社会化商务背景下的情感营销路径——以东方甄选直播为例

社会化商务的发展为企业和消费者提供了双向交互的机会：企业可以通过社交平台更便捷地进行商务活动,消费者可以通过社交网络分享和传递企业信息。在同质化竞争激烈的情况下,企业需要提升对消费者更高层次的情感需求的关注。然而,很多企业对消费需求的认知仍只停留在满足消费者对产品低价的需求痛点上,因而忽视了情感营销策略的运用。东方甄选通过对情感营销策略和社会化交互特征的成功运用,一度成为抖音直播带货的榜首。据此,本文从情感营销的视角切入,结合对东方甄选直播案例的分析,依据社会化交互和情感迁移理论,构建了社会化商务背景下的情感营销路径。通过对东方甄选直播案例的分析,本文提出了“打造情感品牌、组合运用情感营销策略和促进消费者口碑传播”的对策建议,以期为企业管理者提供启示。     

社会化推荐研究进展

文章提供了一个关于社会化推荐研究进展的概述。随着推荐系统研究的不断深入,将社会化影响融入推荐系统成为一个新的研究热点和问题丰富的研究领域。首先描述了社会化推荐的相关技术：推荐系统和社会化网络分析。对当前社会化推荐的一些最新技术方法进行分类介绍,具体包括利用社会化关系推荐物品,利用社会化关系推荐好友,根据内容推荐社会化关系,小组推荐和为团体推荐五个方面。     

社会化媒体营销研究概述

社会化媒体已经成为信息传播的重要渠道，并开始发挥越来越重要的作用，近年来由于WEB2.0技术的成熟与发展，社会化媒体在营销中作用愈加重要。本文主要研究了社会化媒体在营销中的主要作用。     

浅议大学生的马克思主义责任观

根据北京师范大学伦理学与道德教育研究所的一项假设，倘若一个智慧之人过着非常充裕的生活，但如果一个人都不可能见到，那么，他会宁愿离开生活。新形势下，社会对大学生实践能力的要求越来越高，在与人的交往过程中，随之而来的就是要承担着各种各样的社会责任。在借鉴前人研究成果的基础上，从思想政治教育工作的实际出发，结合马克思主义责任观的相关理论作出解释便成了当务之急。     

将SocialCRM进行到底

以社会化媒体营销尝试售后的SocialCRM并算新鲜概念，难的是勾勒出如此清晰量化的CRM投入回报，且兼顾到“口碑”“互动”“CRM”等诸多要素。     

黄章复出能否拯救魅族？

2013年,小米实现销售额213亿元,而创办近11年的魅族,至今尚未突破100亿的门槛。小米的异军突起,让黄章措手不及,同时嗅到了危险的味道。2月9日,黄章通过新浪微博宣布复出。此次复出,其意明显：复燃魅族。从黄章复出之后魅族公司的调整来看,他至少已经意识到了开放的重要性,并试图用社会化营销来挑战小米。     

浅议企业档案管理社会化问题

企业档案管理主要记录了企业发展的历史,它是分析企业发展形势的重要依据,对于企业的发展起到决定性作用。所以,要想提高企业的工作效率,就必须不断创新企业档案管理形式。企业档案管理社会化成为企业发展的必然趋势,它不仅能够为企业节省资本,还能够有效地提高企业档案管理的真实性和科学性。     

Quirky：电商“因小而美”

像Quirky这样的社会化电商企业“因小而美”，不仅成功孵化了很多创意，还让消费者获得了更好的产品。可以说代表了未来电子商务发展的一种方向。     

“来自星星的你”——新媒体时代的整合营销

整合营销不是简单的媒介形式组合,产品的推广到热销,需要一系列构成因素的有机结合。首先介绍了韩剧《来自星星的你》的基本情况,然后深入分析了该剧受到热捧的原因及剧情相关产品大卖背后的营销策略,希望其他产品在进行整合营销时能从中得到借鉴。     

悬念揭晓，微信吹响企业号了!

千呼万唤,微信企业号终于来啦!公测将对所有用户,企业、政府及事业单位、社会化组织开放。     

A framework for tag-aware recommender systems

In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.     

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.     

标签推荐方法研究综述

随着互联网信息的爆炸式增长,标签(由用户指定用来描述项目的关键词)在互联网信息检索领域中变得越来越重要.为在线内容赋予合适的标签,有利于更高效的内容组织和内容消费.而标签推荐通过辅助用户进行打标签的操作,极大地提升了标签的质量,标签推荐也因此受到了研究者们的广泛关注.总结出标签推荐任务的三大特性,即项目内容的多样性、标...     

A Review of Ontology‐Based Tag Recommendation Approaches

Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to use in searching interesting blogs. There is no clear information regarding the meaning of each tag in a tagging process. An user can use various tags for the same content, and he can also use new tags for an item in a blog. When the user selects tags, the resultant metadata may comprise homonyms and synonyms. This may cause an improper relationship among items and ineffective searches for topic information. The collaborative tag recommendation allows a set of freely selected text keywords as tags assigned by users. These tags are imprecise, irrelevant, and misleading because there is no control over the tag assignment. It does not follow any formal guidelines to assist tag generation, and tags are assigned to resources based on the knowledge of the users. This causes misspelled tags, multiple tags with the same meaning, bad word encoding, and personalized words without common meaning. This problem leads to miscategorization of items, irrelevant search results, wrong prediction, and their recommendations. Tag relevancy can be judged only by a specific user. These aspects could provide new challenges and opportunities to its tag recommendation problem. This paper reviews the challenges to meet the tag recommendation problem. A brief comparison between existing works is presented, which we can identify and point out the novel research directions. The overall performance of our ontology‐based recommender systems is favorably compared to other systems in the literature.     

