类,将用户划分到对应的簇,创建用户的兴趣度模型,从而向用户进行个性化推荐。实验对比结果表明,该模型能更好地发现用户当前的购买兴趣,从而进一步提高个性化推荐精度和用户满意度。     

个性化高效元搜索引擎的设计与实现

介绍了一个高效的元搜索引擎系统SMS(Smart Meta Searcher),采用检索实例知识库对用户的检索意图进行推理,同时给出一套独特的星级排行评价策略,通过用户行为分析技术为用户提供个性化信息检索服务,以及其在未来搜索引擎个性化、智能化、专业化和多媒体搜索的发展方向所做的探索工作。     

人体经络系统中的个性化信息服务研究

人体经络较为复杂,涉及到经络、穴位、疾病、脏腑和针灸等方面的知识,容易导致＂信息迷航＂,且对于不同的用户,信息需求也各不相同。针对该问题,文章系统地研究了人体经络系统中的个性化信息服务,构建了用户兴趣模型,并根据用户对场景的访问次数和停留时间来更新用户的兴趣模型,在此基础上提出基于相似用户兴趣的个性化推荐算法,从而实现为用户提供个性化的信息服务。实验结果表明,系统能根据用户信息及其交互行为,有效地推荐与用户兴趣相关的信息,较好地为用户提供个性化的信息服务。     

Personalization and Context-awareness in Social Local Search: State-of-the-art and Future Research Challenges

Location-based services (LBS) are now the platforms for aggregating relevant information about users and understanding their mobile behavior and preferences based on the location histories. The increasing availability of large amounts of spatio-temporal data brings us opportunities and challenges to automatically discover valuable knowledge. While context-aware properties quickly became the key of the success of these pervasive applications, information related to user preferences and social signals still lack of adequate capitalization. Local search in LBSs is a peculiar service where recent and current interests, the network of explicit and implicit social interactions between users can be combined for effectively performing fine-tuned and personalized recommendations of points of interest. In this article we present the various and peculiar aspects of local search in mobile scenarios. Then we explore the added value of personalization and the benefits of considering social signals, summarizing open challenges and emerging technologies.     

基于情景上下文与信任关系的旅游景点推荐算法*

针对现有旅游景点推荐个性化的不足问题,本文提出了一种基于信任关系与于情景上下文的旅游景点推荐算法。首先在传统的协同过滤算法上以用户信任度代替相似度来解决数据稀疏性;其次引入用户情景上下文信息,更全面的反映出用户的个性化需求;最后基于用户的信任度和上下文信息优化,建立一个推荐结果准确度更高的旅游景点推荐模型。模拟实验结果表明,综合考虑信任度和情景上下文信息的推荐策略表现最优。     

Digital content recommender on the Internet

On the Web, where information is vast and users are numerous, personalization that aims to offer suitable information to suitable users is essential. To sustain their competitive advantage, portal sites attract many users' attention by supplying personalized content. Most Web content providers offer all users the same content, failing to satisfy individual users' needs. Providers should be able to offer suitable users suitable content with suitable speed. To do so, they must be able to identify customers, predict their interests, determine appropriate content, and deliver it in a personalized format during customers' online sessions. In this paper, the author presents a digital-content recommender system that suggests Web content, in this case news articles, based on a user's preference when he or she visits an Internet news site and reads the published articles. This recommender system creates a one-to-one relationship between the content provider and the user, raises the user's satisfaction, and increases loyalty toward the content provider.     

基于LBSN的个性化旅游包推荐系统

旅游是人们生活中的重要部分,但是制定旅游计划是一件繁杂的工作。基于位置的社交网络(LBSN)的发展,提供了大量关于位置和活动的信息。为辅助用户制订旅游计划,本文提出一个基于LBSN的个性化旅游包推荐系统。该系统利用采集自LBSN的数据,建立地点和用户偏好模型,根据用户需求,在时空约束下生成旅游路线,形成旅游包推荐给用户。本文实现的原型系统能交互地获取用户旅游意向,实时生成多个旅游包供用户选择,对游客制订旅游计划具有一定的参考价值。     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.     

基于TF-IDF分类算法的雷达情报分发技术

为了提高情报分发的效率,解决雷达组网上信息过载的问题,提出了一种利用个性化推荐技术过滤情报用户感兴趣的情报信息的技术.根据情报用户兴趣多样性的特点和雷达情报的格式化特征,对情报用户兴趣的类别进行划分,并设计出基于层次向量空间模型;在此基础上,利用用户的历史情报信息和定制信息,运用TF-IDF算法挖掘用户兴趣,建立用户兴趣模型,通过实时情报与用户兴趣模型的匹配,将用户感兴趣的情报分发给用户.仿真实验结果表明,该算法能够较好地实现雷达情报的按需分发.     

基于用户兴趣的PageRank算法改进策略

随着互联网海量信息的不断涌现,根据用户的兴趣提供相关查询结果,是现有搜索引擎要考虑的一个问题,PageRank算法是基于链接的排序算法,已在Google搜索引擎广泛应用,但其忽略了用户个性化需求。采用网页预分类技术,来表示用户查询的兴趣度,进一步提出改进传统的PageRank算法,从而能适当提高用户在使用搜索引擎方面的个性化需求。     

新客户在商务站点中的个性化推荐

提出了一种针对新客户在商务站点购物的个性化推荐方法。首先利用已购物客户的浏览信息生成购物行为模型,得到新客户在站点中的浏览行为生成浏览行为模型,通过最近邻居的协同过滤技术生成与新客户行为最为相近的用户集,将最近邻居已购商品推荐给新客户。该方法能够给新客户提供及时准确的个性化商品信息。     

基于用户反馈的个性化搜索引擎的研究

现有搜索引擎基本上采用＂搜索适用所有用户＂的模型,体现不出用户真正的兴趣所在。针对当前搜索引擎的不足,本文提出并研究一个基于用户反馈的个性化搜索引擎系统。通过学习用户满意度反馈信息,挖掘隐藏的用户兴趣信息,实现搜索引擎的个性化。     

Using ubiquitous computing in interactive mobile marketing

Unique features of handheld devices, including their mobility, personalization and location-awareness engender new types of applications for mobile commerce, such as mobile advertising. Mobile marketing and advertising applications deliver promotional information to consumers based on their preferences and location. In this paper, we present SMMART, a context-aware, adaptive and personalized m-commerce application designed to deliver targeted promotions to the users of mobile devices about the products they like while guarding the users’ identity and protecting them from any unsolicited messages. Promotions distributed by SMMART are personalized by performing intelligent matching of the user’s shopping interests to current promotions available at a retail site. SMMART can adapt to changing preferences of its user by inconspicuously monitoring his or her shopping habits. We describe a fully functional prototype of SMMART built for Pocket PCs running Windows CE with .NET Compact Framework. This paper also presents a study demonstrating end-user usability and economic viability of SMMART.     

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.