Abstract Knowledge Graphs (KGs) have already proven their strength as a source of high-quality information for different tasks such as data integration, search, text summarization, and personalization. Another prominent research field that has been benefiting from the adoption of KGs is that of Recommender Systems (RSs). Feeding a RS with data coming from a KG improves recommendation accuracy, diversity, and novelty, and paves the way to the creation of interpretable models that can be used for explanations. This possibility of combining a KG with a RS raises the question whether such an addition can be performed in a plug-and-play fashion – also with respect to the recommendation domain – or whether each combination needs a careful evaluation. To investigate such a question, we consider all possible combinations of (i) three recommendation tasks (books, music, movies); (ii) three recommendation models fed with data from a KG (and in particular, a semantics-aware deep learning model, that we discuss in detail), compared with three baseline models without KG addition; (iii) two main encyclopedic KGs freely available on the Web: DBpedia and Wikidata. Supported by an extensive experimental evaluation, we show the final results in terms of accuracy and diversity of the various combinations, highlighting that the injection of knowledge does not always pay off. Moreover, we show how the choice of the KG, and the form of data in it, affect the results, depending on the recommendation domain and the learning model.

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.

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.

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.

In the last few years, a renewed interest of the research community on conversational recommender systems (CRSs) is emerging. This is probably due to the great diffusion 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 messages. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they are still at an early stage on offering recommendation capabilities by using the conversational paradigm.

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).

The problem of finding commonalities characterizes several Knowledge Management scenarios involving collection of resources. The automatic extraction of shared features in a collection of resource descriptions formalized in accordance with a logic language has been in fact widely investigated in the past. In particular, with reference to Description Logics concept descriptions, Least Common Subsumers have been specifically introduced.

We propose a novel object discovery framework integrating the application layer of Bluetooth and RFID standards. The approach is motivated and illustrated in an innovative u-commerce setting. Given a request, it allows an advanced discovery process, exploiting semantically annotated descriptions of goods available in the u-marketplace. The RFID data exchange protocol and the Bluetooth service discovery protocol have been modified and enhanced to enable support for such semantic annotation of products. Modifications to the standards have been conceived to be backward compatible, thus allowing the smooth coexistence of the legacy discovery and/or identification features. Also noteworthy is the introduction of a dedicated compression tool to reduce storage/transmission problems due to the verbosity of XML-based semantic languages.

We propose a collaborative environment for semantic-enabled mobile devices (e.g. PDAs, cell phones, laptops) in peer to peer scenarios. Within the environment, resource discovery is performed exploiting technologies and techniques for knowledge representation developed for the semantic Web, which have been adapted to cope with the highly flexible structure of ad-hoc networks in ubiquitous computing. The approach exploits the standard Bluetooth stack, using the original UUID payload, to carry semantically annotated data. The environment is motivated and presented in a museum case study.

More and more resources are becoming available on the Web, and there is a growing need for infrastructures that, based on advertised descriptions, are able to semantically match demands with supplies.We formalize general properties a matchmaker should have, then we present a matchmaking facilitator, compliant with desired properties.The system embeds a NeoClassic reasoner, whose structural subsumption algorithm has been modified to allow match categorization into potential and partial, and ranking of matches within categories. Experiments carried out show the good correspondence between users and system rankings.

Article Free Access Share on The size of a revised knowledge base Authors: Marco Cadoli Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, Italy Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, ItalyView Profile , Francesco M. Donini Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, Italy Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, ItalyView Profile , Paolo Liberatore Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, Italy Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, ItalyView Profile , Marco Schaerf Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, Italy Dipartimento di Informatica e Sistemistica, Università di Roma 'La Sapienza', via Salaria 113, I-00198, Roma, ItalyView Profile Authors Info & Claims PODS '95: Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systemsMay 1995 Pages 151–162https://doi.org/10.1145/212433.220205Published:22 May 1995Publication History 12citation198DownloadsMetricsTotal Citations12Total Downloads198Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF