Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS 2024)

Abstract

This is the preface for the Proceedings of the Sixth Knowledge-Aware and Conversational Recommender Systems Workshop (KaRS 2024), co-located with the 18th ACM RecSys 2024 conference.

Keywords

• eol>recommender systems
• knowledge-aware
• conversational
• workshop
• proceedings

1. Introduction

In this volume, we present the contributions showcased at the Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS 2024), co-located with the 18th ACM Conference on Recommender Systems (RecSys 2024) [ 1 ], which took place in Bari, Italy. Since its inaugural edition in Vancouver (Canada) in 2018 [2, 3], KaRS has evolved into a premier forum for discussing the integration of knowledge representation and conversational systems in recommendation. Over the years, KaRS has been co-located with major conferences in various cities such as Beijing (2019) [4, 5], Amsterdam (2021) [6, 7], Seattle (2022) [8, 9], and Singapore (2023) [ 10, 11 ].

Recommender systems are now ubiquitous across a variety of domains, from e-commerce to media content suggestions, and play a pivotal role in enhancing online user experiences. Despite their widespread adoption, these systems face challenges in engaging efectively with human users [ 12 ]. While data-driven algorithms have proven successful in uncovering patterns in user-item interactions [ 13, 14 ], they often fail to fully account for the central actor in this loop: the end-user.

A key aspect of user behavior that is frequently underrepresented in recommendation systems is the use of domain-specific knowledge. Fortunately, Knowledge-aware Recommender Systems are gaining increasing attention within the recommendation community. By leveraging structured domain knowledge represented in ontologies or Knowledge Graphs (KGs), these systems can model semantic relationships between users, items, and entities, ofering more personalized and relevant recommendations. Although knowledge-aware approaches have existed for over two decades [ 15, 16, 17, 18, 19 ], their significance has been revitalized by the Linked Open Data initiative1 and the growing availability of large knowledge repositories such as DBpedia2 and Wikidata3. This renewed interest is evident in workshops and conferences such as ISWC, ACM RecSys, UMAP, AAAI, ECAI, IJCAI, and SIGIR. Linked data underpins many modern approaches in areas such as Knowledge Graph embeddings [ 20, 21, 22, 23 ], hybrid recommendation [ 18, 24 ], link prediction [ 25, 26, 27, 28, 29 ], knowledge transfer [ 13 ], interpretable recommendation [ 30, 31, 18 ], and user modeling [ 32, 33, 34, 35, 19 ], even in distributed and privacy-oriented architectures [ 36, 37 ].

Additionally, recent advances have brought neuro-symbolic systems to the forefront, integrating data-driven methodologies with symbolic reasoning [ 38 ]. This fusion of machine learning, which excels at leveraging data, with symbolic approaches, adept at understanding knowledge, holds great promise for improving recommendation quality, especially in data-sparse environments [ 39 ].

Parallel to these developments, Conversational Recommender Systems (CRSs) [ 40 ] have gained momentum by enhancing the quality of interactions between users and systems, particularly in multi-turn dialogues [ 41 ]. CRSs introduce challenges such as incorporating both shortand long-term preferences, dynamically adapting to user feedback, and navigating limitations in available datasets [ 42 ]. The rise of Large Language Models (LLMs) has revitalized CRSs, enabling more natural and efective user interactions. LLMs, with their advanced capabilities in natural language understanding, have significantly impacted the ability of CRSs to process complex user queries and generate meaningful recommendations [ 42 ]. Moreover, LLMs enhance CRSs’ adaptability and responsiveness by continuously learning from user interactions, thus improving the overall user experience.

This year’s KaRS continues to reflect the growing convergence of knowledge-aware and conversational recommender systems, with a focus on neuro-symbolic approaches and the application of LLMs to enhance personalized and interactive recommendation experiences. We are proud to present an impressive range of contributions, each peer-reviewed by a rigorous program committee, ensuring the high quality of this year’s workshop and solidifying KaRS as a key platform for discussing the future of these exciting technologies.

