Spatiotemporal user and place modelling on the geo-social web

Users of Location-Based Social Networks (LBSN) are giving away information about their whereabouts, and their interactions in the geographic space. In comparison to other types of personal data, location data are sensitive and can reveal user’s daily routines, activities, experiences and interests in the physical world. As a result, the user is facing an information overload that overburdens him to make a satisfied decision on where to go or what to do in a place. Thus, finding the matching places, users and content is one of the key challenges in LSBNs. This thesis investigates the different dimensions of data collected on LBSNs and proposes a user and place modelling framework. In particular, this thesis proposes a novel approach for the construction of different views of personal user profiles that reflect their interest in geographic places, and how they interact with geographic places. Three novel modelling frameworks are proposed, the static user model, the dynamic user model and the semantic place model. The static user model is a basic model that is used to represent the overall user interactions towards places. On the other hand, the dynamic user model captures the change of the user’s preferences over time. The semantic place model identifies user activities in places and models the relationships between places, users, implicit place types, and implicit activities. The proposed models demonstrate how geographic place characteristics as well as implicit user interactions in the physical space can further enrich the user profiles. The enrichment method proposed is a novel method that combines the semantic and the spatial influences into user profiles. Evaluation of the proposed methods is carried out using realistic data sets collected from the Foursquare LBSN. A new Location and content recommendation methods are designed and implemented to enhance existing location recommendation methods and results showed the usefulness of considering place semantics and the time dimension when the proposed user profiles in recommending locations and content. The thesis considers two further related problems; namely, the construction of dynamic place profiles and computing the similarity between users on LBSN. Dynamic place profiles are representations of geographic places through users’ interaction with the places. In comparison to static place models represented in gazetteers and map databases, these place profiles provide a dynamic view of how the places are used by actual people visiting and interacting with places on the LBSN. The different views of personal user profiles constructed within our framework are used for computing the similarity between users on the LBSN. Temporal user similarities on both the semantic and spatial levels are proposed and evaluated. Results of this work show the challenges and potential of the user data collected on LBSN

User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration

Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks. Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion. Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data

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

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

AXMEDIS 2008

The AXMEDIS International Conference series aims to explore all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, protection and rights management, to address the latest developments and future trends of the technologies and their applications, impacts and exploitation. The AXMEDIS events offer venues for exchanging concepts, requirements, prototypes, research ideas, and findings which could contribute to academic research and also benefit business and industrial communities. In the Internet as well as in the digital era, cross-media production and distribution represent key developments and innovations that are fostered by emergent technologies to ensure better value for money while optimising productivity and market coverage

Follow-back Recommendations for Sports Bettors: A Twitter-based Approach

Social network based recommender systems are powered by a complex web of social discussions and user connections. Short text microblogs e.g. Twitter present powerful frameworks for information consumption, due to their real-time nature in content throughput as well as user connections. Therefore, users on such platforms consume the disseminated content to a greater or lesser extent based on their interests. Quantifying this degree of interest is a difficult task based on the amount of information that such platforms generate at any given time. Thus, the generation of personalized profiles based on the Degree of Interest (DoI) that users have towards certain topics in such short texts presents a research problem. We address this challenge by following a two-step process in generation of personalized sports betting related user profiles in tweets as a case study. We (i) compute the Degree of Interest in Sports Betting (DoiSB) of tweeters and (ii) affirm this DoiSB by correlating it with their friendship network. This is an integral process in the design of a short text based recommender systems for users to follow i.e follow-back recommendations as well as content-based recommendations relying on the interests of users on such platforms. In this paper, we described the DoiSB computation and follow-back recommendation process by building a vector representation model for tweets. We then use this model to profile users interested in sports betting. Experiments using real Twitter dataset geolocated to Kenya shows the effectiveness of our approach in the identification of tweeter\u27s DoiSBs as well as their correlation with their friendship network