Price is a key determinant of user purchase behavior in e-commerce. As traditional recommendation techniques typically fail to account for price information, Price-Aware Recommender Systems have emerged in response, aiming to explicitly incorporate price as a core feature to enhance the overall quality and relevance of recommendations. Recent advances include session-based approaches such as CoHHN (Heterogeneous Hypergraphs), PASBR (Graph Neural Network), and the collaborative-filtering approach PUP (Graph Convolutional Networks). All reporting improved performance on their respective evaluation datasets. However, these methods have never been compared under controlled experimental conditions, limiting our understanding of their relative performance. This work addresses this gap through a reproducibility study over CoHHN, PASBR, and PUP. We successfully conducted the first cross-evaluation across six public datasets using standardized metrics (HR@20, MRR@20, NDCG@20). Our findings indicate that CoHHN provides the strongest and most consistent performance across evaluation metrics and datasets, while PASBR shows domain-limited competitiveness and PUP exhibits the weakest generalization, highlighting CoHHN's heterogeneous hypergraph architecture as the most robust framework for Price-Aware Recommendation. Source code available at https://anonymous.4open.science/r/reproducibility-sigir2026-E2CE.

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.

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.

Self-explaining models are becoming an important perk of recommender systems, as they help users understand the reason behind certain recommendations, which encourages them to interact more often with the platform. In order to personalize recommendations, modern approaches make the model aware of the user behavior history for interest evolution representation. However, existing explainable recommender systems do not consider the past user history to further personalize the explanation based on the user interest fluctuation. In this work, we propose a SEQuence-Aware Explainable Recommendation model (SEQUER) that is able to leverage the sequence of user-item review interactions to generate better explanations while maintaining recommendation performance. Experiments validate the effectiveness of our proposal on multiple recommendation scenarios. Our source code and preprocessed datasets are available at https://github.com/alarca94/sequer-recsys23.

Educational recommender systems channel most of the research efforts on the effectiveness of the recommended items. While teachers have a central role in online platforms, the impact of recommender systems for teachers in terms of the exposure such systems give to the courses is an under-explored area. In this paper, we consider data coming from a real-world platform and analyze the distribution of the recommendations w.r.t. the geographical provenience of the teachers. We observe that data is highly imbalanced towards the United States, in terms of offered courses and of interactions. These imbalances are exacerbated by recommender systems, which overexpose the country w.r.t. its representation in the data, thus generating unfairness for teachers outside that country. To introduce equity, we propose an approach that regulates the share of recommendations given to the items produced in a country (visibility) and the position of the items in the recommended list (exposure).

Rockfall detection is a crucial procedure in the field of geology, which helps to reduce the associated risks. Currently, geologists identify rockfall events almost manually utilizing point cloud and imagery data obtained from different caption devices such as Terrestrial Laser Scanner (TLS) or digital cameras. Multitemporal comparison of the point clouds obtained with these techniques requires a tedious visual inspection to identify rockfall events which implies inaccuracies that depend on several factors such as human expertize and the sensibility of the sensors. This paper addresses this issue and provides an intelligent framework for rockfall event detection for any individual working in the intersection of the geology domain and decision support systems. The development of such an analysis framework presents major research challenges and justifies exhaustive experimental analysis. In particular, we propose an intelligent system that utilizes multiple machine learning algorithms to detect rockfall clusters of point cloud data. Due to the extremely imbalanced nature of the problem, a plethora of state-of-the-art resampling techniques accompanied by multiple models and feature selection procedures are being investigated. Various machine learning pipeline combinations have been examined and benchmarked applying well-known metrics to be incorporated into our system. Specifically, we developed machine learning techniques and applied them to analyze point cloud data extracted from TLS in two distinct case studies, involving different geological contexts: the basaltic cliff of Castellfollit de la Roca and the conglomerate Montserrat Massif, both located in Spain. Our experimental results indicate that some of the above-mentioned machine learning pipelines can be utilized to detect rockfall incidents on mountain walls, with experimentally validated accuracy.

Recent observational studies highlight the importance of considering the interactions between users in the group recommendation process, but to date their integration has been marginal. In this article, we propose a collaborative model based on the social interactions that take place in a web-based conversational group recommender system. The collaborative model allows the group recommender to implicitly infer the different roles within the group, namely, collaborative and leader user(s). Moreover, it serves as the basis of several novel collaboration-based consensus strategies that integrate both individual and social interactions in the group recommendation process. A live-user evaluation confirms that our approach accurately identifies the collaborative and leader users in a group and produces more effective recommendations.

A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing (IC) is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user's soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns during the recommendation process, we propose two novel exponential Reinforcement Learning (RL) approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a Global Weighting (GW) approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches.