Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations

New platform for application construction and execution

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 155-158).In today's world, computer application platforms are becoming increasingly important in providing positive application end-user and developer experiences. While there are many successful application platforms available, none are perfect or ideal. In this thesis, I describe the architecture, design and implementation of a new application platform I have developed called Web Machines which attempts to achieve exactly this goal: to be the ideal platform for application construction and execution. In order to better define an ideal application platform, I develop a set of 20 different specific goal criteria for platform end-user experience, developer experience, and administrator experience, and analyze how five different currently successful application platforms achieve those goals. These goals and platform analyses help motivate the architecture and design of Web Machines. The end result is a hybrid remote server/local client application platform that supports construction and execution of applications called Machines. Machine backend logic can be executed either remotely on registered Machine servers, or locally on a user's own computer, and Machine frontend interfaces are rendered and used in a rich, desktop-like MDI web application called the Web Machines environment. Web Machines also provides several different powerful methods for constructing Machines, including both traditional programming code-based approaches, and novel, totally visual approaches which center around drawing applications together as graphs of other applications and components.(cont.) This design, in addition to meeting almost all of the defined ideal application platform goals, makes significant novel and innovative contributions to the application platform field with some exciting new platform concepts: the ability to visually compose and program together arbitrary user-generated applications, components, and other compositions; implicit, high-level parallelization of composed application components, each of which may be running remotely at different locations across the internet; support for both remote and local application execution; semantic definition and understanding of application inputs and outputs; and the ability to drive one application's control input ("clicks and keypresses") with the data output of another.by Robert A. Bryant.M.Eng

Connections in Music

PhDThis work is copyright (c) 2010 Kurt Jacobson, and is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported Licence. To view a copy of this licence, visit http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.Connections between music artists or songs provide a context and lineage for music and form the basis for recommendation, playlist generation, and general navigation of the musical universe. We examine the structure of the connections between music artists found on the web. It is shown that different methods of finding associations between artists yeild different net- work structures - the details of associations and how these associations are discovered impact the global structure of the artist network. This realization informs our associations framework - based on seman- tic web technologies and centered around a small RDF/OWL ontology that emphasizes the provenance and transparency of association statements. We develop the MuSim Similarity Ontology and show how, combined with the concepts of linked data, it can be used to create a distributed web-scale ecosystem for music similarity. The Similarity Ontology is evaluated against psychological models for similarity and shown to be flexible enough to accommodate each model examined. Several applications are developed based on the visualization of music artist network structures and the utilization of our associations framework along with other music-related linked data

Visual exploration of semantic-web-based knowledge structures

Humans have a curious nature and seek a better understanding of the world. Data, in- formation, and knowledge became assets of our modern society through the information technology revolution in the form of the internet. However, with the growing size of accumulated data, new challenges emerge, such as searching and navigating in these large collections of data, information, and knowledge. The current developments in academic and industrial contexts target the corresponding challenges using Semantic Web techno- logies. The Semantic Web is an extension of the Web and provides machine-readable representations of knowledge for various domains. These machine-readable representations allow intelligent machine agents to understand the meaning of the data and information; and enable additional inference of new knowledge. Generally, the Semantic Web is designed for information exchange and its processing and does not focus on presenting such semantically enriched data to humans. Visualizations support exploration, navigation, and understanding of data by exploiting humans’ ability to comprehend complex data through visual representations. In the context of Semantic- Web-Based knowledge structures, various visualization methods and tools are available, and new ones are being developed every year. However, suitable visualizations are highly dependent on individual use cases and targeted user groups. In this thesis, we investigate visual exploration techniques for Semantic-Web-Based knowledge structures by addressing the following challenges: i) how to engage various user groups in modeling such semantic representations; ii) how to facilitate understanding using customizable visual representations; and iii) how to ease the creation of visualizations for various data sources and different use cases. The achieved results indicate that visual modeling techniques facilitate the engagement of various user groups in ontology modeling. Customizable visualizations enable users to adjust visualizations to the current needs and provide different views on the data. Additionally, customizable visualization pipelines enable rapid visualization generation for various use cases, data sources, and user group

Debugging scripts in SPipes editor

Jazyk SPipes je technologie umožňující zpracování strukturovaných dat Sémantického webu. Tato diplomová práce se zabývá zlepšením stavu stávajícího editoru SPipes skriptů. V práci jsou nejprve představeny principy Sémantického webu a relevantní technologie. Na základě zevrubné analýzy již existujícího editoru a provedené rešerše byla navržena úprava architektury aplikace a definovány funkční a nefunkčí požadavky na editor. Hlavní přínosy práce jsou převedení backendové části z jazyka Scala do Javy za účelem odstranění problémů vyplývajících z nekompatibility mezi jazykem Scala a Spring frameworkem, který je použit. Dále pak vytvoření testů, které zjednodušují odhalení potenciálních chyb v aplikaci, rozdělení původně monolytické aplikace na několik oddělených služeb využívajících Docker a docker-compose, čímž se výrazně sníží práce spojená se správnou konfigurací a spouštěním aplikace. V neposlední řadě přináší tato práce nové a netriviální funkce editoru - možnost validovat a ladit editované skripty a moduly.The SPipes language is a technology that enables the processing of structured data in the form of the Semantic Web. This thesis attempts to improve the existing SPipes script editor. The thesis first introduces the principles of the Semantic Web and related technologies. Based on a thorough analysis of the existing editor and conducted survey, the application architecture was redesigned and functional and non-functional requirements for the editor were defined. Main contributions of this work are re-implementation of the backend part from Scala to Java, which eliminates the compatibility issues arising from the incompatibility between Scala and the Spring framework that is used. Special attention was paid to writing tests for most parts of the application, which simplifies the detection of potential bugs in the application. Major change in architecture was to split the originally monolithic application into several separate services with the use of Docker and docker-compose, leading to simpler configuration and easier deployment of the application. Last but not least, this thesis introduces new non-trivial features of the editor - the capability of validating and debugging of SPipes scripts and modules

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges