Konstantinos Christidis

A topic-based recommender system for electronic marketplace platforms

A large number of items are described, and subsequently bought and sold every day in auction marketplaces across the web. The amount of information and the number of available items makes finding what to buy as well as describing an item to sell, a challenge for the participants. In this paper we consider two functions of electronic marketplaces. First, we address the recommendation of related items for users browsing the items offered in a marketplace. Second, in order to support potential sellers we propose the recommendation of relevant items and terms which can be used to describe an item to be sold in the marketplace. The contribution of this paper lies in the proposal of an innovative system that exploits the hidden topics of unstructured information found in the e-marketplace in order to support these functions. We propose a three-step process in which a probabilistic topic modelling approach is used in order to uncover latent topics that provide the basis for item and term similarity calculation for the corresponding recommendations. We present the design of our system and perform evaluations of the quality of the extracted topics as well as of the recommender system using real life scenarios using data extracted from a widely used auction marketplace. The evaluations demonstrate the perceived usefulness of our approach.

Using latent topics to enhance search and recommendation in Enterprise Social Software

Highlights? Application of latent topic models for search and recommendation in Enterprise Social Software. ? Generation and refinement of enterprise knowledge structures with latent topic models. ? Item to item collaborative and content based recommendations with Latent Dirichlet Allocation. Enterprise Social Software refers to open and flexible organizational systems and tools which utilize Web 2.0 technologies to stimulate participation through informal interactions. A challenge in Enterprise Social Software is to discover and maintain over time the knowledge structure of topics found relevant to the organization. Knowledge structures, ranging in formality from ontologies to folksonomies, support user activity by enabling users to categorize and retrieve information resources. In this paper we enhance the search and recommendation functionalities of Enterprise Social Software by extending their knowledge structures with the addition of underlying hidden topics which we discover using probabilistic topic models. We employ Latent Dirichlet Allocation in order to elicit hidden topics and use the latter to assess similarities in resource and tag recommendation as well as for the expansion of query results. As an application of our approach we have extended the search and recommendation facilities of an open source Enterprise Social Software system which we have deployed and evaluated in five knowledge-intensive small and medium enterprises.

Information market based recommender systems fusion

Recommender Systems have emerged as a way to tackle the overload of information reflected in the increasing volume of information artefacts in the web and elsewhere. Recommender Systems analyse existing information on the user activities in order to estimate future preferences. However, in real life situations, different types of information can be found and their interpretation can vary as well. Each recommender system implements a different approach for utilizing the known information and predicting the user preferences. A problem is that of blending the recommendations in an adaptive, intuitive way while performing better than base recommenders. In this work we propose an approach based on information markets for the fusion of recommender systems. Information Markets have unique characteristics that make them suitable building blocks for ensemble recommenders. We evaluate our approach with the Movielens and Netflix datasets and discuss the results of our experiments.

Supercharging Enterprise 2.0

Semantic and linked-data technologies are key to leveraging Enterprise 2.0. Integrating such technologies into a mainstream content management system can bring relevant information to employees, encourage innovation, and increase business performance.

Semantic Interfaces for Personal and Social Knowledge Work

A large number of tools has recently emerged supporting information management for individuals in their work context. Semantic technologies play an important role in the development of such tools as they facilitate advanced organization, annotation, navigation, and search capabilities. This study contributes to the design of such tools by outlining how a user-centred design methodology can be applied to develop usable and effective user interfaces. SPONGE, the resulting system, encapsulates core functionalities that are needed for managing personal information and for seamlessly sharing personal information within knowledge networks.

Using Probabilistic Topic Models in Enterprise Social Software

Enterprise social software (ESS) systems are open and flexible corporate environments which utilize Web 2.0 technologies to stimulate participation through informal interactions and aggregate these interactions into collective structures. A challenge in these systems is to discover, organize and manage the knowledge model of topics found within the enterprise. In this paper we aim to enhance the search and recommendation functionalities of ESS by extending their folksonomies and taxonomies with the addition of underlying topics through the use of probabilistic topic models. We employ Latent Dirichlet Allocation in order to elicit latent topics and use the latter to assess similarities in resource and tag recommendation as well as for the expansion of query results. As an application of our approach we extend the search and recommendation facilities of the Organik enterprise social system.

Combining activity metrics and contribution topics for software recommendations

In this paper we outline work in progress for the development of a recommender system for open source software development communities that takes into account information from multiple sources. Specifically our approach combines latent semantics of contributed information artifacts with quantitative metrics that indicate developer activity.

Exploring Gadget-Based Interfaces for the Social Semantic Desktop

A large number of tools has recently emerged supporting information management for individuals in their social context. Semantic technologies play an important role in the development of such tools because they allow for advanced organisation, annotation, navigation and search capabilities. In this paper we present SPONGE (Social and Personal Ontology-based Gadgets), a set of gadgets for representing and accessing information in the personal and social space of knowledge workers via cross-media and cross-application linking and browsing of information resources based on semantic web data structures, coupled with automated metadata generation support. In SPONGE, we aim to provide a richer, faster, and potentially lighter-touch way to build personal and social knowledge spaces than current desktop applications allow. Moreover, provisions are made to support ad-hoc collaboration between individuals and to enable seamless access to personal and shared resources.

A Topic-Based Recommender System for Electronic Marketplace Platforms

A large number of items are placed, bought and sold every day in auction marketplaces across the web. The amount of information and the number of available items makes finding what to buy, as well as describing an item to sell, a challenge for the participants. In this paper we propose a topic-based recommender system that exploits the latent semantics in the item descriptions in order to support the activities of buyers and sellers in auction electronic marketplaces. We present the design of our system and demonstrate how it can be used in real life scenarios.