基于多语义关系的个性化查询扩展方法*

随着数字内容不断增长,信息检索技术已经不能满足不同用户对高精度信息内容获取的需求.文中提出基于多语义关系的个性化查询扩展方法,并应用于基于社会化标签的个性化搜索系统.模型使用标签-主题模型对用户兴趣模型进行建模,能够更有效地表达语义和提升搜索效果.在此基础上,进一步提出基于多语义关系的个性化查询扩展方法,利用社会化标签的多重语义特征进行扩展词的选择.在大规模真实社会化标签数据集上的实验表明,文中方法优于非个性化搜索及其它基于社会化标签系统的个性化查询扩展方法.     

一种社会化标签系统的用户兴趣建模方法

随着互联网技术的发展,海量信息同时呈现,使得用户难以有效发现本身感兴趣信息,并且大量的网络暗信息少人问津,难以被普通用户获取,为了处理信息过载问题,出现了个性化用户系统,以弥补海量信息中用户很难找到有用信息的问题。而只有具备了精准的用户兴趣模型,个性化用户系统才得以真正存在。因此用户兴趣建模的研究与探索具有深远的意义。从而,本文首先介绍了社会化标签Tag系统,其次分析了用户兴趣建模的四种表示方法,最后讨论了一种基于社会化标签系统的兴趣建模方法。     

Semantic tagging and linking of software engineering social content

Social online communities and platforms play a significant role in the activities of software developers either as an integral part of the main activities or through complimentary knowledge and information sharing. As such techniques become more prevalent resulting in a wealth of shared information, the need to effectively organize and sift through the information becomes more important. Top-down approaches such as formal hierarchical directories have shown to lack scalability to be applicable to these circumstanes. Light-weight bottom-up techniques such as community tagging have shown promise for better organizing the available content. However, in more focused communities of practice, such as software engineering and development, community tagging can face some challenges such as tag explosion, locality of tags and interpretation differences, to name a few. To address these challenges, we propose a semantic tagging approach that benefits from the information available in Wikipedia to semantically ground the tagging process and provide a methodical approach for tagging social software engineering content. We have shown that our approach is able to provide high quality tags for social software engineering content that can be used not only for organizing such content but also for making meaningful and relevant content recommendation to the users both within a local community and also across multiple social online communities. We have empirically validated our approach through four main research questions. The results of our observations show that the proposed approach is quite effective in organizing social software engineering content and making relevant, helpful and novel content recommendations to software developers and users of social software engineering communities.     

Composition pattern oriented tag extraction from short documents using a structural learning method

With the rapid growth of web, automatic tagging that detects informative terms from a document becomes an important problem for information aggregation and sharing services. In particular, automatic tagging for short documents becomes more interesting as many users are increasingly publishing information through social media services which encourage users to create the documents of short length. In this paper, we propose a novel automatic tagging model for short text documents from social media services, following the framework of supervised learning. We redefine traditional frequency-based term features so that they can address the properties of the documents created under length limitation and consider sequential dependencies between successive terms in a document based on a structural support vector machine. In addition, our proposed approach incorporates composition patterns by which users put informative terms into their documents. Extensive experiments have been conducted to validate the presented approach, and it was found that the proposed term features were effective for extracting tags, and the tag extractor trained by considering the sequential dependencies and composition patterns achieved superior performance results over the existing alternative methods.     

Integrating ontology technology with folksonomies for personalized social tag recommendation

Social tagging systems leverage social interoperability by facilitating the searching, sharing, and exchanging of tagging resources. A major drawback of existing social tagging systems is that social tags are used as keywords in keyword-based search. They focus on keywords and human interpretability rather than on computer interpretable semantic knowledge. Therefore, social tags are useful for information sharing and organizing, but they lack the computer-interpretability needed to facilitate a personalized social tag recommendation. An interesting issue is how to automatically generate a personalized social tag recommendation list to users when a resource is accessed by users. The novel solution proposed in this study is a hybrid approach based on semantic tag-based resource profile and user preference to provide personalized social tag recommendation. Experiments show that the Precision and Recall of the proposed hybrid approach effectively improves the accuracy of social tag recommendation.     

