Deep collaborative filtering with social promoter score-based user-item interaction: a new perspective in recommendation

Most of the existing recommender systems understand the preference level of users based on user-item interaction ratings. Rating-based recommendation systems mostly ignore negative users/reviewers (who give poor ratings). There are two types of negative users. Some negative users give negative or poor ratings randomly, and some negative users give ratings according to the quality of items. Some negative users, who give ratings according to the quality of items, are known as reliable negative users, and they are crucial for a better recommendation. Similar characteristics are also applicable to positive users. From a poor reflection of a user to a specific item, the existing recommender systems presume that this item is not in the user’s preferred category. That may not always be correct. We should investigate whether the item is not in the user’s preferred category, whether the user is dissatisfied with the quality of a favorite item or whether the user gives ratings randomly/casually. To overcome this problem, we propose a Social Promoter Score (SPS)-based recommendation. We construct two user-item interaction matrices with users’ explicit SPS value and users’ view activities as implicit feedback. With these matrices as inputs, our attention layer-based deep neural model deepCF_SPS learns a common low-dimensional space to present the features of users and items and understands the way users rate items. Extensive experiments on online review datasets present that our method can be remarkably futuristic compared to some popular baselines. The empirical evidence from the experimental results shows that our model is the best in terms of scalability and runtime over the baselines.

     

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.     

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...     

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。     

LGA: latent genre aware micro-video recommendation on social media

Social media has evolved into one of the most important channels to share micro-videos nowadays. The sheer volume of micro-videos available in social networks often undermines users’ capability to choose the micro-videos that best fit their interests. Recommendation appear as a natural solution to this problem. However, existing video recommendation methods only consider the users’ historical preferences on videos, without exploring any video contents. In this paper, we develop a novel latent genre aware micro-video recommendation model to solve the problem. First, we extract user-item interaction features, and auxiliary features describing both contextual and visual contents of micro-videos. Second, these features are fed into the neural recommendation model that simultaneously learns the latent genres of micro-videos and the optimal recommendation scores. Experiments on real-world dataset demonstrate the effectiveness and the efficiency of our proposed method compared with several state-of-the-art approaches.     

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.     

Automatic multimodal assessment of soft skills in social interactions: a review

Multimedia Tools and Applications - Automatic assessment of soft skills is an interesting problem in social computing. Soft skills are essential to any individual for personal and career...     

Product recommendation with temporal dynamics

In many E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Diapers.com within a relatively long period, and purchase different products for babies within different growth stages. Traditional recommendation algorithms produce recommendation lists similar to items that the target user has accessed before (content filtering), or compute recommendation by analyzing the items purchased by the users who are similar to the target user (collaborative filtering). Such recommendation paradigms cannot effectively resolve the situation with a life cycle, i.e., the need of customers within different stages might vary significantly. In this paper, we model users’ behavior with life cycles by employing hand-crafted item taxonomies, of which the background knowledge can be tailored for the computation of personalized recommendation. In particular, our method first formalizes a user’s long-term behavior using the item taxonomy, and then identifies the exact stage of the user. By incorporating collaborative filtering into recommendation, we can easily provide a personalized item list to the user through other similar users within the same stage. An empirical evaluation conducted on a purchasing data collection obtained from Diapers.com demonstrates the efficacy of our proposed method.     

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...     

Implicit relation-aware social recommendation with variational auto-encoder

World Wide Web - Integrating social networks as auxiliary information shows effectiveness in improving the performance for a recommendation task. Typical models usually characterize the user trust...     

Brand interactions and social media: Enhancing user loyalty through social networking sites

This paper aims to investigate how user loyalty can be achieved and maintained through social networking sites. More specifically, we intend to test the relationships between brands, user loyalty and social media. The research thus provides insights into user-brand relationships through social media and argues how loyal customers can be through social networking websites. Although there are considerable numbers of studies about loyalty; there exists very limited work studying user loyalty through social networking websites. This research presents clearly the reasons for engaging with brands online and examines user behaviors and loyalty. Research provided strong evidence that majority of the social network users follow brand fan pages via social media, even though they have different reasons to do so. The study also measures users’ behavioral and attitudinal loyalty behaviors. Their level of trust to the information they obtained about brands through social media is also established. The hypotheses tested show that brands and customer satisfaction are both positively related to users’ behavioral loyalty.     

