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This two-volume set LNCS 12656 and 12657 constitutes the refereed proceedings of the 43rd European Conference on IR Research, ECIR 2021, held virtually in March/April 2021, due to the COVID-19 pandemic. The 50 full papers presented together with 11 reproducibility papers, 39 short papers, 15 demonstration papers, 12 CLEF lab descriptions papers, 5 doctoral consortium papers, 5 workshop abstracts, and 8 tutorials abstracts were carefully reviewed and selected from 436 submissions. The accepted contributions cover the state of the art in IR: deep learning-based information retrieval techniques, use of entities and knowledge graphs, recommender systems, retrieval methods, information extraction, question answering, topic and prediction models, multimedia retrieval, and much more.

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.

We investigate a new approach for evaluating session-based information retrieval systems, based on Markov chains. In particular, we develop a new family of evaluation measures, inspired by random walks, which account for the probability of moving to the next and previous documents in a result list, to the next query in a session, and to the end of the session. We leverage this Markov chain to substitute what in existing measures is a fixed discount linked to the rank of a document or to the position of a query in a session with a stochastic average time to reach a document and the probability of actually reaching a given query. We experimentally compare our new family of measures with existing measures – namely, session DCG, Cube Test, and Expected Utility – over the TREC Dynamic Domain track, showing the flexibility of the proposed measures and the transparency in modeling the user dynamics.

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.

Monitoring user-system performance in interactive search is a challenging task. Traditional measures of retrieval evaluation, based on recall and precision, are not of any use in real time, for they require a priori knowledge of relevant documents. This paper shows how a Shannon entropy-based measure of user-system performance naturally falls in the framework of (interactive) probabilistic information retrieval. The value of entropy of the distribution of probability of relevance associated with the documents in the collection can be used to monitor search progress in live testing, to allow for example the system to select an optimal combination of search strategies. User profiling and tuning parameters of retrieval systems are other important applications.

The Reducing Online Misinformation through Credible Information Retrieval (ROMCIR) 2021 Workshop, as part of the satellite events of the 43rd European Conference on Information Retrieval (ECIR), is concerned with providing users with access to genuine information, to mitigate the information disorder phenomenon characterizing the current online digital ecosystem. This problem is very broad, as it concerns different information objects (e.g., Web pages, online accounts, social media posts, etc.) on different platforms, and different domains and purposes (e.g., detecting fake news, retrieving credible health-related information, reducing propaganda and hate-speech, etc.). In this context, all those approaches that can serve, from different perspectives, to tackle the credible information access problem, find their place.