Folksonomy Data Research Articles

Search Personalization in Folksonomy by Exploiting Multiple and Temporal Aspects of User Profiles

Social tagging data, also known as folksonomy, are a valuable indication for the user’s understanding of a resource. The nature of folksonomy data in which a user annotates a resource with their opinions provides immense potential to contribute to search personalization. The challenge lies in extracting interests from the folksonomy data and building accurate user profiles while maintaining their characteristics. Furthermore, the current state-of-the-art technologies that utilize folksonomy for search personalization have not fully exploited both multiple and temporal aspects in user profiles. In this paper, we propose a search personalization framework that constructs a user profile network with identification of the multiple topics of the user and the temporal values of tags. Then, the user profile network is further explored through a link analysis technique for the network to score the tags by their importance. The performance of the proposed framework is evaluated against various state-of-the-art folksonomy-based personalization models and it consistently outperforms all of the compared models under the conditions of the best combination of ranking functions and link analysis techniques.

Query Extension with Improved User Profiles for User tailored Search taking Advantage over Folksonomy Data

Query Extension with Improved User Profiles for User tailored Search taking Advantage over Folksonomy Data

Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data

Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships, and semantic matching approaches are among those favored by previous research. However, user profiles which only contain a user's past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile, respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach outperforms traditional techniques, including existing non-personalized and personalized query expansion methods.

Spatio-semantic user profiles in location-based social networks

Knowledge of users’ visits to places is one of the keys to understanding their interest in places. User-contributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users’ association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets.

A probabilistic framework of preference discovery from folksonomy corpus

The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered.

Building Domain Ontologies Out of Folksonomies and Linked Data

In this paper we propose an automatic method for building domain ontologies where we leverage the emerging vocabulary from social tagging systems, and the existing semantics in the Linked Open Data cloud to enrich semantically the terms that shape the domain ontology. We systematically capture a domain vocabulary by searching for relevant resources in the folksonomy graph using a spreading activation strategy. We use the vocabulary to identify domain classes and relationships among them by querying knowledge bases published as linked data. We present a case study in the financial domain where we experiment with different settings and show the feasibility of our approach using real folksonomy data.

TagRec

This article presents TagRec , a framework to foster reproducible evaluation and development of recommender algorithms based on folksonomy data. The purpose of TagRec is to provide the research community with a standardised framework that supports all steps of the development process and the evaluation of tag-based recommendation algorithms in a reproducible way, including methods for data pre-processing, data modeling and recommender evaluation. TagRec currently contains 32 state-of-the-art algorithms for tag and item prediction, including a set of novel and very efficient algorithms based on the human cognition theories ACT-R and MINERVA2. The framework should be relevant for researchers, teachers, students and developers working on recommender systems and predictive modeling in general and those interested in tag-based recommender algorithms in particular.

질의어의 근접성 정보 및 그래프 프로파일링 기법을 이용한 태그 기반 개인화 검색

Folksonomy data, which is derived from social tagging systems, is a useful source for understanding a user's intention and interest. Using the folksonomy data, it is possible to create an accurate user profile which can be utilized to build a personalized search system. However there are limitations in some of the traditional methods such as Vector Space Model(VSM) for user profiling and similarity computation. This paper suggests a novel method with graph-based user and document profile which uses the proximity information of query terms to improve personalized search. We demonstrate the performance of the suggested method by comparing its performance with several state-of-the-art VSM based personalization models in two different folksonomy datasets. The results show that the proposed model constantly outperforms the other state-of-the-art personalization models. Furthermore, the parameter sensitivity results show that the proposed model is parameter-free in that it is not affected by the idiosyncratic nature of datasets.

Community-aware user profile enrichment in folksonomy

In the era of big data, collaborative tagging (a.k.a. folksonomy) systems have proliferated as a consequence of the growth of Web 2.0 communities. Constructing user profiles from folksonomy systems is useful for many applications such as personalized search and recommender systems. The identification of latent user communities is one way to better understand and meet user needs. The behavior of users is highly influenced by the behavior of their neighbors or community members, and this can be utilized in constructing user profiles. However, conventional user profiling techniques often encounter data sparsity problems as data from a single user is insufficient to build a powerful profile. Hence, in this paper we propose a method of enriching user profiles based on latent user communities in folksonomy data. Specifically, the proposed approach contains four sub-processes: (i) tag-based user profiles are extracted from a folksonomy tripartite graph; (ii) a multi-faceted folksonomy graph is constructed by integrating tag and image affinity subgraphs with the folksonomy tripartite graph; (iii) random walk distance is used to unify various relationships and measure user similarities; (iv) a novel prototype-based clustering method based on user similarities is used to identify user communities, which are further used to enrich the extracted user profiles. To evaluate the proposed method, we conducted experiments using a public dataset, the results of which show that our approach outperforms previous ones in user profile enrichment.