2. Background and Goals

Recommender systems have become an integral part of everyday life, powering applications in diverse fields such as e-commerce, entertainment, and content curation. However, despite their widespread use, many systems still face challenges when it comes to engaging users efectively [ 12 ]. While deep learning techniques have significantly improved the ability to uncover latent relationships between users and items [ 43 ], they often fail to fully capture the user’s perspective and feature relevance in the recommendation process [ 44 ]. 1http://linkeddata.org 2https://dbpedia.org 3https://wikidata.org

A key approach to overcoming these limitations lies in knowledge-based methods [ 15, 16, 17, 18 ]. These techniques utilize knowledge graphs and ontologies to model the relationships between users, items, and other entities in a domain. Large knowledge graphs, such as DBpedia4 and Wikidata5, have played a crucial role in revitalizing interest in these methods. Recent advances focus on knowledge graph embeddings [ 20, 21, 22, 27, 23 ], hybrid recommendation systems [ 18, 45, 46 ], link prediction [ 25, 26, 27, 47, 48, 28, 29 ], and interpretable recommendation [ 30, 31, 18 ].

Moreover, a new wave of research is emerging through neuro-symbolic systems, which integrate data-driven machine learning techniques with symbolic reasoning [ 38 ]. These approaches hold promise for improving recommendations, particularly in scenarios where training data is sparse, by efectively leveraging both data and knowledge [ 39 ].

Conversational Recommender Systems (CRSs) represent another important advancement in this space. CRSs engage users through multi-turn dialogues, allowing the system to gather more detailed preferences and adjust recommendations dynamically [ 40, 41 ]. The conversational nature of these systems introduces unique challenges, such as balancing short- and long-term preferences, adapting quickly to user feedback, and developing evaluation metrics that go beyond accuracy [ 42 ]. Despite limited dataset availability often due to privacy requirements [ 42, 49 ], recent research into CRSs has been rapidly growing [ 41, 50 ]. The advent of Large Language Models (LLMs) is set to further transform this field. LLMs bring sophisticated natural language understanding capabilities that can greatly enhance the conversational experience, allowing systems to process complex user queries and provide more intuitive and relevant recommendations.

In light of these advances, the Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop aims to serve as a platform for sharing recent research and exploring future directions in knowledge-aware and conversational recommender systems. This edition will place particular emphasis on the integration of LLMs, neuro-symbolic methodologies, and knowledge-based approaches, highlighting their potential to address the challenges and opportunities in this rapidly evolving domain.

2.1. Objectives

The Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop [ 10, 8, 6, 4, 2 ] is not just another academic event centered on the latest advancements in recommendation algorithms. Instead, its primary goal is to inspire a new wave of research focused on enhancing user experience, engagement, and satisfaction [51], rather than solely emphasizing algorithmic accuracy [ 12 ].

By integrating diverse expertise from fields such as Machine Learning, Human-Computer Interaction, Information Retrieval, and Information Systems, this workshop seeks to drive forward innovative research directions.

KaRS provides a vibrant platform for researchers, practitioners, and industry professionals to not only share their latest findings but also to identify emerging trends, outline future challenges, and explore opportunities for research and development. By encouraging active participation 4https://dbpedia.org 5https://wikidata.org and idea-sharing, KaRS fosters the growth of an interdisciplinary community dedicated to knowledge-aware and conversational recommender systems, while also embracing cutting-edge topics such as Large Language Models (LLMs) and neuro-symbolic approaches, which further expand the scope of this year’s edition.

