Maria Salamó

Self-explaining recommenders enhance user trust by providing justifications for their suggestions. Sequence-aware models have advanced the field by leveraging user interaction history to personalize recommendations and explanations. However, generative models often struggle with sparse data, producing repetitive or irrelevant explanations. This paper explores the optimal methods for infusing rich textual information from past user interactions directly into the item embeddings to feed a user reasoning path leading to personalized explanations. We conduct a comprehensive analysis of various techniques, including: (1) multiple text aggregation strategies to pool fine-grained attributed item opinions into user-aggregated item text representations; (2) several fusion mechanisms to combine text and collaborative modalities, from early fusion to a late fusion approach within the Transformer architecture; and (3) different training regimes for explanation generation. Experiments on three real-world datasets demonstrate which steps to follow in order to successfully leverage textual information into a sequence-aware explainable recommendation model and boost recommendation performance as well as explanation quality.

In this paper, we propose a novel metaheuristic algorithm called Forgetful Swarm Optimization (FSO) for Astronomical Observation Scheduling (AOS), a type of combinatorial optimization problem defined by the tasks and constraints assigned to the telescopes and other devices involved in astrophysical research. FSO combines local optimization, Destroy and Repair, and Swarm Intelligence methodologies to create a flexible and scalable global optimization algorithm to handle the challenges of AOS. The proposal is adapted to the well-justified scenarios of the Ariel Space Mission problem, a particular example of AOS, and compared with previous algorithms that are applied to it including an Evolutionary Algorithm (EA), an Iterated Local Search (ILS), a multi-start metaheuristic, a Tabu Search, and a Hill-Climbing greedy algorithm. The experimental evaluation demonstrates that FSO consistently outperforms other algorithms in objective completeness, up to 8.4% on average, for all instances of the problem regardless of dimensions and complexity. Additionally, it has significantly less computational cost than ILS and the base models of a global optimization algorithm such as EA.

One way to increase trust among users towards recommender systems is to provide the recommendation along with a textual explanation. In the literature, extraction-based, generation-based, and, more recently, hybrid solutions based on retrieval-augmented generation have been proposed to tackle the problem of text-based explainable recommendation. However, the use of different datasets, preprocessing steps, target explanations, baselines, and evaluation metrics complicates the reproducibility and state-of-the-art assessment of previous work among different model categories for successful advancements in the field. Our aim is to provide a comprehensive analysis of text-based explainable recommender systems by setting up a well-defined benchmark that accommodates generation-based, extraction-based, and hybrid approaches. Also, we enrich the existing evaluation of explainability and text quality of the explanations with a novel definition of feature hallucination. Our experiments on three real-world datasets unveil hidden behaviors and confirm several claims about model patterns. Our source code and preprocessed datasets are available at https://github.com/alarca94/text-exp-recsys24.

Nowadays a recommendation model should exploit additional information from both the user and item perspectives, in addition to utilizing user-item interaction data. Datasets are central in offering the required information for evaluating new models or algorithms. Although there are many datasets in the literature with user and item properties, there are several issues not covered yet: (i) it is difficult to perform cross-analysis of properties at user and item level as they are not related in most cases; and (ii) on top of that, in many occasions datasets do not allow analysis at different granularity levels. In this paper, we propose a new dataset in the music domain, named AMBAR, that tackles the above-mentioned issues. Besides detailing in depth the structure of the new dataset, we also show its application in contexts (i.e., multi-objective, fair, and calibrated recommendations) where both the effectiveness and the beyond-accuracy perspectives of recommendation are assessed.

Provider fairness aims at regulating the recommendation lists, so that the items of different providers/provider groups are suggested by respecting notions of equity. When group fairness is among the goals of a system, a common way is to use coarse groups since the number of considered provider groups is usually small (e.g., two genders, or three/four age groups) and the number of items per group is large. From a practical point of view, having few groups makes it easier for a platform to manage the distribution of equity among them. Nevertheless, there are sensitive attributes, such as the age or the geographic provenance of the providers that can be characterized at a fine granularity (e.g., one might group providers at the country level, instead of the continent one), which increases the number of groups and decrements the number of items per group. In this study, we show that, in large demographic groups, when considering coarse-grained provider groups, the fine-grained provider groups are under-recommended by the state-of-the-art models. To overcome this issue, in this paper, we present an approach that brings equity to both coarse and fine-grained provider groups. Experiments on two real-world datasets show the effectiveness of our approach.