Content-awareness and graph-based ranking for tag recommendation in folksonomies

Tag recommendation algorithms aid the social tagging process in many userdriven document indexing applications, such as social bookmarking and publication sharing websites. This thesis gives an overview of existing tag recommendation methods and proposes novel approaches that address the new document problem and the task of ranking tags. The focus is on graph-based methods such as Folk- Rank that apply weight spreading algorithms to a graph representation of the folksonomy. In order to suggest tags for previously untagged documents, extensions are presented that introduce content into the recommendation process as an additional information source. To address the problem of ranking tags, an in-depth analysis of graph models as well as ranking algorithms is conducted. Implicit assumptions made by the widely-used graph model of the folksonomy are highlighted and an improved model is proposed that captures the characteristics of the social tagging data more accurately. Additionally, issues in the tag rank computation of FolkRank are analysed and an adapted weight spreading approach for social tagging data is presented. Moreover, the applicability of conventional weight spreading methods to data from the social tagging domain is examined in detail. Finally, indications of implicit negative feedback in the data structure of folksonomies are analysed and novel approaches of identifying negative relationships are presented. By exploiting the three-dimensional characteristics of social tagging data the proposed metrics are based on stronger evidence and provide reliable measures of negative feedback. Including content into the tag recommendation process leads to a significant increase in recommendation accuracy on real-world datasets. The proposed adaptations to graph models and ranking algorithms result in more accurate and computationally less expensive recommenders. Moreover, new insights into the fundamental characteristics of social tagging data are revealed and a novel data interpretation that takes negative feedback into account is proposed

FolkRank++: An optimization of folkrank tag recommendation algorithm integrating user and item information

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance

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

Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites

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

The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems

Social bookmarking systems have established themselves as an important part in today’s Web. In such systems, tag recommender systems support users during the posting of a resource by suggesting suitable tags. Tag recommender algorithms have often been evaluated in offline benchmarking experiments. Yet, the particular setup of such experiments has rarely been analyzed. In particular, since the recommendation quality usually suffers from difficulties such as the sparsity of the data or the cold-start problem for new resources or users, datasets have often been pruned to so-called cores (specific subsets of the original datasets), without much consideration of the implications on the benchmarking results. In this article, we generalize the notion of a core by introducing the new notion of a set-core, which is independent of any graph structure, to overcome a structural drawback in the previous constructions of cores on tagging data. We show that problems caused by some types of cores can be eliminated using set-cores. Further, we present a thorough analysis of tag recommender benchmarking setups using cores. To that end, we conduct a large-scale experiment on four real-world datasets, in which we analyze the influence of different cores on the evaluation of recommendation algorithms. We can show that the results of the comparison of different recommendation approaches depends on the selection of core type and level. For the benchmarking of tag recommender algorithms, our results suggest that the evaluation must be set up more carefully and should not be based on one arbitrarily chosen core type and level