Social Search: retrieving information in Online Social Platforms -- A Survey

Social Search research deals with studying methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users' preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users' social profiles and behaviors (as they can be inferred from data available on social platforms) to optimize their information needs further

Social and Semantic Contexts in Tourist Mobile Applications

The ongoing growth of the World Wide Web along with the increase possibility of access information through a variety of devices in mobility, has defi nitely changed the way users acquire, create, and personalize information, pushing innovative strategies for annotating and organizing it. In this scenario, Social Annotation Systems have quickly gained a huge popularity, introducing millions of metadata on di fferent Web resources following a bottom-up approach, generating free and democratic mechanisms of classi cation, namely folksonomies. Moving away from hierarchical classi cation schemas, folksonomies represent also a meaningful mean for identifying similarities among users, resources and tags. At any rate, they suff er from several limitations, such as the lack of specialized tools devoted to manage, modify, customize and visualize them as well as the lack of an explicit semantic, making di fficult for users to bene fit from them eff ectively. Despite appealing promises of Semantic Web technologies, which were intended to explicitly formalize the knowledge within a particular domain in a top-down manner, in order to perform intelligent integration and reasoning on it, they are still far from reach their objectives, due to di fficulties in knowledge acquisition and annotation bottleneck. The main contribution of this dissertation consists in modeling a novel conceptual framework that exploits both social and semantic contextual dimensions, focusing on the domain of tourism and cultural heritage. The primary aim of our assessment is to evaluate the overall user satisfaction and the perceived quality in use thanks to two concrete case studies. Firstly, we concentrate our attention on contextual information and navigation, and on authoring tool; secondly, we provide a semantic mapping of tags of the system folksonomy, contrasted and compared to the expert users' classi cation, allowing a bridge between social and semantic knowledge according to its constantly mutual growth. The performed user evaluations analyses results are promising, reporting a high level of agreement on the perceived quality in use of both the applications and of the speci c analyzed features, demonstrating that a social-semantic contextual model improves the general users' satisfactio

Using Data Mining for Facilitating User Contributions in the Social Semantic Web

This thesis utilizes recommender systems to aid the user in contributing to the Social Semantic Web. In this work, we propose a framework that maps domain properties to recommendation technologies. Next, we develop novel recommendation algorithms for improving personalized tag recommendation and for recommendation of semantic relations. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on those systems

Unified entity search in social media community

ABSTRACT The search for entities is the most common search behavior on the Web, especially in social media communities where entities (such as images, videos, people, locations, and tags) are highly heterogeneous and correlated. While previous research usually deals with these social media entities separately, we are investigating in this paper a unified, multilevel, and correlative entity graph to represent the unstructured social media data, through which various applications (e.g., friend suggestion, personalized image search, image tagging, etc.) can be realized more effectively in one single framework. We regard the social media objects equally as "entities" and all of these applications as "entity search" problem which searches for entities with different types. We first construct a multi-level graph which organizes the heterogeneous entities into multiple levels, with one type of entities as vertices in each level. The edges between graphs pairwisely connect the entities weighted by intra-relations in the same level and inter-links across two different levels distilled from the social behaviors (e.g., tagging, commenting, and joining communities). To infer the strength of intrarelations, we propose a circular propagation scheme, which reinforces the mutual exchange of information across different entity types in a cyclic manner. Based on the constructed unified graph, we explicitly formulate entity search as a global optimization problem in a unified Bayesian framework, in which various applications are efficiently realized. Empirically, we validate the effectiveness of our unified entity graph for various social media applications on millionscale real-world dataset

