The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags

In this paper, we evaluate the effectiveness of a semantic smoothing technique to organize folksonomy tags. Folksonomy tags have no explicit relations and vary because they form uncontrolled vocabulary. We discriminates so-called subjective tags like “cool” and “fun” from folksonomy tags without any extra knowledge other than folksonomy triples and use the level of tag generalization to form the objective tags into a hierarchy. We verify that entropy of folksonomy tags is an effective measure for discriminating subjective folksonomy tags. Our hierarchical tag allocation method guarantees the number of children nodes and increases the number of available paths to a target node compared to an existing tree allocation method for folksonomy tags.     

Community-Aware Resource Profiling for Personalized Search in Folksonomy

In recent years,there is a fast proliferation of collaborative tagging(a.k.a.folksonomy) systems in Web 2.0 communities.With the increasingly large amount of data,how to assist users in searching their interested resources by utilizing these semantic tags becomes a crucial problem.Collaborative tagging systems provide an environment for users to annotate resources,and most users give annotations according to their perspectives or feelings.However,users may have different perspectives or feelings on resources,e.g.,some of them may share similar perspectives yet have a conflict with others.Thus,modeling the profile of a resource based on tags given by all users who have annotated the resource is neither suitable nor reasonable.We propose,to tackle this problem in this paper,a community-aware approach to constructing resource profiles via social filtering.In order to discover user communities,three different strategies are devised and discussed.Moreover,we present a personalized search approach by combining a switching fusion method and a revised needs-relevance function,to optimize personalized resources ranking based on user preferences and user issued query.We conduct experiments on a collected real life dataset by comparing the performance of our proposed approach and baseline methods.The experimental results verify our observations and effectiveness of proposed method.     

Semantic similarity measures for enhancing information retrieval in folksonomies

Collaborative tagging systems, also known as folksonomies, enable a user to annotate various web resources with a free set of tags for sharing and searching purposes. Tags in a folksonomy reflect users’ collaborative cognition about information. Tags play an important role in a folksonomy as a means of indexing information to facilitate search and navigation of resources. However, the semantics of the tags, and therefore the semantics of the resources, are neither known nor explicitly stated. It is therefore difficult for users to find related resources due to the absence of a consistent semantic meaning among tags. The shortage of relevant tags increases data sparseness and decreases the rate of information extraction with respect to user queries. Defining semantic relationships between tags, resources, and users is an important research issue for the retrieval of related information from folksonomies. In this research, a method for finding semantic relationships among tags is proposed. The present study considers not only the pairwise relationships between tags, resources, and users, but also the relationships among all three. Experimental results using real datasets from Flickr and Del.icio.us show that the method proposed here is more effective than previous methods such as LCH, JCN, and LIN in finding semantic relationships among tags in a folksonomy.     

Swallow: Resource and Tag Recommender System Based on Heat Diffusion Algorithm in Social Annotation Systems

Social annotation systems (SAS) allow users to annotate different online resources with keywords (tags). These systems help users in finding, organizing, and retrieving online resources to significantly provide collaborative semantic data to be potentially applied by recommender systems. Previous studies on SAS had been worked on tag recommendation. Recently, SAS‐based resource recommendation has received more attention by scholars. In the most of such systems, with respect to annotated tags, searched resources are recommended to user, and their recent behavior and click‐through is not taken into account. In the current study, to be able to design and implement a more precise recommender system, because of previous users' tagging data and users' current click‐through, it was attempted to work on the both resource (such as web pages, research papers, etc.) and tag recommendation problem. Moreover, by applying heat diffusion algorithm during the recommendation process, more diverse options would present to the user. After extracting data, such as users, tags, resources, and relations between them, the recommender system so called “Swallow” creates a graph‐based pattern from system log files. Eventually, following the active user path and observing heat conduction on the created pattern, user further goals are anticipated and recommended to him. Test results on SAS data set demonstrate that the proposed algorithm has improved the accuracy of former recommendation algorithms.     

Introducing lateral thinking in search engines

Decomposing a very complex problem into smaller subproblems that are much easier to solve is not a new idea. The “Parisian Approach”[9] applies this principle extensively to shatter complexity by cutting down the original problem into many small subproblems that are then globally optimized thanks to an evolutionary algorithm. This paper describes how this approach has been used to interactively evolve a user profile to be used by a search engine. User queries are rewritten thanks to the evolved profile, resulting in an increased diversity in the retrieved documents that is showing an interesting property: even though precision is lost, retrieved documents relate both to the user’s query and to his areas of interest in a manner that evokes “lateral thinking”. This paper describes ELISE, an Evolutionary Learning Interactive Search Engine that interactively evolves rewriting modules and rules (some kind of elaborated user profile) along a Parisian Approach. Results obtained over a public domain benchmark (Cystic Fibrosis Database) are presented and discussed. This research is partly funded by Novartis-Pharma (IK@N/KE)     

