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SPrank: Semantic Path-Based Ranking for Top-N Recommendations Using Linked Open Data

Authors:

Tommaso Di Noia,

Vito Claudio Ostuni,

Eugenio Di SciascioAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 8, Issue 1

Article No.: 9, Pages 1 - 34

https://doi.org/10.1145/2899005

Published: 20 September 2016 Publication History

Abstract

In most real-world scenarios, the ultimate goal of recommender system applications is to suggest a short ranked list of items, namely top-N recommendations, that will appeal to the end user. Often, the problem of computing top-N recommendations is mainly tackled with a two-step approach. The system focuses first on predicting the unknown ratings, which are eventually used to generate a ranked recommendation list. Actually, the top-N recommendation task can be directly seen as a ranking problem where the main goal is not to accurately predict ratings but to directly find the best-ranked list of items to recommend. In this article we present SPrank, a novel hybrid recommendation algorithm able to compute top-N recommendations exploiting freely available knowledge in the Web of Data. In particular, we employ DBpedia, a well-known encyclopedic knowledge base in the Linked Open Data cloud, to extract semantic path-based features and to eventually compute top-N recommendations in a learning-to-rank fashion. Experiments with three datasets related to different domains (books, music, and movies) prove the effectiveness of our approach compared to state-of-the-art recommendation algorithms.

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Zhang HZeng XGanchev I(2024)WeightedSLIM: A Novel Item-Weights Enriched Baseline Recommendation ModelWSEAS TRANSACTIONS ON COMPUTER RESEARCH10.37394/232018.2024.12.2012(201-210)Online publication date: 14-Feb-2024
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Index Terms

SPrank: Semantic Path-Based Ranking for Top-N Recommendations Using Linked Open Data
1. Information systems
  1. Information retrieval

Recommendations

Top-N recommendations from implicit feedback leveraging linked open data
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Exploiting Linked Open Data in Cold-start Recommendations with Positive-only Feedback
CERI '16: Proceedings of the 4th Spanish Conference on Information Retrieval

In recommender systems, user preferences can be acquired either explicitly by means of ratings, or implicitly --e.g., by processing text reviews, and by mining item browsing and purchasing records. Most existing collaborative filtering approaches have ...
The Adaptive Ontology-Based Personalized Recommender System

Recommender systems provide strategies that help users search or make decisions within the overwhelming information spaces nowadays. They have played an important role in various areas such as e-commerce and e-learning. In this paper, we propose a ...

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 8, Issue 1

January 2017

363 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2973184

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2016 ACM.

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Publication History

Published: 20 September 2016

Accepted: 01 March 2016

Revised: 01 February 2016

Received: 01 October 2014

Published in TIST Volume 8, Issue 1

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Zhang HZeng XGanchev I(2024)WeightedSLIM: A Novel Item-Weights Enriched Baseline Recommendation ModelWSEAS TRANSACTIONS ON COMPUTER RESEARCH10.37394/232018.2024.12.2012(201-210)Online publication date: 14-Feb-2024
https://doi.org/10.37394/232018.2024.12.20
Huang WLi QMeng S(2024)KG2Rec: LSH-CF recommendation method based on knowledge graph for cloud servicesWireless Networks10.1007/s11276-020-02387-z30:5(3483-3494)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11276-020-02387-z
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https://dl.acm.org/doi/10.1007/s10844-023-00830-z
Xuan YNawi ROsman NHarun N(2024)Alleviating Sparsity to Enhance Group Recommendation with Cross-Linked Domain ModelRecent Advances on Soft Computing and Data Mining10.1007/978-3-031-66965-1_4(33-42)Online publication date: 30-Jul-2024
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Gupta MSaini P(2023)Knowledge Graphs for Explaination of Black-Box Recommender SystemDeep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing10.2174/9789815079210123010015(183-205)Online publication date: 20-Aug-2023
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Heist NHertling SPaulheim HFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream TasksProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615241(3938-3942)Online publication date: 21-Oct-2023
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Kuznetsov SKordík P(2023)Overcoming the Cold-Start Problem in Recommendation Systems with Ontologies and Knowledge GraphsNew Trends in Database and Information Systems10.1007/978-3-031-42941-5_52(591-603)Online publication date: 31-Aug-2023
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