Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

CLEF 2017 NewsREEL Overview: Offline and Online Evaluation of Stream-based News Recommender Systems

The CLEF NewsREEL challenge allows researchers to evaluate news recommendation algorithms both online (NewsREEL Live) and offline (News- REEL Replay). Compared with the previous year NewsREEL challenged participants with a higher volume of messages and new news portals. In the 2017 edition of the CLEF NewsREEL challenge a wide variety of new approaches have been implemented ranging from the use of existing machine learning frameworks, to ensemble methods to the use of deep neural networks. This paper gives an overview over the implemented approaches and discusses the evaluation results. In addition, the main results of Living Lab and the Replay task are explained

A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

Evaluation of recommender systems in streaming environments

Evaluation of recommender systems is typically done with finite datasets. This means that conventional evaluation methodologies are only applicable in offline experiments, where data and models are stationary. However, in real world systems, user feedback is continuously generated, at unpredictable rates. Given this setting, one important issue is how to evaluate algorithms in such a streaming data environment. In this paper we propose a prequential evaluation protocol for recommender systems, suitable for streaming data environments, but also applicable in stationary settings. Using this protocol we are able to monitor the evolution of algorithms' accuracy over time. Furthermore, we are able to perform reliable comparative assessments of algorithms by computing significance tests over a sliding window. We argue that besides being suitable for streaming data, prequential evaluation allows the detection of phenomena that would otherwise remain unnoticed in the evaluation of both offline and online recommender systems.Comment: Workshop on 'Recommender Systems Evaluation: Dimensions and Design' (REDD 2014), held in conjunction with RecSys 2014. October 10, 2014, Silicon Valley, United State

A large multilingual and multi-domain dataset for recommender systems

This paper presents a multi-domain interests dataset to train and test Recommender Systems, and the methodology to create the dataset from Twitter messages in English and Italian. The English dataset includes an average of 90 preferences per user on music, books, movies, celebrities, sport, politics and much more, for about half million users. Preferences are either extracted from messages of users who use Spotify, Goodreads and other similar content sharing platforms, or induced from their ”topical” friends, i.e., followees representing an interest rather than a social relation between peers. In addition, preferred items are matched with Wikipedia articles describing them. This unique feature of our dataset provides a mean to derive a semantic categorization of the preferred items, exploiting available semantic resources linked to Wikipedia such as the Wikipedia Category Graph, DBpedia, BabelNet and others