Ubiquitous Memories: a memory externalization system using physical objects

In this paper we propose an object-triggered human memory augmentation system named “Ubiquitous Memories” that enables a user to directly associate his/her experience data with physical objects by using a “touching” operation. A user conceptually encloses his/her experiences gathered through sense organs into physical objects by simply touching an object. The user can also disclose and re-experience for himself/herself the experiences accumulated in an object by the same operation. We implemented a prototype system composed basically of a radio frequency identification (RFID) device. Physical objects are also attached to RFID tags. We conducted two experiments. The first experiment confirms a succession of the “encoding specificity principle,” which is well known in the research field of psychology, to the Ubiquitous Memories system. The second experiment aims at a clarification of the system’s characteristics by comparing the system with other memory externalization strategies. The results show the Ubiquitous Memories system is effective for supporting memorization and recollection of contextual events.     

Preventing Recommendation Attack in Trust-Based Recommender Systems

Despite its success, similarity-based collaborative filtering suffers from some limitations, such as scalability, sparsity and recommendation attack. Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations. We argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender system. To the best of our knowledge, there has not any prior study on recommendation attack in a trust-based recommender system. We analyze the attack problem, and find that “victim” nodes play a significant role in the attack. Furthermore, we propose a data provenance method to trace malicious users and identify the “victim” nodes as distrust users of recommender system. Feasibility study of the defend method is done with the dataset crawled from Epinions website.     

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.     

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.     

Deep Web adaptive crawling based on minimum executable pattern

The key to Deep Web Crawling is to submit valid input values to a query form and retrieve Deep Web content efficiently. In the literature, related work focus only on generic text boxes or entire query forms, causing the problem of “data islands” or inferior validity of query submission. This paper proposes the concept of Minimum Executable Pattern (MEP), a minimal combination of elements in a query form that can conduct a successful query, and then presents a MEPGeneration method and a MEP-based Deep Web adaptive crawling method. The query form is parsed and partitioned into MEP set, and then local-optimal queries are generated by choosing a MEP in the MEP set and a keyword vector of the MEP. Furthermore, the crawler can make a decision on its termination to balance the trade-off between high coverage of the content and resource consumption. The adoption of MEP is expected to improve the validity of query submission, and adaptive selection of multiple MEPs shows good effect for overcoming the problem of “data islands”. We present a set of experiments to validate the effectiveness of the proposed method. Experimental results show that our method outperforms the state of art methods in terms of query capability and applicability, and on average, it achieves good coverage by issuing only a few hundred queries.     

On a serendipity-oriented recommender system based on folksonomy

This paper proposes a recommendation method that focuses on not only predictive accuracy but also serendipity. On many of the conventional recommendation methods, items are categorized according to their attributes (a genre, an authors, etc.) by the recommender in advance, and recommendation is made using the results of the categorization. In this study, impressions of users to items are adopted as a feature of the items, and each item is categorized according to the feature. Impressions used in such categorization are prepared using folksonomy, which classifies items using tags given by users. Next, the idea of “concepts” was introduced to avoid synonym and polysemy problems of tags. “Concepts” are impressions of users on items inferred from attached tags of folksonomy. The inferring method was also devised. A recommender system based on the method was developed in java language, and the effectiveness of the proposed method was verified through recommender experiments.     

An expressive language and interface for image querying

One key component in providing effective image data management support is an expressive query language/interface. In this paper, we describe the EXQUISI system that we have developed. A main contribution of EXQUISI is its ability to allow a user to express subtle differences that may exist between images to be retrieved and other images that are similar. In particular, it allows the user to incorporate ambiguities and imprecisions in specifying his/her query. Another important aspect of EXQUISI is the provision of a reformulation language by which the user can ask “like this in what” queries, by specifying which parts of a returned image the user wants to include and exclude.     

Personalized Web search for improving retrieval effectiveness

Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.     

一种面向协作标签系统的图片检索聚类方法

为了更有效地进行图片检索,提出了一种面向Web2.0协作标签系统的图片检索聚类方法。该算法首先针对标签空间由于标签表达多样性带来的不一致问题,并通过挖掘标签间的词汇关系实现语义级查询扩展来得到语义可能相关的扩展图片结果集;然后根据标签间的相关度度量选出图片结果集中与查询标签高相关的标签集,接着采用一种自顶向下启发式的图划分算法来自动对次相关标签集进行分类。最后图片结果集即根据标签分类结果被聚类。为验证该方法的效果,从标签图片共享网站Flickr上随机下载了大量真实图片集以及所含带的标签元数据,在已实现的图片检索原型系统PivotBrowser上进行了大量实验,结果证明,该聚类算法能有效解决标签空间存在的标签表达不一致问题和标签查询歧义性问题,能提供更满意的用户检索。     