融合关系与内容分析的社会标签推荐

标签是Web 2.0时代信息分类与索引的重要方式.为解决标签系统所面临的不一致性、冗余性以及完备性等问题,标签推荐通过提供备选标签的方法来提高标签的质量.为了进一步提升标签推荐的质量,提出了一种基于标签系统中对象间关系与资源内容融合分析的标签推荐方法,给出了基于LDA(latent Dirichlet allocation)的融合表示对象间关系与资源内容的标签系统生成模型TSM/Forc,提出了一种基于概率的标签推荐方法,并给出了基于吉布斯(Gibbs)抽样的参数估计方法.实验结果表明,该方法可以提供比当前主流与最新方法更加准确的推荐结果.     

标签模糊和冗余在标签推荐中的研究及应用

现有的Folksonomy标签推荐系统使用的推荐算法没有考虑标签模糊和冗余问题,影响了用户建模和对推荐系统评估的准确性,并且降低了系统的推荐质量,增加了用户选择喜好项目时的负担。通过对标签推荐系统的研究,将标签模糊和冗余应用到标签推荐算法当中,有助于提高系统的推荐质量,并且能提供更合理的评价方法。实验结果表明：经过标签模糊和冗余处理的标签推荐算法显著地提高了推荐系统的推荐质量。     

基于标签自适应选择的矩阵分解推荐算法

将标签融入矩阵分解方法是当前推荐系统研究的热点。提出了一种基于标签自适应选择的矩阵分解推荐算法。首先,提出了标签 评分稀疏系数,较好地平衡了推荐过程中潜在特征与标签的使用问题。其次,利用标签的次数来计算标签向量,体现了标签的不同频率对不同物品的影响。最后,给出了算法的总体描述。实验结果表明,算法具有较高的推荐精度和较快的收敛速度。     

TAFFY: incorporating tag information into a diffusion process for personalized recommendations

The last few years have witnessed an explosion of information caused by the exponential growth of the Internet and World Wide Web, which confronted us with information overload and brought about an era of big data, appealing for efficient personalized recommender systems to assist the screening of useful information from various sources. As for a recommender system with more than the fundamental object-user rating information, such accessorial information as tags can be exploited and integrated into final ranking lists to improve recommendation performance. However, although existing studies have demonstrated that tags, as the additional yet useful resource, can be designed to improve recommendation performance, most network-based approaches take users, objects and tags as two bipartite graphs, or a tripartite graph, and therefore overlook either the important information among homogeneous nodes in each sub-graph, or the bipartite relations between users, objects or tags. Moreover, recent studies have suggested that the filtration of weak relationships in networks may reasonably enhance recommendation performance of collaborative filtering methods, and it has also been demonstrated that approaches based on the diffusion processes could more effectively capture relationships between objects and users, hence exhibiting higher performance than a typical collaborative filtering method. Based on these understandings, we propose a data fusion approach that integrates historical and tag data towards personalized recommendations. Our method coverts historical and tag data into complex networks, resorts to a diffusion kernel to measure the strength of associations between users and objects, and adopts Fisher’s combined probability test to obtain the statistical significance of such associations for personalized recommendations. We validate our approach via 10-fold cross-validation experiments. Results show that our method outperforms existing methods in not only the recommendation accuracy and diversity, but also retrieval performance. We further show the robustness of our method to related parameters.     

Content-based and collaborative techniques for tag recommendation: an empirical evaluation

The rapid growth of the so-called Web 2.0 has changed the surfers’ behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents. The success of that collaborative model is justified by two factors: firstly, information is organized in a way that closely reflects the users’ mental model; secondly, the absence of a controlled vocabulary reduces the users’ learning curve and allows the use of evolving vocabularies. Since tags are handled in a purely syntactical way, annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness for complex tasks. Consequently, tag recommenders, with their ability of providing users with the most suitable tags for the resources to be annotated, recently emerged as a way of speeding up the process of tag convergence. The contribution of this work is a tag recommender system implementing both a collaborative and a content-based recommendation technique. The former exploits the user and community tagging behavior for producing recommendations, while the latter exploits some heuristics to extract tags directly from the textual content of resources. Results of experiments carried out on a dataset gathered from Bibsonomy show that hybrid recommendation strategies can outperform single ones and the way of combining them matters for obtaining more accurate results.     

一种三部图网络中标签时间加权的推荐方法

社会标签可以提供对象高度抽象的内容信息和个性偏好信息,因此标签的使用有助于提高个性推荐的精度.用户的偏好会随时间的变化而变化,网络中的资源也会随着时间推移而增加.如何根据用户兴趣的变化推荐出用户即时感兴趣的网络资源,已成为推荐系统研究的新问题.在用户-标签-对象三部分图网络结构中,结合标签使用频率和用户添加标签的时间,提出了一种利用标签时间加权的资源推荐算法.实验结果表明,利用标签时间加权的算法能有效地提高推荐的精度和多样性.