DynamiCITY: Revealing city dynamics from citizens social media broadcasts

Social media and mobile devices have revolutionized the way people communicate and share information in various contexts, such as in cities. In today’s “smart” cities, massive amounts of multiple forms of geolocated content is generated daily in social media, out of which knowledge for social interactions and urban dynamics can be derived. This work addresses the problem of detecting urban social activity patterns and interactions, by modeling cities into “dynamic areas”, i.e., coherent geographic areas shaped through social activities. Social media users provide the information on such social activities and interactions in cases when they are on the move around the city neighborhoods. The proposed approach models city places as feature vectors which represent users visiting patterns (social activity), the time of observed visits (temporal activity), and the context of functionality of visited places category. To uncover the dynamics of city areas, a clustering approach is proposed which considers the derived feature vectors to group people’s activities with respect to location, time, and context. The proposed methodology has been implemented on the DynamiCITY platform which demonstrates neighborhood analytics via a Web interface that allows end-users to explore neighborhoods dynamics and gain insights for city cross-neighborhood patterns and inter-relationships.     

Dual influence embedded social recommendation

Recommender systems are designed to solve the information overload problem and have been widely studied for many years. Conventional recommender systems tend to take ratings of users on products into account. With the development of Web 2.0, Rating Networks in many online communities (e.g. Netflix and Douban) allow users not only to co-comment or co-rate their interests (e.g. movies and books), but also to build explicit social networks. Recent recommendation models use various social data, such as observable links, but these explicit pieces of social information incorporating recommendations normally adopt similarity measures (e.g. cosine similarity) to evaluate the explicit relationships in the network - they do not consider the latent and implicit relationships in the network, such as social influence. A target user’s purchase behavior or interest, for instance, is not always determined by their directly connected relationships and may be significantly influenced by the high reputation of people they do not know in the network, or others who have expertise in specific domains (e.g. famous social communities). In this paper, based on the above observations, we first simulate the social influence diffusion in the network to find the global and local influence nodes and then embed this dual influence data into a traditional recommendation model to improve accuracy. Mathematically, we formulate the global and local influence data as new dual social influence regularization terms and embed them into a matrix factorization-based recommendation model. Experiments on real-world datasets demonstrate the effective performance of the proposed method.     

基于位置的社会化网络的并行化推荐算法

针对传统的协同过滤算法在利用签到记录进行兴趣点(POI)推荐时不能充分利用签到信息所隐含的偏好、位置和社交网络信息而损失准确率的问题,以及传统的单机串行算法在大数据处理能力上的弱势,提出一种基于位置和朋友关系的协同过滤(LFBCF)算法,以用户历史偏好为基础,综合考虑用户社交关系网络进行协同过滤,并以用户的活动范围作为约束实现对用户的兴趣点推荐。为了支持大数据量的实验,将算法在Spark分布式计算平台上进行了并行化实现。研究过程中使用了Gowalla和Brightkite这两个基于位置的社会化网络数据集,分析了数据集中签到数量、签到位置之间距离、社交关系等可能对推荐结果造成影响的因素,以此来支持提出的算法。实验部分通过与传统的协同过滤算法等经典算法在准确率、F-measure上的对比验证了算法在推荐效果上的优越性,并通过并行算法与单机串行算法在不同数据规模上加速比的对比验证了算法并行化的意义以及性能上的优越性。     

基于位置社交网络的个性化兴趣点推荐

兴趣点(point-of-interest,POI)推荐是基于位置的社交网络(location-based social networks,LBSN)中一项重要的服务。针对目前推荐算法存在的噪声数据影响推荐质量,用户个性化程度低的问题,提出了一种个性化联合推荐算法。提出了引入POI的位置因素去除不可能或可能性较小的POI,形成初步候选集;综合考虑POI的类别、流行度及用户的社会行为,增加用户个性化的程度,提高推荐结果的质量。在Foursquare真实签到数据集上的实验,证明了提出的联合推荐算法与目前先进的算法相比,准确率提高11%,召回率提高8%。