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As natural language interfaces enable users to express increasingly complex natural language queries, there is a parallel explosion of user review content that can allow users to better find items such as restaurants, books, or movies that match ...

Meetings are recurrent organizational tasks intended to drive progress in an interdisciplinary and collaborative manner. They are, however, prone to inefficiency due to factors such as differing knowledge among participants. The research goal of ...

To date, graph collaborative filtering (CF) strategies have been shown to outperform pure CF models in generating accurate recommendations. Nevertheless, recent works have raised concerns about fairness and potential biases in the recommendation ...

Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. ...

In this paper we propose a novel approach for combining first-stage lexical retrieval models and Transformer-based re-rankers: we inject the relevance score of the lexical model as a token in the middle of the input of the cross-encoder re-ranker. ...

The analysis of emotions expressed in text has numerous applications. In contrast to categorical analysis, focused on classifying emotions according to a pre-defined set of common classes, dimensional approaches can offer a more nuanced way to ...

In this paper, we hypothesize that sarcasm detection is closely associated with the emotion present in memes. Thereafter, we propose a deep multitask model to perform these two tasks in parallel, where sarcasm detection is treated as the primary ...

Check-worthiness detection is the task of identifying claims, worthy to be investigated by fact-checkers. Resource scarcity for non-world languages and model learning costs remain major challenges for the creation of models supporting multilingual ...

We consider the cross-market recommendation (CMR) task, which involves recommendation in a low-resource target market using data from a richer, auxiliary source market. Prior work in CMR utilised meta-learning to improve recommendation performance ...

The tourism industry is important for the benefits it brings and due to its role as a commercial activity that creates demand and growth for many more industries. Yet there is not much work on data science problems in tourism. Unfortunately, there ...

Data representation plays a crucial role in natural language processing (NLP), forming the foundation for most NLP tasks. Indeed, NLP performance highly depends upon the effectiveness of the preprocessing pipeline that builds the data ...

Recommender system research has oftentimes focused on approaches that operate on large-scale datasets containing millions of user interactions. However, many small businesses struggle to apply state-of-the-art models due to their very limited ...$\mathrm{}$

Depression detection from user-generated content on the internet has been a long-lasting topic of interest in the research community, providing valuable screening tools for psychologists. The ubiquitous use of social media platforms lays out the ...

Recently, recommendation algorithms based on Graph Convolution Network (GCN) have achieved many surprising results thanks to the ability of GCN to learn more efficient node embeddings. However, although GCN shows powerful feature extraction ...

Evaluation in Information Retrieval (IR) relies on post-hoc empirical procedures, which are time-consuming and expensive operations. To alleviate this, Query Performance Prediction (QPP) models have been developed to estimate the performance of a ...

Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the edges of a bipartite graph. However, in the absence of side ...

Multi-choice reading comprehension (MC-RC) is supposed to select the most appropriate answer from multiple candidate options by reading and comprehending a given passage and a question. Recent studies dedicate to catching the relationships within ...

Adverse phenomena such as the search engine manipulation effect (SEME), where web search users change their attitude on a topic following whatever most highly-ranked search results promote, represent crucial challenges for research and industry. ...

Lexical exact match systems that use inverted lists are a fundamental text retrieval architecture. A recent advance in neural IR, COIL, extends this approach with contextualized inverted lists from a deep language model backbone and performs ...

Retrieval studies often reuse TREC collections after the corresponding tracks have passed. Yet, a fair evaluation of new systems that retrieve documents outside the original judgment pool is not straightforward. Two common ways of dealing with ...

Playlists are a major way of interacting with music, as evidenced by the fact that streaming services currently host billions of playlists. In this content overload scenario, it is crucial to automatically characterise playlists, so that music can ...

Nowadays, search engine users commonly rely on query suggestions to improve their initial inputs. Current systems are very good at recommending lexical adaptations or spelling corrections to users’ queries. However, they often struggle to suggest ...

Scientific articles tend to follow a standardised discourse that enables a reader to quickly identify and extract useful or important information. We hypothesise that such structural conventions are strongly influenced by the scientific domain (...

In this paper, we address the detection of named entities in multilingual historical collections. We argue that, besides the multiple challenges that depend on the quality of digitization (e.g., misspellings and linguistic errors), historical ...

Disseminating and incorporating logic rules inspired by domain knowledge in Deep Neural Networks (DNNs) is desirable to make their output causally interpretable, reduce data dependence, and provide some human supervision during training to prevent ...

Inverse-propensity scoring and neural click models are two popular methods for learning rankers from user clicks that are affected by position bias. Despite their prevalence, the two methodologies are rarely directly compared on equal footing. In ...

We address the task of retrieving sentences for an open domain dialogue that contain information useful for generating the next turn. We propose several novel neural retrieval architectures based on dual contextual modeling: the dialogue context ...