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Venue recommendation strategies are built upon collaborative filtering techniques that rely on matrix factorisation (mf), to model users’ preferences. Various cross-domain strategies have been proposed to enhance the effectiveness of mf-based models on a target domain, by transferring knowledge from a source domain. Such cross-domain recommendation strategies often require user overlap, that is common users on the different domains. However, in practice, common users across different domains may not be available. To tackle this problem, recently, several cross-domains strategies without users’ overlaps have been introduced. In this paper, we investigate the performance of state-of-the-art cross-domain recommendation that do not require overlap of users for the venue recommendation task on three large location-based social networks (lbsn) datasets. Moreover, in the context of cross-domain recommendation we extend a state-of-the-art sequential-based deep learning model to boost the recommendation accuracy. Our experimental results demonstrate that state-of-the-art cross-domain recommendation does not clearly contribute to the improvements of venue recommendation systems, and, further we validate this result on the latest sequential deep learning-based venue recommendation approach. Finally, for reproduction purposes we make our implementations publicly available.keywordscross-domain recommendationvenue suggestiontransfer learning.

Pseudo test collections are automatically generated to provide training material for learning to rank methods. We propose a method for generating pseudo test collections in the domain of digital libraries, where data is relatively sparse, but comes with rich annotations. Our intuition is that documents are annotated to make them better findable for certain information needs. We use these annotations and the associated documents as a source for pairs of queries and relevant documents. We investigate how learning to rank performance varies when we use different methods for sampling annotations, and show how our pseudo test collection ranks systems compared to editorial topics with editorial judgements. Our results demonstrate that it is possible to train a learning to rank algorithm on generated pseudo judgments. In some cases, performance is on par with learning on manually obtained ground truth.

Word clouds are a summarised representation of a document’s text, similar to tag clouds which summarise the tags assigned to documents. Word clouds are similar to language models in the sense that they represent a document by its word distribution. In this paper we investigate the differences between word cloud and language modelling approaches, and specifically whether effective language modelling techniques also improve word clouds. We evaluate the quality of the language model using a system evaluation test bed, and evaluate the quality of the resulting word cloud with a user study. Our experiments show that different language modelling techniques can be applied to improve a standard word cloud that uses a TF weighting scheme in combination with stopword removal. Including bigrams in the word clouds and a parsimonious term weighting scheme are the most effective in both the system evaluation and the user study.

Motivated by the ever increasing difficulties faced by laypeople in retrieving and digesting valid and relevant information to make health-centred decisions, the CLEF eHealth lab series has offered shared tasks to the community in the fields of Information Extraction (IE), management, and Information Retrieval (IR) since 2013. These tasks have attracted large participation and led to statistically significant improvements in processing quality. In 2021, CLEF eHealth is calling for participants to contribute to the following two tasks: Task 1 on IE focuses on IE from noisy text. Participants will identify and classify Named Entities in written ultrasonography reports, containing misspellings and inconsistencies, from a major public hospital in Argentina. Identified entities will then have to be classified, which can be very challenging as it requires to handle lexical variations. Task 2 is a novel extension of the most popular and established task on consumer health search (CHS), aiming at retrieving relevant, understandable, and credible information for patients and their next-of-kins. In this paper we describe recent advances in the fields of IE and IR, and the subsequent offerings of this years CLEF eHealth lab challenges.