2.2. Topics

Topics of interests include, but are not limited to:

• Models and Feature Engineering: Data models based on structured knowledge sources (e.g., Linked Open Data, Wikidata, BabelNet, etc.), Neuro-Symbolic approaches to recommendation, Semantics-aware approaches exploiting the analysis of textual sources (e.g., Wikipedia, Social Web, etc.), Knowledge-aware user modeling, Methodological aspects (evaluation protocols, metrics, and datasets), Logic-based modeling of a recommendation process, Knowledge Representation and Automated Reasoning for recommendation, Deep learning methods to model semantic features, Large Language Models for Conversational Recommenders • Beyond-Accuracy Recommendation Quality: Using knowledge bases and knowledge graphs to increase recommendation quality (e.g., in terms of novelty, diversity, serendipity, or explainability), Explainable Recommender Systems, Knowledge-aware explanations (compliant with the GDPR) • Online Studies: Knowledge sources for cross-lingual recommendations, Applications of knowledge-aware recommenders (e.g., music or news recommendation, of-mainstream application areas), User studies (e.g., on the user’s perception of knowledge-based recommendations), field studies • Design of a Conversational Agent: Design and implementation methodologies, Dialogue management (end-to-end, dialogue-state-tracker models), UX design, Dialogue protocol design • User Modeling and Interfaces: Critiquing and user’s feedback exploitation, Shortand Long-term user profiling and modeling, Preference elicitation, Natural language, multimodal, and voice-based interfaces, Next-question problem • Methodological and Theoretical aspects: Evaluation and metrics, Datasets, Theoretical aspects of conversational recommender systems

3. Program

The program of the half-day workshop consisted of:

• a session with the presentation of three papers on Content-based and Knowledge-aware

Recommender Systems; • a session with the presentation of two papers on Large Language Models for recommendation; • a keynote by Nicola Ferro (Full Professor in Computer Science at the Department of Information Engineering at the University of Padua, Italy) on the Dagstuhl CAFE Framework for the evaluation of Conversational Agents: Ferro discussed the evaluation of conversational agents used in Information Retrieval (IR) and Recommender Systems (RS), emphasizing challenges like personalization, veracity, bias, and trustworthiness; he presented insights from the Dagstuhl Perspectives Workshop on the topic, proposing evaluation methods to address these issues, especially with the rise of Large Language Models; • a session with the presentation of three papers on Conversational Recommender Systems.

4. Website & Proceedings

All workshop material including schedule and news will be found on the 2024 workshop website at https://kars-workshop.github.io/2024/.