개인화 검색 및 파트너쉽 선정을 위한 사용자 프로파일링

학위논문 (박사)-- 서울대학교 대학원 : 치의과학과, 2014. 2. 김홍기.The secret of change is to focus all of your energy not on fighting the old, but on building the new. - Socrates The automatic identification of user intention is an important but highly challenging research problem whose solution can greatly benefit information systems. In this thesis, I look at the problem of identifying sources of user interests, extracting latent semantics from it, and modelling it as a user profile. I present algorithms that automatically infer user interests and extract hidden semantics from it, specifically aimed at improving personalized search. I also present a methodology to model user profile as a buyer profile or a seller profile, where the attributes of the profile are populated from a controlled vocabulary. The buyer profiles and seller profiles are used in partnership match. In the domain of personalized search, first, a novel method to construct a profile of user interests is proposed which is based on mining anchor text. Second, two methods are proposed to builder a user profile that gather terms from a folksonomy system where matrix factorization technique is explored to discover hidden relationship between them. The objective of the methods is to discover latent relationship between terms such that contextually, semantically, and syntactically related terms could be grouped together, thus disambiguating the context of term usage. The profile of user interests is also analysed to judge its clustering tendency and clustering accuracy. Extensive evaluation indicates that a profile of user interests, that can correctly or precisely disambiguate the context of user query, has a significant impact on the personalized search quality. In the domain of partnership match, an ontology termed as partnership ontology is proposed. The attributes or concepts, in the partnership ontology, are features representing context of work. It is used by users to lay down their requirements as buyer profiles or seller profiles. A semantic similarity measure is defined to compute a ranked list of matching seller profiles for a given buyer profile.1 Introduction 1 1.1 User Profiling for Personalized Search . . . . . . . . 9 1.1.1 Motivation . . . . . . . . . . . . . . . . . . . 10 1.1.2 Research Problems . . . . . . . . . . . . . . 11 1.2 User Profiling for Partnership Match . . . . . . . . 18 1.2.1 Motivation . . . . . . . . . . . . . . . . . . . 19 1.2.2 Research Problems . . . . . . . . . . . . . . 24 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . 25 1.4 System Architecture - Personalized Search . . . . . 29 1.5 System Architecture - Partnership Match . . . . . . 31 1.6 Organization of this Dissertation . . . . . . . . . . 32 2 Background 35 2.1 Introduction to Social Web . . . . . . . . . . . . . . 35 2.2 Matrix Decomposition Methods . . . . . . . . . . . 40 2.3 User Interest Profile For Personalized Web Search Non Folksonomy based . . . . . . . . . . . . . . . . 43 2.4 User Interest Profile for Personalized Web Search Folksonomy based . . . . . . . . . . . . . . . . . . . 45 2.5 Personalized Search . . . . . . . . . . . . . . . . . . 47 2.6 Partnership Match . . . . . . . . . . . . . . . . . . 52 3 Mining anchor text for building User Interest Profile: A non-folksonomy based personalized search 56 3.1 Exclusively Yours' . . . . . . . . . . . . . . . . . . . 59 3.1.1 Infer User Interests . . . . . . . . . . . . . . 61 3.1.2 Weight Computation . . . . . . . . . . . . . 64 3.1.3 Query Expansion . . . . . . . . . . . . . . . 67 3.2 Exclusively Yours' Algorithm . . . . . . . . . . . . 68 3.3 Experiments . . . . . . . . . . . . . . . . . . . . . . 71 3.3.1 DataSet . . . . . . . . . . . . . . . . . . . . 72 3.3.2 Evaluation Metrics . . . . . . . . . . . . . . 73 3.3.3 User Profile Efficacy . . . . . . . . . . . . . 74 3.3.4 Personalized vs. Non-Personalized Results . 76 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . 80 4 Matrix factorization for building Clustered User Interest Profile: A folksonomy based personalized search 82 4.1 Aggregating tags from user search history . . . . . 86 4.2 Latent Semantics in UIP . . . . . . . . . . . . . . . 90 4.2.1 Computing the tag-tag Similarity matrix . . 90 4.2.2 Tag Clustering to generate svdCUIP and modSvdCUIP 98 4.3 Personalized Search . . . . . . . . . . . . . . . . . . 101 4.4 Experimental Evaluation . . . . . . . . . . . . . . . 103 4.4.1 Data Set and Experiment Methodology . . . 103 4.4.1.1 Custom Data Set and Evaluation Metrics . . . . . . . . . . . . . . . 103 4.4.1.2 AOL Query Data Set and Evaluation Metrics . . . . . . . . . . . . . 107 4.4.1.3 Experiment set up to estimate the value of k and d . . . . . . . . . . 107 4.4.1.4 Experiment set up to compare the proposed approaches with other approaches . . . . . . . . . . . . . . . 109 4.4.2 Experiment Results . . . . . . . . . . . . . . 111 4.4.2.1 Clustering Tendency . . . . . . . . 111 4.4.2.2 Determining the value for dimension parameter, k, for the Custom Data Set . . . . . . . . . . . . . . . 113 4.4.2.3 Determining the value of distinctness parameter, d, for the Custom data set . . . . . . . . . . . . . . . 115 4.4.2.4 CUIP visualization . . . . . . . . . 117 4.4.2.5 Determining the value of the dimension reduction parameter k for the AOL data set. . . . . . . . . . . . 119 4.4.2.6 Determining the value of distinctness parameter, d, for the AOL data set . . . . . . . . . . . . . . . . . . 120 4.4.2.7 Time to generate svdCUIP and modSvd-CUIP . . . . . . . . . . . . . . . . 122 4.4.2.8 Comparison of the svdCUIP, modSvd-CUIP, and tfIdfCUIP for different classes of queries . . . . . . . . . . 123 4.4.2.9 Comparing all five methods - Improvement . . . . . . . . . . . . . . 124 4.4.3 Discussion . . . . . . . . . . . . . . . . . . . 126 5 User Profiling for Partnership Match 133 5.1 Supplier Selection . . . . . . . . . . . . . . . . . . . 137 5.2 Criteria for Partnership Establishment . . . . . . . 140 5.3 Partnership Ontology . . . . . . . . . . . . . . . . . 143 5.4 Case Study . . . . . . . . . . . . . . . . . . . . . . 147 5.4.1 Buyer Profile and Seller Profile . . . . . . . 153 5.4.2 Semantic Similarity Measure . . . . . . . . . 155 5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . 160 5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . 162 6 Conclusion 164 6.1 Future Work . . . . . . . . . . . . . . . . . . . . . . 167 6.1.1 Degree of Personalization . . . . . . . . . . . 167 6.1.2 Filter Bubble . . . . . . . . . . . . . . . . . 168 6.1.3 IPR issues in Partnership Match . . . . . . . 169 Bibliography 170 Appendices 193 .1 Pairs of Query and target URL . . . . . . . . . . . 194 .2 Examples of Expanded Queries . . . . . . . . . . . 197 .3 An example of svdCUIP, modSvdCUIP, tfIdfCUIP 198Docto

Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

The Use of Social Tags in Text and Image Searching on the Web.

In recent years, tags have become a standard feature on a diverse range of sites on the Web, accompanying blog posts, photos, videos, and online news stories. Tags are descriptive terms attached to Internet resources. Despite the rapid adoption of tagging, how people use tags during the search process is not well understood. There is little empirical data on the use and perceptions of tags created by those other than the searcher. Previous research on tags focused on the motivations and behaviors of taggers, although non-taggers represent a larger proportion of Web users than taggers. This study examines how people use tags, created by others, during the search process. Forty-eight subjects were each assigned four search tasks in a within-subjects study. Subjects searched for text documents and images in a controlled laboratory setting, using information retrieval interfaces differing in their incorporation of tags. User behavior and perception data were collected through search logs and interviews. Both direct and indirect uses of tags across the search process were examined. Tags are used directly when they are clicked on, resulting in a new query, while tags are used indirectly when used for judgments of relevance or to obtain additional terms for query reformulation. Tags increased interactions with the information retrieval system, as subjects issued more queries and saw more search results when using the tagged interface. For both text and image searches, tags were used for query reformulation, predictive judgment, and evaluative judgment of relevance. Subjects interacted most frequently with tags on the search results page, using them for query reformulation and predictive judgment. Tags were more likely to be used for predictive judgment in text searches than in image searches. Subjects’ understanding of tags focused on the role of tags in search, especially findability through a search engine. Tags were not uniformly perceived as being user-generated; site owners and automatic generation were mentioned as sources of tags. Several implications for the design of search interfaces and presentation of tags to support information interactions are discussed in the conclusion.Ph.D.InformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89816/1/kimym_1.pd

Classification design : understanding the decisions between theory and consequence