Summarizing dynamic Social Tagging Systems

Social tagging is a popular method that allows users of social networks to share annotation in the form of keywords, called tags, assigned to resources. Social tagging addresses information overload by easing the task of locating interesting entities in a social network. Nevertheless, users can still be overwhelmed by too many tags posted at each moment. A process is needed that offers an accurate overview of the representative entities and their relationships with each other, while dealing with the dynamics of social tagging and of tags’ semantics. We propose a method for the automated summarization of an evolving multi-modal social network, focusing on the entities that stay representative over time for some subnetwork in the social tagging system. We report on experiments with real data from the Bibsonomy social tagging system, where we compare our dynamic approach with a static one.     

Folksonomy‐Based Recommender Systems: A State‐of‐the‐Art Review

Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open‐ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy‐based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link‐analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines.     

一种基于社会化标注的查询扩展研究

采用社会化标签可以提高检索质量,但真实的标注系统往往比较稀疏,并且标签存在无序性、不规范性和低效性等特点,因此单纯使用传统的SimRank等相似度算法难以奏效.为此,在SimRank算法基础上融入Jaccard系数计算,提出一种改进的社会化标签的相似度计算方法,称作Jaccard SimRank(JSR)算法,更加直观地描述社会化标签之间的相似度,在用户标注网络资源时自动对标签集进行扩展,增加标注密度,并在检索时对标签集进行扩展,因而能够更充分利用社会化标注系统的信息实现有效检索.实验结果表明,与传统的相似度算法相比,JSR方法有效提高了查询扩展系统的性能.     

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.     

A self-adapted method for the categorization of social resources

Social tagging systems have become a popular system to organize information in many web 2.0 sites. They are also being rapidly adopted in enterprises to enhance information sharing, knowledge sharing and emerged as a novel categorization scheme based on the collective knowledge of people.Scalability is an issue of the categorization of the resources of social tagging systems. Scalability has highlighted a critical trade-off between accuracy and complexity. As social tagging systems evolve over time, resource categories can appear or disappear either by grouping new resources or disaggregating existing ones, and this implies the re-assignation of the resources involved to others categories. This makes the methods and/or algorithms that categorize resources of social tagging systems to be non-scalable, and then not efficiently implementable on real social tagging systems. This paper presents a simple method for categorizing resources on social tagging systems which is self-adaptive, scalable and implementable in any real social tagging system.     

In & out zooming on time-aware user/tag clusters

The common ground behind most approaches that analyze social tagging systems is addressing the information challenge that emerges from the massive activity of millions of users who interact and share resources and/or metadata online. However, lack of any time-related data in the analysis process implicitly denies much of the dynamic nature of social tagging activity. In this paper we claim that holding a temporal dimension, allows for tracking macroscopic and microscopic users’ interests, detecting emerging trends and recognizing events. To this end, we propose a time-aware co-clustering approach for acquiring semantic and temporal patterns out of the tagging activity. The resulted clusters contain both users and tags of similar patterns over time, and reveal non-obvious or “hidden” relations among users and topics of their common interest. Zoom in & out views serve as visualization methods on different aspects of the clusters’ structure, in order to evaluate the efficiency of the approach.     

The evaluation of a social adaptive website for cultural events

In this paper, we present an evaluation of a social adaptive website in the domain of cultural events, iCITY DSA, which provides information about cultural resources and events that promote the cultural heritage in the city of Turin. Using this evaluation, our objective was to investigate the actual usage of a social adaptive website, in an effort to discover the real behavior of users, the unforeseen correlations among user actions and the consequent interactive behavior, the accuracy of both system and social recommendations and their impact on the users themselves, and the role of tagging in the user modeling process. The major contributions of the paper are manifold: insights into user interactions with social adaptive systems; guidelines for future designs; evaluation of the tagging activity and tag meanings in relation to the application domain and thus their impact on the representation of the user model; and a demonstration of how a combination and interplay of evaluation methodologies (e.g., quantitative and qualitative) can enhance our comprehension of evaluation data.     