5. Program Committee

We thank the members of the Program Committee of KaRS 2024 for their thorough reviews and the detailed feedback they gave to the authors. The PC comprised people from diferent countries and spanning various levels of seniority: Nourah AlRossais (King Saud University), Vito Walter Anelli (Polytechnic University of Bari), Marco Angelini (Sapienza University of Rome), Pierpaolo Basile (University of Bari), Giovanni Maria Biancofiore (Polytechnic University of Bari), Ludovico Boratto (University of Cagliari), Salvatore Bufi (Polytechnic University of Bari), Tommaso Carraro (University of Padova), Giandomenico Cornacchia (Polytechnic University of Bari), Alessandro De Bellis (Polytechnic University of Bari), Marco de Gemmis (University of Bari), Dario Di Palma (Polytechnic University of Bari), Davide Di Ruscio (Università degli Studi dell’Aquila), Francesco Maria Donini (Università della Tuscia), Antonio Ferrara (Polytechnic University of Bari), Dietmar Jannach (University of Klagenfurt), Daniele Malitesta (Polytechnic University of Bari), Alberto Carlo Maria Mancino (Polytechnic University of Bari), Mirko Marras (University of Cagliari), Giacomo Medda (University of Cagliari), Cataldo Musto (University of Bari), Franco Maria Nardini (ISTI-CNR), Fedelucio Narducci (Polytechnic University of Bari), Vincenzo Paparella (Polytechnic University of Bari), Aleksandr Petrov (University of Glasgow), Claudio Pomo (Polytechnic University of Bari), Azzurra Ragone (University of Bari), Paolo Sorino (Polytechnic University of Bari), Marko Tkalcic (Free University of Bozen), Markus Zanker (Free University of Bozen and University of Klagenfurt). Machinery, New York, NY, USA, 2024, p. 1245–1249. URL: https://doi.org/10.1145/3640457. 3687114. doi:10.1145/3640457.3687114. [2] V. W. Anelli, P. Basile, D. G. Bridge, T. D. Noia, P. Lops, C. Musto, F. Narducci, M. Zanker, Knowledge-aware and conversational recommender systems, in: S. Pera, M. D. Ekstrand, X. Amatriain, J. O’Donovan (Eds.), Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2-7, 2018, ACM, 2018, pp. 521–522. [3] V. W. Anelli, T. D. Noia, P. Lops, C. Musto, M. Zanker, P. Basile, D. G. Bridge, F. Narducci (Eds.), Proceedings of the Workshop on Knowledge-aware and Conversational Recommender Systems 2018 co-located with 12th ACM Conf. on Recommender Systems, KaRS@RecSys 2018, Vancouver, Canada, October 7, 2018, volume 2290 of CEUR Workshop Proc., CEUR-WS.org, 2019. URL: http://ceur-ws.org/Vol-2290. [4] V. W. Anelli, T. D. Noia, 2nd workshop on knowledge-aware and conversational recommender systems - kars, in: W. Zhu, D. Tao, X. Cheng, P. Cui, E. A. Rundensteiner, D. Carmel, Q. He, J. X. Yu (Eds.), Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019, ACM, 2019, pp. 3001–3002. [5] V. W. Anelli, T. D. Noia (Eds.), Proceedings of the Second Workshop on Knowledge-aware and Conversational Recommender Systems, co-located with 28th ACM International Conference on Information and Knowledge Management, KaRS@CIKM 2019, Beijing, China, November 7, 2019, volume 2601 of CEUR Workshop Proceedings, CEUR-WS.org, 2020. URL: http://ceur-ws.org/Vol-2601. [6] V. W. Anelli, P. Basile, T. D. Noia, F. M. Donini, C. Musto, F. Narducci, M. Zanker, Third knowledge-aware and conversational recommender systems workshop (kars), in: H. J. C. Pampín, M. A. Larson, M. C. Willemsen, J. A. Konstan, J. J. McAuley, J. Garcia-Gathright, B. Huurnink, E. Oldridge (Eds.), RecSys ’21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021 - 1 October 2021, ACM, 2021, pp. 806–809. [7] V. W. Anelli, P. Basile, T. D. Noia, F. M. Donini, C. Musto, F. Narducci, M. Zanker, H. Abdollahpouri, T. Bogers, B. Mobasher, C. Petersen, M. S. Pera (Eds.), Joint Workshop Proceedings of the 3rd Edition of Knowledge-aware and Conversational Recommender Systems (KaRS) and the 5th Edition of Recommendation in Complex Environments (ComplexRec) colocated with 15th ACM Conference on Recommender Systems (RecSys 2021), Virtual Event, Amsterdam, The Netherlands, September 25, 2021, volume 2960 of CEUR Workshop Proceedings, CEUR-WS.org, 2021. [8] V. W. Anelli, P. Basile, G. de Melo, F. M. Donini, A. Ferrara, C. Musto, F. Narducci, A. Ragone, M. Zanker, Fourth knowledge-aware and conversational recommender systems workshop (kars), in: J. Golbeck, F. M. Harper, V. Murdock, M. D. Ekstrand, B. Shapira, J. Basilico, K. T. Lundgaard, E. Oldridge (Eds.), RecSys ’22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18 - 23, 2022, ACM, 2022, pp. 663–666. URL: https: //doi.org/10.1145/3523227.3547412. doi:10.1145/3523227.3547412. [9] V. W. Anelli, P. Basile, G. de Melo, F. M. Donini, A. Ferrara, C. Musto, F. Narducci, A. Ragone, M. Zanker (Eds.), Proceedings of the Fourth Knowledge-aware and Conversational Recommender Systems Workshop co-located with 16th ACM Conference on Recommender

References

[1] V. W. Anelli , A. Ferrara , C. Musto , F. Narducci , A. Ragone , M. Zanker , Sixth knowledgeaware and conversational recommender systems workshop (kars), in: Proceedings of the 18th ACM Conference on Recommender Systems, RecSys '24, Association for Computing Systems (RecSys 2022 ), Seattle, WA, USA, September 22nd , 2022 , volume 3294 of CEUR Workshop Proceedings, CEUR-WS.org , 2022 . URL: https://ceur-ws. org/ Vol- 3294 .

