Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs

In the context of Social TV, the increasing popularity of first and second screen users, interacting and posting content online, illustrates new business opportunities and related technical challenges, in order to enrich user experience on such environments. SAM (Socializing Around Media) project uses Social Media-connected infrastructure to deal with the aforementioned challenges, providing intelligent user context management models and mechanisms capturing social patterns, to apply collaborative filtering techniques and personalized recommendations towards this direction. This paper presents the Context Management mechanism of SAM, running in a Social TV environment to provide smart recommendations for first and second screen content. Work presented is evaluated using real movie rating dataset found online, to validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging Technologies for Education. SETE 201

Recommender Systems based on Linked Data

Backgrounds: The increase in the amount of structured data published using the principles of Linked Data, means that now it is more likely to find resources in the Web of Data that describe real life concepts. However, discovering resources related to any given resource is still an open research area. This thesis studies Recommender Systems (RS) that use Linked Data as a source for generating recommendations exploiting the large amount of available resources and the relationships among them. Aims: The main objective of this study was to propose a recommendation tech- nique for resources considering semantic relationships between concepts from Linked Data. The specific objectives were: (i) Define semantic relationships derived from resources taking into account the knowledge found in Linked Data datasets. (ii) Determine semantic similarity measures based on the semantic relationships derived from resources. (iii) Propose an algorithm to dynami- cally generate automatic rankings of resources according to defined similarity measures. Methodology: It was based on the recommendations of the Project management Institute and the Integral Model for Engineering Professionals (Universidad del Cauca). The first one for managing the project, and the second one for developing the experimental prototype. Accordingly, the main phases were: (i) Conceptual base generation for identifying the main problems, objectives and the project scope. A Systematic Literature Review was conducted for this phase, which highlighted the relationships and similarity measures among resources in Linked Data, and the main issues, features, and types of RS based on Linked Data. (ii) Solution development is about designing and developing the experimental prototype for testing the algorithms studied in this thesis. Results: The main results obtained were: (i) The first Systematic Literature Re- view on RS based on Linked Data. (ii) A framework to execute and an- alyze recommendation algorithms based on Linked Data. (iii) A dynamic algorithm for resource recommendation based on on the knowledge of Linked Data relationships. (iv) A comparative study of algorithms for RS based on Linked Data. (v) Two implementations of the proposed framework. One with graph-based algorithms and other with machine learning algorithms. (vi) The application of the framework to various scenarios to demonstrate its feasibility within the context of real applications. Conclusions: (i) The proposed framework demonstrated to be useful for develop- ing and evaluating different configurations of algorithms to create novel RS based on Linked Data suitable to users’ requirements, applications, domains and contexts. (ii) The layered architecture of the proposed framework is also useful towards the reproducibility of the results for the research community. (iii) Linked data based RS are useful to present explanations of the recommen- dations, because of the graph structure of the datasets. (iv) Graph-based algo- rithms take advantage of intrinsic relationships among resources from Linked Data. Nevertheless, their execution time is still an open issue. Machine Learn- ing algorithms are also suitable, they provide functions useful to deal with large amounts of data, so they can help to improve the performance (execution time) of the RS. However most of them need a training phase that require to know a priory the application domain in order to obtain reliable results. (v) A log- ical evolution of RS based on Linked Data is the combination of graph-based with machine learning algorithms to obtain accurate results while keeping low execution times. However, research and experimentation is still needed to ex- plore more techniques from the vast amount of machine learning algorithms to determine the most suitable ones to deal with Linked Data

Degree Ranking Using Local Information

Most real world dynamic networks are evolved very fast with time. It is not feasible to collect the entire network at any given time to study its characteristics. This creates the need to propose local algorithms to study various properties of the network. In the present work, we estimate degree rank of a node without having the entire network. The proposed methods are based on the power law degree distribution characteristic or sampling techniques. The proposed methods are simulated on synthetic networks, as well as on real world social networks. The efficiency of the proposed methods is evaluated using absolute and weighted error functions. Results show that the degree rank of a node can be estimated with high accuracy using only $1\%$ samples of the network size. The accuracy of the estimation decreases from high ranked to low ranked nodes. We further extend the proposed methods for random networks and validate their efficiency on synthetic random networks, that are generated using Erd\H{o}s-R\'{e}nyi model. Results show that the proposed methods can be efficiently used for random networks as well

Executing, Comparing, and Reusing Linked Data-Based Recommendation Algorithms With the Allied Framework

International audienceData published on the Web following the Linked Data principles has resulted in a global data space called the Web of Data. These principles led to semantically interlink and connect different resources at data level regardless their structure, authoring, location, etc. The tremendous and continuous growth of the Web of Data also implies that now it is more likely to find resources that describe real-life concepts. However, discovering and recommending relevant related resources is still an open research area. This chapter studies recommender systems that use Linked Data as a source containing a significant amount of available resources and their relationships useful to produce recommendations. Furthermore, it also presents a framework to deploy and execute state-of-the-art algorithms for Linked Data that have been re-implemented to measure and benchmark them in different application domains and without being bound to a unique dataset