Personalized recommendation of popular blog articles for mobile applications

Weblogs have emerged as a new communication and publication medium on the Internet for diffusing the latest useful information. Providing value-added mobile services, such as blog articles, is increasingly important to attract mobile users to mobile commerce, in order to benefit from the proliferation and convenience of using mobile devices to receive information any time and anywhere. However, there are a tremendous number of blog articles, and mobile users generally have difficulty in browsing weblogs owing to the limitations of mobile devices. Accordingly, providing mobile users with blog articles that suit their particular interests is an important issue. Very little research, however, has focused on this issue.In this work, we propose a novel Customized Content Service on a mobile device (m-CCS) to filter and push blog articles to mobile users. The m-CCS includes a novel forecasting approach to predict the latest popular blog topics based on the trend of time-sensitive popularity of weblogs. Mobile users may, however, have different interests regarding the latest popular blog topics. Thus, the m-CCS further analyzes the mobile users’ browsing logs to determine their interests, which are then combined with the latest popular blog topics to derive their preferred blog topics and articles. A novel hybrid approach is proposed to recommend blog articles by integrating personalized popularity of topic clusters, item-based collaborative filtering (CF) and attention degree (click times) of blog articles. The experiment result demonstrates that the m-CCS system can effectively recommend mobile users’ desired blog articles with respect to both popularity and personal interests.     

在线教育平台中个性化学习资源推荐系统设计

为了通过充分挖掘和分析用户的学习行为规律及认知特点,借助互联网和人工智能技术提升个性化教育的深度和广度,设计了一个包含用户画像的个性化学习资源推荐系统.该系统由数据层、数据分析层和推荐计算层构成.数据层由用户数据以及包含知识资料、学习资料和标签集的资源库组成;数据分析层融合了以基础信息、学习行为等为代表的静态数据和动态...     

Consumer attitudes toward blogger’s sponsored recommendations and purchase intention: The effect of sponsorship type,product type,and brand awareness

Sponsored recommendation blog posts, a form of online consumer review, are blog articles written by bloggers who receive benefits from sponsoring marketers to review and promote products on their personal blog. Because national regulations require that marketer sponsorship must be revealed in the blog post, sponsored recommendation posts can no longer conceal their marketing intent. Consumer’s attitudes toward sponsored recommendation posts are thus a vital issue in assessing the effectiveness of the advertisement. This study uses a 2(sponsorship type) × 2(product type) × 2(brand awareness) experimental design and a total of 613 valid samples to examine consumer attitudes toward sponsored recommendation posts and purchase intention. The results show that when products recommended in blog posts are search goods or have high brand awareness, consumers have highly positive attitudes toward sponsored recommendation posts, which improves purchase intention. The directly-monetary/indirect-monetary benefits received by the bloggers have no significant effect on readership attitudes. Using these features in blog writings appears to improve online readers’ trust toward and the credibility of sponsored recommendation posts and thus can be a vital online marketing tool for marketers.     

Interactive resource recommendation algorithm based on tag information

With the popularization of social media and the exponential growth of information generated by online users, the recommender system has been popular in helping users to find the desired resources from vast amounts of data. However, the cold-start problem is one of the major challenges for personalized recommendation. In this work, we utilized the tag information associated with different resources, and proposed a tag-based interactive framework to make the resource recommendation for different users. During the interaction, the most effective tag information will be selected for users to choose, and the approach considers the users’ feedback to dynamically adjusts the recommended candidates during the recommendation process. Furthermore, to effectively explore the user preference and resource characteristics, we analyzed the tag information of different resources to represent the user and resource features, considering the users’ personal operations and time factor, based on which we can identify the similar users and resource items. Probabilistic matrix factorization is employed in our work to overcome the rating sparsity, which is enhanced by embedding the similar user and resource information. The experiments on real-world datasets demonstrate that the proposed algorithm can get more accurate predictions and higher recommendation efficiency.     