[10] V. W. Anelli , P. Basile , G. de Melo, F. M. Donini , A. Ferrara , C. Musto , F. Narducci , A. Ragone , M. Zanker , Fifth knowledge-aware and conversational recommender systems workshop (kars), in: J. Zhang , L. Chen, S. Berkovsky , M. Zhang , T. D. Noia , J. Basilico , L. Pizzato , Y. Song (Eds.), Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023 , Singapore, Singapore, September 18-22 , 2023 , ACM, 2023 , pp. 1259 - 1262 . URL: https://doi.org/10.1145/3604915.3608759. doi: 10 .1145/3604915.3608759.

[11] V. W. Anelli , P. Basile , G. de Melo, F. M. Donini , A. Ferrara , C. Musto , F. Narducci , A. Ragone , M. Zanker (Eds.), Proceedings of the Fifth Knowledge-aware and Conversational Recommender Systems Workshop co-located with 17th ACM Conference on Recommender Systems (RecSys 2023 ), Singapore, September 19th , 2023 , volume 3560 of CEUR Workshop Proceedings, CEUR-WS.org , 2023 . URL: https://ceur-ws. org/ Vol- 3560 .

[12] D. Jannach , P. Resnick , A. Tuzhilin , M. Zanker , Recommender systems-beyond matrix completion , Communications of the ACM 59 ( 2016 ) 94 - 102 .

[13] I. Fernández-Tobías , I. Cantador , P. Tomeo , V. W. Anelli , T. D. Noia , Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization, User Model . User-Adapt. Interact . 29 ( 2019 ) 443 - 486 .

[14] V. W. Anelli , A. Bellogín , A. Ferrara , D. Malitesta , F. A. Merra , C. Pomo , F. M. Donini , T. D. Noia , Elliot: A comprehensive and rigorous framework for reproducible recommender systems evaluation , in: SIGIR, ACM, 2021 , pp. 2405 - 2414 .

[15] R. Burke , Knowledge-based recommender systems . encyclopedia of library and information systems: Vol. 6 . (supplement 32) , 2000 .

[16] A. Felfernig , G. Friedrich , D. Jannach , M. Zanker , Constraint-based recommender systems , in: Recommender Systems Handbook , 2015 , pp. 161 - 190 .

[17] M. Zanker , M. Jessenitschnig , W. Schmid, Preference reasoning with soft constraints in constraint-based recommender systems , Constraints 15 ( 2010 ) 574 - 595 .

[18] V. W. Anelli , T. D. Noia , E. D. Sciascio , A. Ragone , J. Trotta , How to make latent factors interpretable by feeding factorization machines with knowledge graphs , in: C. Ghidini , O. Hartig , M. Maleshkova , V. Svátek , I. F. Cruz , A. Hogan , J. Song , M. Lefrançois , F. Gandon (Eds.), The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference , Auckland, New Zealand, October 26-30 , 2019 , Proceedings, Part I , volume 11778 of Lecture Notes in Computer Science, Springer, 2019 , pp. 38 - 56 .

[19] V. W. Anelli , T. D. Noia , E. D. Sciascio , A. Ferrara , A. C. M. Mancino , Sparse feature factorization for recommender systems with knowledge graphs , in: RecSys, ACM, 2021 , pp. 154 - 165 .

[20] E. Palumbo , D. Monti , G. Rizzo, R. Troncy , E. Baralis, entity2rec: Property-specific knowledge graph embeddings for item recommendation , Expert Syst. Appl . 151 ( 2020 ) 113235 .

[21] Y. Zhang , X. Xu , H. Zhou , Y. Zhang, Distilling structured knowledge into embeddings for explainable and accurate recommendation , in: J. Caverlee , X. B. Hu , M. Lalmas , W. Wang (Eds.), WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining , Houston, TX, USA, February 3- 7 , 2020 , ACM, 2020 , pp. 735 - 743 .