Efficient and secure exact-match queries in outsourced databases

Data management can now be outsourced to cloud service providers like Amazon Web Services or IBM SmartCloud. This calls for encrypted data-representation schemes that also give way to efficient query processing. State-of-the-art approaches are overly expensive for exact-match queries in the worst case, or they do not ensure privacy if an adversary knows the data distribution. In this paper, we propose a new privacy approach without these shortcomings. It makes use of encryption, obfuscated indices, and data fragmentation. To speed up query processing, we propose three novel data-transformation and query-execution schemes. For two schemes, we prove that an adversary capable of solving any polynomial problem cannot determine if any attribute values appear together in a tuple. Thus, with our schemes, sensitive data is not linked to personally identifiable information. To evaluate our third scheme, we propose a measure that quantifies the risk of disclosure. We evaluate our approach on real-world folksonomy data. Our evaluation shows that its average response time of exact-match queries with 15 million tuples is under one second on a conventional desktop PC.

Identifying Overlying Group of People through Clustering

Folksonomies like Delicious and LastFm are modeled as multilateral (user-resource-tag) hypergraphs for studying their network properties. Detecting communities of similar nodes from such networks is a challenging problem. Most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, users have multiple relevant interests and same resource is often tagged with semantically different tags. Few attempts to perceive overlapping communities work on forecasts of hypergraph, which results in momentous loss of information contained in original tripartite structure. Propose first algorithm to detect overlapping communities in folksonomies using complete hypergraph structure. The authors’ algorithm converts a hypergraph into its parallel line graph, using measures of hyperedge similarity, whereby any community detection algorithm on unipartite graphs can be used to produce intersecting communities in folksonomy. Through extensive experiments on synthetic as well as real folksonomy data, demonstrate that proposed algorithm can detect better community structures as compared to existing state-of-the-art algorithms for folksonomies.

Folksonomy-based ad hoc community detection in online social networks

Advances in Web 2.0 technologies have led to unstructured information overload in the complex and multidimensional datasets that originate in social networks. This overload can diminish the quality of web-related services such as recommender systems in the social web and can overwhelm users with irrelevant information. Such overload indicates the need to design and develop efficient and accurate information management and retrieval systems in social networks and anticipate recommendations in the semantic context of user interest. Community detection in social networks can help identify higher-order structures that unveil insight into networks and their functional organization and reduce irrelevant information received by the user. In this study, the application of community detection in online social networks is investigated within the framework of topic discovery-based user link identification and is consequently used to uncover implicit user communities (ad hoc communities). In the proposed approach, we use a graph-based information extraction technique that provides for a personalized information retrieval (recommender) system. We hypothesize that the ad hoc communities of users sharing similar interests embedded in a folksonomy-based social network can be identified by overlapping tag clusters in the tag concept hierarchy. Our methodology incorporates the novel information extraction techniques of topic modeling for topic extraction (feature extraction), user profile modeling for user profile extraction, and community extraction from the social graph, modeled in a framework to derive relevant ad hoc user communities of social interest from folksonomy data. Our experimental results demonstrate an accuracy of 70–98 % in community detection using data obtained from CiteULike®.

Improving neighborhood based Collaborative Filtering via integrated folksonomy information

Personalized recommender systems which can provide people with suggestions according to individual interests usually rely on Collaborative Filtering (CF). The neighborhood based model (NBM) is a common choice when implementing such recommenders due to the intuitive nature; however, the recommendation accuracy is a major concern. Current NBM based recommenders mostly address the accuracy issue based on the rating data alone, whereas research on hybrid recommender systems suggests that users enjoy specifying feedback about items across multiple dimensions. In this work we aim to improve the accuracy of NBM via integrating the folksonomy information. To achieve this objective, we first propose the folksonomy network (FN) to analyze the item relevance described by the folksonomy data. We subsequently integrate the obtained folksonomy information into the global-optimization based NBM for making multi-source based recommendations. Experiments on the MovieLens dataset suggest positive results, which prove the efficiency of our strategy.