面向移动终端的微博信息推荐方法

微型博客(简称“微博')以其简洁方便的交互方式,受到越来越多手机用户的喜爱。然而,微博数据量大、更新速度快以及手机屏幕小、登录网络服务速度较慢等原因,使得用户很难通过移动终端快速了解到近期内微博流行内容。提出一种基于相关主题模型(correlated topic model)的移动微博信息推荐方法,并基于此方法设计了一个可视化移动信息推荐系统。通过‘用户一主题一词语’三维关联矩阵的建立,帮助用户快速了解最近一段时间内的热点主题,并查找与其感兴趣主题相关的其他用户作为备选好友,同时计算主题之间的关联关系,进行主题扩展。在微博代表性网站—Fricndfccd数据集上进行的实验表明了该方法在移动微博信息推荐中的简洁性和有效性。     

User comments for news recommendation in forum-based social media

News recommendation and user interaction are important features in many Web-based news services. The former helps users identify the most relevant news for further information. The latter enables collaborated information sharing among users with their comments following news postings. This research is intended to marry these two features together for an adaptive recommender system that utilizes reader comments to refine the recommendation of news in accordance with the evolving topic. This then turns the traditional “push-data” type of news recommendation to “discussion” moderator that can intelligently assist online forums. In addition, to alleviate the problem of recommending essentially identical articles, the relationship (duplicate, generalization, or specialization) between recommended news articles and the original posting is investigated. Our experiments indicate that our proposed solutions provide an improved news recommendation service in forum-based social media.     

基于协同过滤的水果推荐系统设计与实现

随着大数据技术的飞速发展,从大量的信息中如何让用户发现和挖掘出有价值的信息,一直是人们研究的热点问题。推荐系统的发展起到了关键作用,主要是发现用户和商品之间的信息,一方面为用户找到有价值的信息,另一方面为用户推荐感兴趣的商品,从而实现了用户和信息生成者的共赢。基于协同过滤的水果推荐系统通过分析用户的历史行为了解用户的喜好,在为用户提供其感兴趣的信息的同时,也能够实现个性化的推荐。     

MONERS: A news recommender for the mobile web

Mobile web news services, which served by mobile service operators collecting news articles from diverse news contents providers, provide articles sorted by category or on the basis of attributes, such as the time at which they were posted. The mobile web should provide easy access to the categories or news contents preferred by users because user interface of wireless devices, particularly cell phones is limited for browsing between contents.This paper presents a mobile web news recommendation system (MONERS) that incorporates news article attributes and user preferences with regard to categories and news articles. User preference of news articles are estimated by aggregating news article importance and recency, user preference change, and user segment’s preference on news categories and articles. Performance of MONERS was tested in an actual mobile web environment; news organized by category had more page hits, while recommended news had a higher overall article read ratio.     

A recommender agent based on learning styles for better virtual collaborative learning experiences

Almost unlimited access to educational information plethora came with a drawback: finding meaningful material is not a straightforward task anymore. Based on a survey related to how students find additional bibliographical resources for university courses, we concluded there is a strong need for recommended learning materials, for specialized online search and for personalized learning tools. As a result, we developed an educational collaborative filtering recommender agent, with an integrated learning style finder. The agent produces two types of recommendations: suggestions and shortcuts for learning materials and learning tools, helping the learner to better navigate through educational resources. Shortcuts are created taking into account only the user’s profile, while suggestions are created using the choices made by the learners with similar learning styles. The learning style finder assigns to each user a profile model, taking into account an index of learning styles, as well as patterns discovered in the virtual behavior of the user. The current study presents the agent itself, as well as its integration to a virtual collaborative learning environment and its success and limitations, based on users’ feedback.     

PENETRATE: Personalized news recommendation using ensemble hierarchical clustering

Recommending online news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Many online readers have their own reading preference on news articles; however, a group of users might be interested in similar fascinating topics. It would be helpful to take into consideration the individual and group reading behavior simultaneously when recommending news items to online users. In this paper, we propose PENETRATE, a novel PErsonalized NEws recommendaTion framework using ensemble hieRArchical clusTEring to provide attractive recommendation results. Specifically, given a set of online readers, our approach initially separates readers into different groups based on their reading histories, where each user might be designated to several groups. Once a collection of newly-published news items is provided, we can easily construct a news hierarchy for each user group. When recommending news articles to a given user, the hierarchies of multiple user groups that the user belongs to are merged into an optimal one. Finally a list of news articles are selected from this optimal hierarchy based on the user’s personalized information, as the recommendation result. Extensive empirical experiments on a set of news articles collected from various popular news websites demonstrate the efficacy of our proposed approach.     

垂直学习社区基于学习兴趣与风格的社会化推荐算法

垂直学习社区包含了海量的学习资源,出现了信息过载现象,个性化推荐是解决这个难题的方法之一.但垂直学习社区中评分数据稀疏而文本、社交信息丰富,传统的协同过滤推荐算法不完全适用.基于用户产生的文本和行为信息,利用作者主题模型构建新的用户学习兴趣相似度衡量模型;根据用户交互行为信息综合考虑信任与不信任因素构建用户全面信任关系计算全面信任度;通过分析用户多维度学习行为模式,自动识别用户学习风格;最后提出融合兴趣相似度、全面信任度及学习风格的社会化推荐算法.用垂直学习社区网站CSDN实际数据集进行了实验分析.结果表明本文提出的推荐方法能更好向用户推荐其感兴趣的学习资源,有效地提高了推荐精度,进而提高用户学习效果.     

社交网络中融合社交关系和语义信息的推荐算法

协同过滤方法广泛应用于推荐,但是数据稀疏成为模型提供高质量推荐的一大障碍.为了解决此问题,文中提出融合社交关系和语义信息的推荐算法,提高协同过滤方法的推荐性能,有机融合稀疏的用户行为记录、项目的社交信息和项目的语义信息.应用矩阵分解技术把行为矩阵和项目社交关系映射到一个低维的特征空间,提供项目社交关系信息分解的显式解释,分析关系信息对用户行为偏好产生的影响.同时,使用社会化因子正则的级联去噪自编码器模型学习项目语义特征,改进传统深度学习模型.在真实腾讯微博和Twitter数据集上的实验表明,文中方法有效提高召回率、准确率和推荐效率.     

一种面向获取空间信息的潜在好友推荐算法

随着社交网络的不断发展,朋友推荐已成为各大社交网络的青睐对象,在能够帮助用户拓宽社交圈的同时可以通过新朋友获取大量信息.由此朋友推荐应该着眼于拓宽社交圈和获取信息,然而传统的朋友推荐算法几乎没有考虑从获取信息的角度为用户推荐潜在好友,大多是依赖于用户在线的个人资料和共同的物理空间中的签到信息.而由于人们活动具有空间局部性,被推荐的好友分布在用户了解的地理空间,并不能够满足用户通过推荐的朋友获取更多地理信息的需求.本文采用用户在物理世界中的签到行为代替虚拟社交网络中的用户资料,挖掘真实世界中用户之间的签到行为的相似性,为用户推荐具有相似的签到行为且地理位置分布更广泛的陌生人,能够增加用户接受被推荐的陌生人成为朋友的可能性和在保证一定的推荐精度的基础上增加用户的信息获取量.本文采用核密度估计估算用户签到行为概率分布,用时间熵度量签到行为在时间上的集中程度,选择可以为用户带来更多新的地理信息的陌生人作为推荐的对象,通过大规模Foursquare的用户签到数据集验证本文的算法在精度上保证了与目前已有LBSN上陌生人推荐算法的相似性,在信息扩大程度上高于上述已有算法.     

Communication Communities or “CyberGhettos?”: A Path Analysis Model Examining Factors that Explain Selective Exposure to Blogs1

This study used an online panel of Internet users to examine the degree to which blog users practice selective exposure when seeking political information. The research employed a path analysis model to explore the extent to which exposure to offline and online discussion of political issues, and offline and online media use, as well as political variables and demographic factors, predict an individual's likelihood to engage in selective exposure to blogs. The findings indicate that respondents did practice selective exposure to blogs, predominantly those who are heavy blog users, politically active both online and offline, partisan, and highly educated.     

协同过滤推荐系统综述

随着互联网和信息计算的飞速发展,衍生了海量数据,我们已经进入信息爆炸的时代。网络中各种信息量的指数型增长导致用户想要从大量信息中找到自己需要的信息变得越来越困难,信息过载问题日益突出。推荐系统在缓解信息过载问题中起着非常重要的作用,该方法通过研究用户的兴趣偏好进行个性化计算,由系统发现用户兴趣进而引导用户发现自己的信息需求。目前,推荐系统已经成为产业界和学术界关注、研究的热点问题,应用领域十分广泛。在电子商务、会话推荐、文章推荐、智慧医疗等多个领域都有所应用。传统的推荐算法主要包括基于内容的推荐、协同过滤推荐以及混合推荐。其中,协同过滤推荐是推荐系统中应用最广泛最成功的技术之一。该方法利用用户或物品间的相似度以及历史行为数据对目标用户进行推荐,因此存在用户冷启动和项目冷启动问题。此外,随着信息量的急剧增长,传统协同过滤推荐系统面对数据的快速增长会遇到严重的数据稀疏性问题以及可扩展性问题。为了缓解甚至解决这些问题,推荐系统研究人员进行了大量的工作。近年来,为了提高推荐效果、提升用户满意度,学者们开始关注推荐系统的多样性问题以及可解释性等问题。由于深度学习方法可以通过发现数据中用户和项目之间的非线性关系从而学习一个有效的特征表示,因此越来越受到推荐系统研究人员的关注。目前的工作主要是利用评分数据、社交网络信息以及其他领域信息等辅助信息,结合深度学习、数据挖掘等技术提高推荐效果、提升用户满意度。对此,本文首先对推荐系统以及传统推荐算法进行概述,然后重点介绍协同过滤推荐算法的相关工作。包括协同过滤推荐算法的任务、评价指标、常用数据集以及学者们在解决协同过滤算法存在的问题时所做的工作以及努力。最后提出未来的几个可研究方向。     