[22] C. Ni , K. S. Liu , N. Torzec , Layered graph embedding for entity recommendation using wikipedia in the yahoo! knowledge graph, in: A. E. F. Seghrouchni , G. Sukthankar, T. Liu, M. van Steen (Eds.), Companion of The 2020 Web Conference 2020 , Taipei, Taiwan, April 20-24 , 2020 , ACM / IW3C2, 2020 , pp. 811 - 818 .

[23] M. Nayyeri , S. Vahdati , X. Zhou , H. S. Yazdi , J. Lehmann , Embedding-based recommendations on scholarly knowledge graphs , in: A. Harth , S. Kirrane , A. N. Ngomo , H. Paulheim , A. Rula , A. L. Gentile , P. Haase , M. Cochez (Eds.), The Semantic Web - 17th International Conference, ESWC 2020 , Heraklion, Crete, Greece, May 31-June 4, 2020 , Proceedings, volume 12123 of Lecture Notes in Computer Science, Springer, 2020 , pp. 255 - 270 .

[24] A. C. M. Mancino , A. Ferrara , S. Bufi , D. Malitesta , T. D. Noia , E. D. Sciascio , Kgtore: Tailored recommendations through knowledge-aware GNN models , in: J. Zhang , L. Chen, S. Berkovsky , M. Zhang , T. D. Noia , J. Basilico , L. Pizzato , Y. Song (Eds.), Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023 , Singapore, Singapore, September 18-22 , 2023 , ACM, 2023 , pp. 576 - 587 . URL: https://doi.org/10.1145/3604915. 3608804. doi: 10 .1145/3604915.3608804.

[25] G. He , J. Li , W. X. Zhao , P. Liu , J. Wen , Mining implicit entity preference from user-item interaction data for knowledge graph completion via adversarial learning , in: Y. Huang , I. King , T. Liu, M. van Steen (Eds.), WWW '20: The Web Conference 2020 , Taipei, Taiwan, April 20-24 , 2020 , ACM / IW3C2, 2020 , pp. 740 - 751 .

[26] Y. Cao , X. Wang , X. He , Z. Hu , T. Chua , Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences , in: L. Liu, R. W. White , A. Mantrach , F. Silvestri , J. J. McAuley , R. Baeza-Yates , L. Zia (Eds.), The World Wide Web Conference, WWW 2019 , San Francisco, CA, USA, May 13 -17, 2019 , ACM, 2019 , pp. 151 - 161 .

[27] T. Dettmers , P. Minervini , P. Stenetorp , S. Riedel , Convolutional 2d knowledge graph embeddings, in: S. A . McIlraith , K. Q. Weinberger (Eds.), Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence , (AAAI-18) , the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18) , New Orleans, Louisiana, USA, February 2- 7 , 2018 , AAAI Press, 2018 , pp. 1811 - 1818 .

[28] C. Shang , Y. Tang , J. Huang , J. Bi , X. He , B. Zhou , End-to-end structure-aware convolutional networks for knowledge base completion , in: The Thirty-Third AAAI Conference on Artificial Intelligence , AAAI 2019 , The Thirty-First Innovative Applications of Artificial Intelligence Conference , IAAI 2019 , The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 , Honolulu, Hawaii, USA, January 27 - February 1, 2019 , AAAI Press, 2019 , pp. 3060 - 3067 .

[29] X. Wang , X. He , Y. Cao , M. Liu, T. Chua, KGAT: knowledge graph attention network for recommendation , in: A. Teredesai , V. Kumar , Y. Li , R. Rosales , E. Terzi, G. Karypis (Eds.), Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019 , Anchorage , AK , USA, August 4- 8 , 2019 , ACM, 2019 , pp. 950 - 958 .

[30] X. Wang , D. Wang , C. Xu , X. He , Y. Cao , T.-S. Chua, Explainable reasoning over knowledge graphs for recommendation , in: Proceedings of the AAAI Conference on Artificial Intelligence , volume 33 , 2019 , pp. 5329 - 5336 .

