Mining tag-clouds to improve social media recommendation

Multimedia Tools and Applications - Massive amounts of data are available on social websites, therefore finding the suitable item is a challenging issue. According to recent social statistics, we...     

基于社会化媒体情境的多维协同智能推荐

针对传统协同智能推荐技术的冷启动、数据稀缺性问题,为提高推荐算法的效率和准确性,提出一种基于社会化媒体情境的多维智能推荐算法模型。该模型将目标用户的属性特征、行为特征考虑到社会化媒体情境信息中,并动态实时捕捉用户在不同社会化媒体情境下的偏好倾向,利用联机分析处理(OLAP)技术对多维数据进行处理。该模型将用户间的社会化关系和所处的政治经济环境视为衡量用户相似的重要指标,同时使用皮尔森系数和云模型来计算用户间各特征的相似度,并以此为推荐基础向用户呈现更个性化和定制化的推荐结果。实验结果表明,该模型的推荐结果的平均绝对误差明显小于传统的协同智能推荐和单纯的基于云模型推荐技术。     

融合社交信息的局部潜在空间推荐方法

随着社交网络的发展,越来越多的研究利用社交信息来改进传统推荐算法的性能,然而现有的推荐算法大多忽略了用户兴趣的多样化,未考虑用户在不同社交维度中关心的层面不同,导致推荐质量较差.为了解决这个问题,提出了一种同时考虑全局潜在因子和不同子集特定潜在因子的推荐方法LSFS,使得推荐过程既考虑了用户共享偏好又考虑了用户在不同子...     

基于重要性的信任感知推荐方法

针对基于信任模型的推荐系统认为所有项目对所有用户具有相同重要性的问题,提出一种基于重要性的信任感知推荐方法.基于项目重要性对所有用户不一样的假设提出了新的信任度量,利用相对于用户的人口统计上下文来测量项目对于用户的重要性,计算活动用户和每个集群的人口统计特征的相似性,将最相似集群中的用户视为候选邻居,根据活动用户的信任邻居的偏好为其生成一个推荐列表.实验结果表明,提出方法在大多数情况下都优于其它方法,具有较高的预测精度和质量.     

社交媒体中的信息推荐

近年来社交媒体越来越流行,可以从中获得大量丰富多彩的信息的同时,也带来了严重的"信息过载"问题.推荐系统作为缓解信息过载最有效的方法之一,在社交媒体中的作用日趋重要.区别于传统的推荐方法,社交媒体中包含大量的用户产生内容,因此在社交媒体中,通过结合传统的个性化的推荐方法,集成各类新的数据、元数据和清晰的用户关系,产生了各种新的推荐技术.总结了社交推荐系统中的几个关键研究领域,包括基于社会化标注的推荐、组推荐和基于信任的推荐,之后介绍了在信息推荐中考虑时间因素时的情况,最后对社交媒体中信息推荐有待深入研究的难点和发展趋势进行了展望.     

Exploring sharing patterns for video recommendation on YouTube-like social media

YouTube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Face-book-like online social networks (OSNs) have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSS videos are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users’ viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends have higher common interest and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for video recommendation. An autoencoder model is developed to learn the social similarity of different videos in terms of their sharing in OSNs. We, therefore, propose a similarity-based strategy to enhance video recommendation for YouTube-like social media. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.     

A movie recommendation algorithm based on genre correlations

Since the late 20th century, the number of Internet users has increased dramatically, as has the number of Web searches performed on a daily basis and the amount of information available. A huge amount of new information is transferred to the Web on a daily basis. However, not all data are reliable and valuable, which implies that it may become more and more difficult to obtain satisfactory results from Web searches. We often iterate searches several times to find what we are looking for. To solve this problem, researchers have suggested the use of recommendation systems. Instead of searching for the same information several times, a recommendation system proposes relevant information. In the Web 2.0 era, recommendation systems often rely on collaborative filtering by users. In general, a collaborative filtering approach based on user information such as gender, location, or preference is effective. However, the traditional approach can fail due to the cold-start problem or the sparsity problem, because initial user information is required for this approach to be effective. Recently, several attempts have been made to tackle these collaborative filtering problems. One such attempt used category correlations of contents. For instance, a movie has genre information provided by movie experts and directors. This category information is more reliable than user ratings. Moreover, newly created content always has category information, allowing avoidance of the cold-start problem. In this study, we consider a movie recommendation system and improve the previous algorithms based on genre correlations to correct its shortcomings. We also test the modified algorithm and analyze the results with respect to two characteristics of genre correlations.     