对话推荐算法研究综述

推荐系统是一种通过理解用户的兴趣和偏好帮助用户过滤大量无效信息并获取感兴趣的信息或者物品的信息过滤系统.目前主流的推荐系统主要基于离线的、历史的用户数据,不断训练和优化线下模型,继而为在线的用户推荐物品,这类训练方式主要存在3个问题:基于稀疏且具有噪声的历史数据估计用户偏好的不可靠估计、对影响用户行为的在线上下文环境因素的忽略和默认用户清楚自身偏好的不可靠假设.由于对话系统关注于用户的实时反馈数据,获取用户当前交互的意图,因此“对话推荐”通过结合对话形式与推荐任务成为解决传统推荐问题的有效手段.对话推荐将对话系统实时交互的数据获取方式应用到推荐系统中,采用了与传统推荐系统不同的推荐思路,通过利用在线交互信息,引导和捕捉用户当前的偏好兴趣,并及时进行反馈和更新.在过去的几年里,越来越多的研究者开始关注对话推荐系统,这一方面归功于自然语言处理领域中语音助手以及聊天机器人技术的广泛使用,另一方面受益于强化学习、知识图谱等技术在推荐策略中的成熟应用.将对话推荐系统的整体框架进行梳理,将对话推荐算法研究所使用的数据集进行分类,同时对评价对话推荐效果的相关指标进行讨论,重点关注于对话推荐系统中的后...     

A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.     

Personalized Tag Recommendation Using Social Influence

Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object,which provides a feasible solution for content-based multimedia information retrieval.In this paper,we study personalized tag recommendation in a popular online photo sharing site - Flickr.Social relationship information of users is collected to generate an online social network.From the perspective of network topology,we propose node topological potential to characterize user’s social influence.With this metric,we distinguish different social relations between users and find out those who really have influence on the target users.Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user’s social network.We evaluate our method on large scale real-world data.The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks.We also analyze the further usage of our approach for the cold-start problem of tag recommendation.     

一种潜在特征同步学习和偏好引导的推荐方法

根据用户的历史评分数据为用户提供推荐的商品列表，是目前推荐系统研究的主流.研究者发现，随着用户参与度的不断提高，将反映用户偏好的评论文本与评分数据结合，可以进一步提高推荐的质量.提出了基于潜在特征同步学习和偏好引导的商品推荐方法，将评论文本的主题与用户的"打分偏好"进行关联，同步学习用户评论文本的潜在主题、评分矩阵的用户潜在因子和商品潜在因子，并将潜在主题作为用户个人偏好引导来约束推荐方法对商品的预测打分.该方法对推荐质量的优化主要体现在两个方面：一是在评论文本的潜在主题和评分数据的两种潜在因子之间建立映射关系，同步求解主题模型和矩阵分解模型；二是将从评论文本中学习得到的潜在主题作为用户对商品的个性偏好引入到矩阵分解中，进一步优化推荐方法.在来自Amazon网站的28组真实数据集上进行实验，以均方误差为评价指标，与已有的模型进行了对比分析.实验结果表明，该方法有效减少了推荐误差，与已有的TopicMF方法相比，均方误差在数据子集上最大减少了3.32%，平均减少了0.92%.     

Modeling and broadening temporal user interest in personalized news recommendation

User profiling is an important step for solving the problem of personalized news recommendation. Traditional user profiling techniques often construct profiles of users based on static historical data accessed by users. However, due to the frequent updating of news repository, it is possible that a user’s fine-grained reading preference would evolve over time while his/her long-term interest remains stable. Therefore, it is imperative to reason on such preference evaluation for user profiling in news recommenders. Besides, in content-based news recommenders, a user’s preference tends to be stable due to the mechanism of selecting similar content-wise news articles with respect to the user’s profile. To activate users’ reading motivations, a successful recommender needs to introduce “somewhat novel” articles to users.In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that a user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen as the recommended candidates based on the short-term user profile. We further propose to select news items from the user–item affinity graph using absorbing random walk model to increase the diversity of the recommended news list. Extensive empirical experiments on a collection of news data obtained from various popular news websites demonstrate the effectiveness of our method.