Recommender systems have demonstrated a significant impact across diverse domains, yet ensuring the reproducibility of experimental findings remains a persistent challenge.A primary obstacle lies in the fragmented and often opaque data management strategies employed during the preprocessing stage, where decisions about dataset selection, filtering, and splitting can substantially influence outcomes.To address these limitations, we introduce DataRec, an open-source Python-based library specifically designed to unify and streamline data handling in recommender system research.By providing reproducible routines for dataset preparation, data versioning, and seamless integration with other frameworks, DataRec promotes methodological standardization, interoperability, and comparability across different experimental setups.Our design is informed by an in-depth review of 55 stateof-the-art recommendation studies, ensuring that DataRec adopts best practices while addressing common pitfalls in data management.Ultimately, our contribution facilitates fair benchmarking, enhances reproducibility, and fosters greater trust in experimental results within the broader recommender systems community.The DataRec library, documentation, and examples are freely available at https://github.com/sisinflab/DataRec.

Recommender systems, though widely used, often struggle to engage users effectively. While deep learning methods have enhanced connections between users and items, they often neglect the user’s perspective. Knowledge-based approaches, utilizing knowledge graphs, offer semantic insights and address issues like knowledge graph embeddings, hybrid recommendation, and interpretable recommendation. More recently, neural-symbolic systems, combining data-driven and symbolic techniques, show promise in recommendation systems, especially when used with knowledge graphs. Moreover, content features become vital in conversational recommender systems, which demand multi-turn dialogues. Recent literature highlights increasing interest in this area, particularly with the emergence of Large Language Models (LLMs), which excel in understanding user queries and generating recommendations in natural language. Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop aims to disseminate advancements and discuss about challenges and opportunities.

Recommender systems have become ubiquitous in daily life, but their limitations in interacting with human users have become evident. Deep learning approaches have led to the development of data-driven algorithms that identify connections between users and items, but they often miss a critical actor in the loop - the end-user. Knowledge-based approaches are gaining attention due to the availability of knowledge-graphs, such as DBpedia and Wikidata, which provide semantics-aware information on different knowledge domains. These approaches are being used for recommendation and challenges such as knowledge graph embeddings, hybrid recommendation, and interpretable recommendation. Moreover, the emergence of neural-symbolic systems, which combine data-driven and symbolic methods, can significantly improve recommendation systems. A growing number of research papers on such topics demonstrate the growing interest and research potential of these systems. Furthermore, content features become crucial when interaction requires it. The development of conversational recommender systems presents new challenges, as they require multi-turn dialogues between users and systems, blurring the line between recommendation and retrieval. Evaluation of these systems goes beyond simple accuracy metrics and is hampered by the limited availability of datasets. While research and development into conversational recommender systems has been less prominent in the past, recent literature shows growing interest and potential for these systems.

Knowledge graphs (KG) have been proven to be a powerful source of side information to enhance the performance of recommendation algorithms. Their graph-based structure paves the way for the adoption of graph-aware learning models such as Graph Neural Networks (GNNs). In this respect, state-of-the-art models achieve good performance and interpretability via user-level combinations of intents leading users to their choices. Unfortunately, such results often come from and end-to-end learnings that considers a combination of the whole set of features contained in the KG without any analysis of the user decisions. In this paper, we introduce KGTORe, a GNN-based model that exploits KG to learn latent representations for the semantic features, and consequently, interpret the user decisions as a personal distillation of the item feature representations. Differently from previous models, KGTORe does not need to process the whole KG at training time but relies on a selection of the most discriminative features for the users, thus resulting in improved performance and personalization. Experimental results on three well-known datasets show that KGTORe achieves remarkable accuracy performance and several ablation studies demonstrate the effectiveness of its components. The implementation of KGTORe is available at: https://github.com/sisinflab/KGTORe.

Collaborative filtering models have undoubtedly dominated the scene of recommender systems in recent years. However, due to the little use of content information, they narrowly focus on accuracy, disregarding a higher degree of personalization. Meanwhile, knowledge graphs are arousing considerable interest in recommendation models thanks to their ability to enrich the system with content features that captures subtle user-item relations. Nevertheless, with many high-quality features, the models become more complex and challenging to train. We extend KGFlex [ 16 ], a hybrid model that analyzes historical data to understand the semantic features the user decisions depend on. KGFlex represents item features as embeddings, and it models user-item interactions as a factorized entropy-driven combination of the item attributes relevant to the user, thus reducing the complexity and raising the degree of personalization. The method does not neglect long tail items, reducing the popularity bias and ensuring a high level of fairness. The user-item prediction is mediated by the user’s personal views of the embeddings that grant a high degree of expressiveness. This extension analyzes different entropy measurement strategies, an enhanced user negative decision modeling, and assesses the fairness of KGFlex and the impact of its hyperparameters. KGFlex is available at https://split.to/kgflex .