Linking information and people in a social system for academic conferences

This paper investigates the feasibility of maintaining a social information system to support attendees at an academic conference. The main challenge of this work was to create an infrastructure where users’ social activities, such as bookmarking, tagging, and social linking could be used to enhance user navigation and maximize the users’ ability to locate two important types of information in conference settings: presentations to attend and attendees to meet. We developed Conference Navigator 3, a social conference support system that integrates a conference schedule planner with a social linking service. We examined its potential and functions in the context of a medium-scale academic conference. In this paper, we present the design of the system’s socially enabled features and report the results of a conference-based study. Our study demonstrates the feasibility of social information systems for supporting academic conferences. Despite the low number of potential users and the short timeframe in which conferences took place, the usage of the system was high enough to provide sufficient data for social mechanisms. The study shows that most critical social features were highly appreciated and used, and provides direction for further research.     

A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis

Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different types of recommendations: They can recommend 1) tags to users, based on what tags other users have used for the same items, 2) items to users, based on tags they have in common with other similar users, and 3) users with common social interest, based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags. These data are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the Kernel-SVD smoothing technique. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.     

Understanding why users tag: A survey of tagging motivation literature and results from an empirical study

While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.     

基于时序行为的协同过滤推荐算法

协同过滤直接根据用户的行为记录去预测其可能喜欢的产品,是现今最为成功、应用最广泛的推荐方法.概率矩阵分解算法是一类重要的协同过滤方式.它通过学习低维的近似矩阵进行推荐,能够有效处理海量数据.然而,传统的概率矩阵分解方法往往忽略了用户(产品)之间的结构关系,影响推荐算法的效果.通过衡量用户(产品)之间的关系寻找相似的邻居用户(产品),可以更准确地识别用户的个人兴趣,从而有效提高协同过滤推荐精度.为此,提出一种对用户(产品)间的时序行为建模的方法.基于该方法,可以发现对当前用户(产品)影响最大的邻居集合.进一步地,将该邻居集合成功融合到基于概率矩阵分解的协同过滤推荐算法中.在两个真实数据集上的验证结果表明,所提出的SequentialMF 推荐算法与传统的使用社交网络信息与标签信息的推荐算法相比,能够更有效地预测用户实际评分,提升推荐精度.     

WESONet: Applying semantic web technologies and collaborative tagging to multimedia web information systems

The publication of different media types, like images, audio and video in the World Wide Web is getting more importance each day. However, searching and locating content in multimedia sites is challenging. In this paper, we propose a platform for the development of multimedia web information systems. Our approach is based on the combination between semantic web technologies and collaborative tagging. Producers can add meta-data to multimedia content associating it with different domain-specific ontologies. At the same time, users can tag the content in a collaborative way. The proposed system uses a search engine that combines both kinds of meta-data to locate the desired content. It will also provide browsing capabilities through the ontology concepts and the developed tags.     

Understanding why users tag: A survey of tagging motivation literature and results from an empirical study

While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.     

An Empirical Study of Tagging for Personal Information Organization: Performance,Workload, Memory,and Consistency

Tagging allows users to organize their information and retrieve it later with multiple, freely chosen keywords, which is impossible with categorical folders. The need to organize information for personal later retrieval has been found to be one of the most important motivations for tagging. Despite the popularity of the concept, more empirical evidence is still required to verify the real benefit of tagging for information organization and retrieval. Furthermore, the problems of inconsistency in tagging hamper the usefulness of tagging as an effective organization tool. The current study aims to investigate users' motivation, performance, and workload when they use tagging to organize personal information and how the system design could improve the process. First, a pilot study combining think-aloud and interviews was conducted to obtain insights on why and how users select tags. Then, the first experiment with 40 participants was conducted to empirically compare the performance and workload difference in information organization and retrieval tasks between categorization and tagging interfaces. The results show that tagging users reported a significantly higher level of mental demand and frustration when performing organizational tasks and a significantly higher level of temporal demand and error rate when performing retrieval tasks compared with categorization. However, tagging users tend to have better memory of the organized content. The second experiment aimed to study how individual tagging consistency can be improved by the proper visualization of tag suggestions. The impact of frequency visualization by font size and semantically clustering was studied with 40 participants. The results show that semantically clustered tag clouds improve tagging consistency significantly; when a semantic clustering effect is presented, frequency visualization by font size can significantly alleviate the physical demand perceived by users.     

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.