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.     

Preference dynamics with multimodal user-item interactions in social media recommendation

Recommender systems elicit the interests and preferences of individuals and make recommendations accordingly, a main challenge for expert and intelligent systems. An essential problem in recommender systems is to learn users’ preference dynamics, that is, the constant evolution of the explicit or the implicit information, which is diversified throughout time according to the user actions. Also, in real settings data sparsity degrades the recommendation accuracy. Hence, state-of-the-art methods exploit multimodal information of users-item interactions to reduce sparsity, but they ignore preference dynamics and do not capture users’ most recent preferences. In this article, we present a Temporal Collective Matrix Factorization (TCMF) model, making the following contributions: (i) we capture preference dynamics through a joint decomposition model that extracts the user temporal patterns, and (ii) co-factorize the temporal patterns with multimodal user-item interactions by minimizing a joint objective function to generate the recommendations. We evaluate the performance of TCMF in terms of accuracy and root mean square error, and show that the proposed model significantly outperforms state-of-the-art strategies.     

Exploring context and content links in social media: a latent space method

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.     

A multi-label ensemble predicting model to service recommendation from social media contents

The Journal of Supercomputing - Consumer sentiment is one of the essential measures of predictive recommendations in travel and tourism. Nowadays, a massive amount of data is available on the...     

A social recommendation method based on opinion leaders

With the rapid development of information technology, social media has been widely used, and Internet information has been exploded, and consumers may experience information overload. Recommender systems using the social recommendation method that integrates social relationship information can provide users with target information that meets their needs. However, most of the existing methods only rely on the user’s ordinary friends to make recommendations, neglecting another influential group, the opinion leaders. In this study, we propose a new social recommendation method based on opinion leaders. The proposed method assumes that the influence of the opinion leader on the user is much greater than that of the user’s ordinary friends. The experimental results on two real datasets show that the proposed method not only has a better recommendation effect than the state-of-the-art recommendation algorithms, but also has a good performance in the cases of cold-start users.

     

基于弱关系的异质社交网络推荐

针对基于"相似性"或深度信任的传统强关系推荐导致的推荐结果趋于同质性问题,利用弱关系丰富的语义信息和强大的信息传递能力,提出一种基于弱关系的异质社交网络推荐算法.借助用户间的信任值对因引入弱关系产生的大量用户节点进行筛选,减少后续计算负载;充分利用网络中丰富的对象和关系信息,建立拓展的全关系用户-项目异质信息网络模型(...     

基于社会化标注的博客标签推荐方法

为了提高博客系统推荐标签的质量,分析了现有的标签推荐算法及相关技术,提出了一种基于社会化标注的博客标签推荐方法。该方法的优势在于:利用相似博客的社会化标签作为候选标签集,确保了推荐标签的全面性和可用性;基于TF-IDF相似度方法定义筛选步骤去除候选标签集中冗余和冷僻的标签,提高了推荐标签的准确性和高效性。实验结果表明了该方法的有效性。     

基于降噪自编码器的社会化推荐算法

现有的社会化推荐算法未考虑信任用户对目标用户深层的偏好影响。针对这一问题,提出了一种基于深度学习的混合推荐算法,利用降噪自编码器学习用户及其信任用户的评分偏好,使用加权隐藏层来平衡这些表示的重要性,有效建模用户间的潜在偏好交互。在此基础上,通过用户聚类和个性化权重区分不同类的用户受其信任用户的影响程度。在开放数据集上的实验结果表明,该算法优于现有的社会化推荐算法,与主要的推荐算法SoRec、RSTE、SocialMF、TrustMF相比,其平均绝对误差(MAE)和均方根误差(RMSE)显著降低,获得了较好的推荐效果。