그룹화된 폭소노미 구축을 위한 프레임워크와 지원도구의 개발

폭소노미는 웹에 존재하는 리소스에 대해 구성원이 자유롭게 선택한 태그를 붙여서 정보를 체계화하는 새로운 분류 체계이다. Del.icio.us, Flickr, YouTube 등과 같은 최근의 폭소노미 기반 협력 태깅 시스템들을 살펴보면, 폭소노미 데이터를 체계화하고 유용한 서비스를 제공하기 위해 폭소노미의 각 구성요소들(사용자들, 태그들, 리소스들)을 그룹핑하기 위한 기능들을 추가하고 있다. 본 논문에서는, 일반적인 폭소노미에 "그룹(Group)"이라는 개념을 도입하여 확장한 그룹화된 폭소노미(Grouped Folksonomy) 모델과 그룹핑 관련 기본 연산들(Group Aggregation, Group Composition, Group Intersection, Group Difference)을 제안한다. 또한, 본 논문에서는 그룹화된 폭소노미의 구축과 기본연산 수행을 지원하는 도구(GFO)의 개발과 활용사례를 소개한다. 본 연구에서 제안된 모델과 기본 연산들로 구성된 그룹화된 폭소노미 구축을 위한 프레임워크는 폭소노미의 각 구성요소들을 그룹핑하여 그룹화된 폭소노미를 구성하고 보다 유용한 정보들을 추출 및 통합하여 체계화 할 수 있는 토대를 제공한다. A folksonomy is a new classification approach for organizing information by users to freely attach one or more tags to various resources published on the web. Recently, in order to provide useful services and organize the folksonomy data, many collaborative tagging systems based on folksonomy offer additional functionalities for grouping each elements of a folksonomy. In this paper, organization framework for grouped folksonomy is proposed. That is, we suggest the grouped folksonomy model that is an extended folksonomy with the concept of "group" and fundamental operations(Group Aggregation, Group Composition, Group Intersection, Group Difference) for grouping of folksonomy elements. Also, we developed a supporting tool(GFO) that constructs grouped folksonomy and executes fundamental operations. And we introduce some cases using the fundamental operations for grouping of each elements of folksonomy. Based on suggested our approach, we can construct grouped folksonomy and organize and extract useful information from the folksonomy data by grouping each elements of a folksonomy.

FR: A Novel Strategy to Improve Item-Oriented Collaborative Filtering

Traditional Item-oriented Collaborative Filtering (ICF) suffers the problem of low recommendation accuracy in spite of its popularity. In this paper we propose two novel strategies respectively named Recommendation Bias Removal (RBR) and Folksonomy Network Based Filtering (FNBF) to address this issue; with a special focus on the accuracy of individual recommendations. RBR statistically estimates the Recommendation Bias (RB) between system predictions and actual user ratings, and then modifies the system predictions via removing the corresponding RB estimations. FNBF first builds the Folksonomy Network (FN) model based on the folksonomy data, and then applies the FN model to filtering out the irrelative recommendation results of ICF. We further combine FNBF and RBR to form the FR strategy which can further improve the recommendation quality based on information from both ratings and folksonomy data. The empirical studies on large real datasets showed that both FNBF and RBR were effective on the accuracy issue; furthermore, a recommender with FR could make more accurate recommendations due to the aggregating effect of FNBF and RBR.

형식개념분석을 이용한 폭소노미 마이닝 기법과 지원도구의 개발

폭소노미(folksonomy)는 웹에 존재하는 리소스에 대해 사용자가 자유롭게 선택한 태그(tag)를 붙여서 정보를 체계화하는 새로운 분류 체계이다. 폭소노미 기반의 시스템에서는 사용자들의 협력태깅에 의해 사용자, 태그, 리소스사이의 관계를 나타내는 3항원 소데이터가 생성된다. 이와 같은 폭소노미 데이터는 웹 리소스에 대한 정보체계화를 위한 메타데이터로서 시맨틱 웹과 웹2.0 분야에 활용되고 있다. 본 논문에서는 다종다양한 폭소노미 데이터를 다양한 관점으로 분석하여 유용한 정보를 추출하기 위한 형식개념분석 기반의 폭소노미 데이터 마이닝 기법을 제안하고, 이를 지원하기 위한 분석도구 FMT를 개발하였다. 또한, 제안한 기법과 FMT의 유용성을 검증하기 위하여, 폭소노미 기반 시스템인 del.icio.us의 데이터를 대상으로 실험을 수행하고, 그 결과를 보고한다. Folksonomy is a user-generated taxonomy to organize information by which a user assigns tags to resources published on the web. Triadic datas that indicate relations of between users, tags, and resources, are created by collaborative tagging from many users in folksonomy-based system. Such the folksonomy data has been utilized in the field of the semantic web and web2.0 as metadata about web resources. In this paper, we propose FCA-based folksonomy data mining approach in order to extract the useful information from folksonomy data with various points of view. And we developed tool for supporting our approach. In order to verify the usefulness of our proposed approach and FMT, we have done some experiments for data of del.icio.us, which is a popular folksonomy-based bookmarking system. And we report about result of our experiments.