In the last few years, a renewed interest of the research community in conversational recommender systems (CRSs) has been emerging. This is likely due to the massive proliferation of Digital Assistants (DAs) such as Amazon Alexa, Siri, or Google Assistant that are revolutionizing the way users interact with machines. DAs allow users to execute a wide range of actions through an interaction mostly based on natural language utterances. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they still remain at an early stage in terms of their recommendation capabilities via a conversation. In addition, we have been witnessing the advent of increasingly precise and powerful recommendation algorithms and techniques able to effectively assess users’ tastes and predict information that may be of interest to them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and neglect the huge amount of knowledge, both structured and unstructured, describing the domain of interest of a recommendation engine. Although very effective in predicting relevant items, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move in the direction of providing novel and diverse results as well as generating explanations for recommended items. Knowledge-aware side information becomes crucial when a conversational interaction is implemented, in particular for preference elicitation, explanation, and critiquing steps.

Recommender systems (RSs) have widely grown thanks to the outstanding capability of providing users with accurate and tailored recommendations. Recently, public awareness and new regulations forced RS researchers and practitioners to study solutions to user privacy endangerment. This tutorial will guide the attendees through the possible threats and the solutions towards private RSs.

The paper introduces Visual-Elliot (V-Elliot), a reproducibility framework for Visual Recommendation systems (VRSs) based on Elliot. framework provides the widest set of VRSs compared to other recommendation frameworks in the literature (i.e., 6 state-of-the-art models which have been commonly employed as baselines in recent works). The framework pipeline spans from the dataset preprocessing and item visual features loading to easily train and test complex combinations of visual models and evaluation settings. V-Elliot provides an extended set of features to ease the design, testing, and integration of novel VRSs into V-Elliot. The framework exploits of dataset filtering/splitting functions, 40 evaluation metrics, five hyper-parameter optimization methods, more than 50 recommendation algorithms, and two statistical hypothesis tests. The files of this demonstration are available at: github.com/sisinflab/elliot.

Deep Learning and factorization-based collaborative filtering recommendation models have undoubtedly dominated the scene of recommender systems in recent years. However, despite their outstanding performance, these methods require a training time proportional to the size of the embeddings and it further increases when also side information is considered for the computation of the recommendation list. In fact, in these cases we have that with a large number of high-quality features, the resulting models are more complex and difficult to train. This paper addresses this problem by presenting KGFlex: a sparse factorization approach that grants an even greater degree of expressiveness. To achieve this result, KGFlex analyzes the historical data to understand the dimensions the user decisions depend on (e.g., movie direction, musical genre, nationality of book writer). KGFlex represents each item feature as an embedding and it models user-item interactions as a factorized entropy-driven combination of the item attributes relevant to the user. KGFlex facilitates the training process by letting users update only those relevant features on which they base their decisions. In other words, the user-item prediction is mediated by the user’s personal view that considers only relevant features. An extensive experimental evaluation shows the approach’s effectiveness, considering the recommendation results’ accuracy, diversity, and induced bias. The public implementation of KGFlex is available at https://split.to/kgflex.

Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of proposed recommendation algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. Puzzled and frustrated by the continuous recreation of appropriate evaluation benchmarks, experimental pipelines, hyperparameter optimization, and evaluation procedures, we have developed an exhaustive framework to address such needs. Elliot is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple configuration file. The framework loads, filters, and splits the data considering a vast set of strategies (13 splitting methods and 8 filtering approaches, from temporal training-test splitting to nested K-folds Cross-Validation). Elliot(https://github.com/sisinflab/elliot) optimizes hyperparameters (51 strategies) for several recommendation algorithms (50), selects the best models, compares them with the baselines providing intra-model statistics, computes metrics (36) spanning from accuracy to beyond-accuracy, bias, and fairness, and conducts statistical analysis (Wilcoxon and Paired t-test).