Classification systems are systems of terms and term relationships intended to sort and gather like concepts and documents. These systems are ubiquitous as the substrate of our interactions with library collections, retail websites, and bureaucracies. Through their design and impact, classification systems share with other technologies an unavoidable though often ignored relationship to politics, power, and authority (Fleischmann & Wallace, 2007). Despite concern among scholars that classification systems embody values and bias, there is little work examining how these qualities are built into a classification system. Specifically, we do not adequately understand classification construction, in which classification designers make decisions by applying classification theory to the specific context of a project (Park, 2008). If systems embody values— particularly values that might either cause harm (Berman, 1971) or provide an additional means of communicating the creator’s position (Feinberg, 2007)— we must understand how and when the system takes on these qualities. This dissertation bridges critical classification theory with design-oriented classification theory. Where critical classification theory is concerned with the outcomes of classification system design, design-oriented classification theory is concerned with the correct processes by which to build a classification system. To connect the consequences of classification system design to designers’ methods and intentions, I use the research lens of infrastructure studies, particularly infrastructural inversion (Star & Ruhleder, 1996) or making visible the work behind infrastructures such as classification systems. Accordingly, my research focuses on designers’ decisions and rethinks our assumptions regarding the factors that classification designers consider in making their design decisions. I adopted an ethnographic approach to the study of classification design that would make visible design decisions and designers’ consideration of factors. Using this approach, I studied the daily design work of volunteer classification designers who maintain a curated folksonomy. Using the grounded theory method (Strauss & Corbin, 1998), I analyzed the designers’ decisions. My analysis identified the implications of the designers’ convergences and divergences from established classification methods for the character of the system and for the connection between classification theory and classification methods. I show how the factors—and the prioritization of factors—that these designers considered in making their decisions were consistent with the values and needs of the community. Therefore, I argue that classification designers have an important role in creating the values or bias of a classification system. In particular, designers’ divergence from universal guidelines and designers’ choices among sources of evidence represent opportunities to align a classification system to its community. I recommend that classification research focus on such instances of divergence and choice to understand the connection between classification design and the values of classification systems. The Introduction motivates the problem space around values in classification systems and outlines my approach in focusing on classification design. The Literature Review outlines the dominant theories in classification scholarship according to three elements of classification design: what decisions designers make, what information designers use in their decisions, and what skills designers apply to their decisions. In the Methods chapter, I introduce the site of my ethnographic research (The Fanwork Repository), detail my ethnographic methods, summarize the types of data I collected, and describe my grounded analysis. Three findings chapters examine one type of complex decision each: Names, Works, and Guidelines, respectively. In the fourth findings chapter, Synthesis, I define 10 factors designers considered across these complex design decisions. I then discuss how the factors figured into complex design decisions, how the factors overlapped and conflicted in design decisions, and how designers understood their role in making complex design decisions. In the Discussion chapter I connect the findings from the site of my ethnography to classification scholarship. In the Conclusion, I consider the contribution of examining classification systems as infrastructure, highlight the differences in accounts of classification design decisions made visible through classification theory and infrastructure studies approaches, and present suggestions for future research in classification design and the study of classification systems as infrastructure.Informatio

Personalized Recommender Systems for Resource-based Learning - Hybrid Graph-based Recommender Systems for Folksonomies

As the Web increasingly pervades our everyday lives, we are faced with an overload of information. We often learn on-the-job without a teacher and without didactically prepared learning resources. We not only learn on our own but also collaboratively on social platforms where we discuss issues, exchange information and share knowledge with others. We actively learn with resources we find on the Web such as videos, blogs, forums or wikis. This form of self-regulated learning is called resource-based learning. An ongoing challenge in technology enhanced learning (TEL) and in particular in resource-based learning, is supporting learners in finding learning resources relevant to their current needs and learning goals. In social tagging systems, users collaboratively attach keywords called tags to resources thereby forming a network-like structure called a folksonomy. Additional semantic information gained for example from activity hierarchies or semantic tags, form an extended folksonomy and provide valuable information about the context of the resources the learner has tagged, the related activities the resources could be relevant for, and the learning task the learner is currently working on. This additional semantic information could be exploited by recommender systems to generate personalized recommendations of learning resources. Thus, the first research goal of this thesis is to develop and evaluate personalized recommender algorithms for a resource-based learning scenario. To this end, the resource-based learning application scenario is analysed, taking an existing learning platform as a concrete example, in order to determine which additional semantic information could be exploited for the recommendation of learning resources. Several new hybrid graph-based recommender approaches are implemented and evaluated. Additional semantic information gained from activities, activity hierarchies, semantic tag types, the semantic relatedness between tags and the context-specific information found in a folksonomy are thereby exploited. The proposed recommender algorithms are evaluated in offline experiments on different datasets representing diverse evaluation scenarios. The evaluation results show that incorporating additional semantic information is advantageous for providing relevant recommendations. The second goal of this thesis is to investigate alternative evaluation approaches for recommender algorithms for resource-based learning. Offline experiments are fast to conduct and easy to repeat, however they face the so called incompleteness problem as datasets are limited to the historical interactions of the users. Thus newly recommended resources, in which the user had not shown an interest in the past, cannot be evaluated. The recommendation of novel and diverse learning resources is however a requirement for TEL and needs to be evaluated. User studies complement offline experiments as the users themselves judge the relevance or novelty of the recommendations. But user studies are expensive to conduct and it is often difficult to recruit a large number of participants. Therefore a gap exists between the fast, easy to repeat offline experiments and the more expensive user studies. Crowdsourcing is an alternative as it offers the advantages of offline experiments, whilst still retaining the advantages of a user-centric evaluation. In this thesis, a crowdsourcing evaluation approach for recommender algorithms for TEL is proposed and a repeated evaluation of one of the proposed recommender algorithms is conducted as a proof-of-concept. The results of both runs of the experiment show that crowdsourcing can be used as an alternative approach to evaluate graph-based recommender algorithms for TEL