[31] V. W. Anelli , T. D. Noia , E. D. Sciascio , A. Ragone , J. Trotta , Semantic interpretation of top-n recommendations , IEEE Trans. Knowl. Data Eng . 34 ( 2022 ) 2416 - 2428 .

[32] H. Wang , F. Zhang , J. Wang , M. Zhao , W. Li , X. Xie , M. Guo, Exploring high-order user preference on the knowledge graph for recommender systems , ACM Trans. Inf. Syst . 37 ( 2019 ) 32 : 1 - 32 : 26 .

[33] V. W. Anelli , R. D. Leone , T. D. Noia , T. Lukasiewicz , J. Rosati , Combining RDF and SPARQL with cp-theories to reason about preferences in a linked data setting , Semantic Web 11 ( 2020 ) 391 - 419 .

[34] T. Di Noia , V. C. Ostuni , Recommender systems and linked open data , in: Reasoning Web Int. Summer School , Springer, 2015 , pp. 88 - 113 .

[35] T. Di Noia , C. Magarelli , A. Maurino , M. Palmonari , A. Rula , Using ontology-based data summarization to develop semantics-aware recommender systems , in: The Semantic Web - 15th Int. Conf., ESWC 2018 , Heraklion, Crete, Greece, June 3-7, 2018 , Proc., Springer New York, 2018 , pp. 128 - 144 .

[36] A. Ferrara , V. W. Anelli , A. C. M. Mancino , T. D. Noia , E. D. Sciascio , Kgflex: Eficient recommendation with sparse feature factorization and knowledge graphs , Trans. Recomm. Syst . 1 ( 2023 ) 1 - 30 . URL: https://doi.org/10.1145/3588901. doi: 10 .1145/3588901.

[37] V. W. Anelli , Y. Deldjoo , T. D. Noia , A. Ferrara , Prioritized multi-criteria federated learning , Intelligenza Artificiale 14 ( 2020 ) 183 - 200 . URL: https://doi.org/10.3233/IA-200054. doi: 10 . 3233/IA- 200054.

[38] S. Badreddine , A. S. d'Avila Garcez , L. Serafini , M. Spranger , Logic tensor networks , Artif. Intell . 303 ( 2022 ) 103649 .

[39] T. Carraro , A. Daniele , F. Aiolli , L. Serafini , Logic tensor networks for top-n recommendation , in: NeSy , volume 3212 of CEUR Workshop Proceedings, CEUR-WS.org , 2022 , pp. 1 - 14 .

[40] K. Zhou , W. X. Zhao , S. Bian , Y. Zhou , J.-R. Wen , J. Yu , Improving conversational recommender systems via knowledge graph based semantic fusion , in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020 , pp. 1006 - 1014 .

[41] D. Jannach , A. Manzoor , W. Cai , L. Chen , A survey on conversational recommender systems , arXiv preprint arXiv: 2004 . 00646 ( 2020 ).

[42] S. Zhang , K. Balog, Evaluating conversational recommender systems via user simulation , in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020 , pp. 1512 - 1520 .

[43] V. W. Anelli , A. Bellogín , T. D. Noia , C. Pomo, Reenvisioning the comparison between neural collaborative filtering and matrix factorization , in: RecSys, ACM, 2021 , pp. 521 - 529 .

[44] V. W. Anelli , T. D. Noia , P. Lops , E. D. Sciascio , Feature factorization for top-n recommendation: From item rating to features relevance , in: Y. Zheng, W. Pan , S. S. Sahebi , I. Fernández (Eds.), Proceedings of the 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning co-located with ACM Conference on Recommender Systems (RecSys 2017 ), Como, Italy, August 27 , 2017 , volume 1887 of CEUR Workshop Proceedings, CEUR-WS.org , 2017 , pp. 16 - 21 .

[45] M. Polignano , C. Musto , M. de Gemmis, P. Lops, G. Semeraro, Together is better: Hybrid recommendations combining graph embeddings and contextualized word representations , in: RecSys, ACM, 2021 , pp. 187 - 198 .