Tag-based user modeling for social multi-device adaptive guides

This paper aims to demonstrate that the principles of adaptation and user modeling, especially social annotation, can be integrated fruitfully with those of the web 2.0 paradigm and thereby enhance in the domain of cultural heritage. We propose a framework for improving recommender systems through exploiting the users tagging activity. We maintain that web 2.0’s participative features can be exploited by adaptive web-based systems in order to enrich and extend the user model, improve social navigation and enrich information from a bottom-up perspective. Thus our approach stresses social annotation as a new and powerful kind of feedback and as a way to infer knowledge about users. The prototype implementation of our framework in the domain of cultural heritage is named iCITY. It is serving to demonstrate the validity of our approach and to highlight the benefits of this approach specifically for cultural heritage. iCITY is an adaptive, social, multi-device recommender guide that provides information about the cultural resources and events promoting the cultural heritage in the city of Torino. Our paper first describes this system and then discusses the results of a set of evaluations that were carried out at different stages of the systems development and aimed at validating the framework and implementation of this specific prototype. In particular, we carried out a heuristic evaluation and two sets of usability tests, aimed at checking the usability of the user interface, specifically of the adaptive behavior of the system. Moreover, we conducted evaluations aimed at investigating the role of tags in the definition of the user model and the impact of tags on the accuracy of recommendations. Our results are encouraging.

Assisted Navigation for Persons with Reduced Mobility: Path Recognition Through Particle Filtering (Condensation Algorithm)

In the few past decades, several international researchers have worked to develop intelligent wheelchairs for the people with reduced mobility. For many of these projects, the structured set of commands is based on a sensor-based command. Many types of commands are available but the final decision is to be made by the user. A former work established a behaviour-based multi-agent form of control ensuring that the user selects the best option for him/her in relation to his/her preferences or requirements. This type of command aims at “merging” this user and his/her machine—a kind of symbiotic relationship making the machine more amenable and the command more effective. In this contribution, the approach is based on a curve matching procedure to provide comprehensive assistance to the user. This new agent, using a modelization of the paths that are most frequently used, assists the user during navigation by proposing the direction to be taken when the path has been recognized. This approach will spare the user the effort of determining a new direction—which might be a major benefit in the case of severe disabilities. The approach considered uses particle filtering to implement the recognition of the most frequent paths according to a topological map of the environment.     

Dynamic adaptation of numerical attributes in a user profile

Recommender systems try to help users in their decisions by analyzing and ranking the available alternatives according to their preferences and interests, modeled in user profiles. The discovery and dynamic update of the users’ preferences are key issues in the development of these systems. In this work we propose to use the information provided by a user during his/her interaction with a recommender system to infer his/her preferences over the criteria used to define the decision alternatives. More specifically, this paper pays special attention on how to learn the user’s preferred value in the case of numerical attributes. A methodology to adapt the user profile in a dynamic and automatic way is presented. The adaptations in the profile are performed after each interaction of the user with the system and/or after the system has gathered enough information from several user selections. We have developed a framework for the automatic evaluation of the performance of the adaptation algorithm that permits to analyze the influence of different parameters. The obtained results show that the adaptation algorithm is able to learn a very accurate model of the user preferences after a certain amount of interactions with him/her, even if the preferences change dynamically over time.     

Efficient fuzzy full-text type-ahead search

Traditional information systems return answers after a user submits a complete query. Users often feel “left in the dark” when they have limited knowledge about the underlying data and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step toward solving this problem. In this paper, we study a new information-access paradigm, called “type-ahead search” in which the system searches the underlying data “on the fly” as the user types in query keywords. It extends autocomplete interfaces by allowing keywords to appear at different places in the underlying data. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental-search algorithms for both single-keyword queries and multi-keyword queries, using previously computed and cached results in order to achieve a high interactive speed. We develop novel techniques to support fuzzy search by allowing mismatches between query keywords and answers. We have deployed several real prototypes using these techniques. One of them has been deployed to support type-ahead search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency.     

Improving image tags by exploiting web search results

Automatic image tagging automatically assigns image with semantic keywords called tags, which significantly facilitates image search and organization. Most of present image tagging approaches are constrained by the training model learned from the training dataset, and moreover they have no exploitation on other type of web resource (e.g., web text documents). In this paper, we proposed a search based image tagging algorithm (CTSTag), in which the result tags are derived from web search result. Specifically, it assigns the query image with a more comprehensive tag set derived from both web images and web text documents. First, a content-based image search technology is used to retrieve a set of visually similar images which are ranked by the semantic consistency values. Then, a set of relevant tags are derived from these top ranked images as the initial tag set. Second, a text-based search is used to retrieve other relevant web resources by using the initial tag set as the query. After the denoising process, the initial tag set is expanded with other tags mined from the text-based search result. Then, an probability flow measure method is proposed to estimate the probabilities of the expanded tags. Finally, all the tags are refined using the Random Walk with Restart (RWR) method and the top ones are assigned to the query images. Experiments on NUS-WIDE dataset show not only the performance of the proposed algorithm but also the advantage of image retrieval and organization based on the result tags.