SEEUNRS: Semantically Enriched Entity-Based Urdu News Recommendation System
Article No.: 48, Pages 1 - 13
Abstract
The advancement in the production, distribution, and consumption of news has fostered easy access to the news with fair challenges. The main challenge is to present the right news to the right audience. The news recommendation system is one of the technological solutions to this problem. Much work has been done on news recommendation systems for the major languages of the world, but trivial work has been done for resource-poor languages like Urdu. Another significant hurdle in the development of an efficient news recommendation system is the scarcity of an accessible and suitable Urdu dataset. To this end, an Urdu news mobile application was used to collect the news data and user feedback for 1 month. After refinement, the first-ever Urdu dataset of 100 users and 23,250 news was curated for the Urdu news recommendation system. In addition, SEEUNRS, a semantically enriched entity-based Urdu news recommendation system, is proposed. The proposed scheme exploits the hidden features of a news article and entities to suggest the right article to the right audience. Results have shown that the presented model has an improvement of 6.9% in the F1 measure from traditional recommendation system techniques.
References
[1]
Hamed Aboutorab, Ran Yu, Alishiba Dsouza, Morteza Saberi, and Omar Khadeer Hussain. 2023. A news recommendation system for environmental risk management. In Proceedings of the 2nd International Workshop on Linked Data-Driven Resilience Research (D2R2’23). 1–10.
[2]
M. P. Akhter, Z. Jiangbin, I. R. Naqvi, M. Abdelmajeed, and M. T. Sadiq. 2020. Automatic detection of offensive language for Urdu and Roman Urdu. IEEE Access 8 (2020), 91213–91226.
[3]
M. An, F. Wu, C. Wu, K. Zhang, Z. Liu, and X. Xie. 2019. Neural news recommendation with long- and short-term user representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL’19). 336–345.
[4]
K. Bauman, B. Liu, and A. Tuzhilin. 2017. Aspect based recommendations: Recommending items with the most valuable aspects based on user reviews. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 717–725.
[5]
D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3 (2003), 993–1022.
[6]
E. Brocken, A. Hartveld, E. de Koning, T. van Noort, F. Hogenboom, F. Frasincar, and T. Robal. 2019. Bing-CF-IDF+: A semantics-driven news recommender system. In Advanced Information Systems Engineering. Springer International Publishing, Cham, 32–47.
[7]
O. Cakir and M. E. Aras. 2012. A recommendation engine by using association rules. Procedia—Social and Behavioral Sciences 62 (2012), 452–456.
[8]
M. Capelle, F. Frasincar, M. Moerland, and F. Hogenboom. 2012. Semantics-based news recommendation. In Proceedings of the 2nd International Conference on Web Intelligence, Mining, and Semantics(WIMS’12). ACM, New York, NY, Article 27, 9 pages.
[9]
M. Capelle, M. Moerland, F. Hogenboom, F. Frasincar, and D. Vandic. 2015. Bing-SF-IDF+: A hybrid semantics-driven news recommender. In Proceedings of the 30th Annual ACM Symposium on Applied Computing(SAC’15). ACM, New York, NY, 732–739.
[10]
C. Chen, X. Meng, Z. Xu, and T. Lukasiewicz. 2017. Location-aware personalized news recommendation with deep semantic analysis. IEEE Access 5 (2017), 1624–1638.
[11]
C. Chen, M. Zhang, Y. Liu, and S. Ma. 2018. Neural attentional rating regression with review-level explanations. In Proceedings of the 2018 World Wide Web Conference(WWW’18). 1583–1592.
[12]
A. S. Das, M. Datar, A. Garg, and S. Rajaram. 2007. Google news personalization: Scalable online collaborative filtering. In Proceedings of the 16th International Conference on World Wide Web(WWW’07). ACM, New York, NY, 271–280.
[13]
E. De Koning, F. Hogenboom, and F. Frasincar. 2018. News recommendation with CF-IDF+. In Advanced Information Systems Engineering. Lecture Notes in Computer Science, Vol. 10816. Springer, 170–184.
[14]
G. de Souza Pereira Moreira, F. Ferreira, and A. M. da Cunha. 2018. News session-based recommendations using deep neural networks. In Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems(DLRS’18). ACM, New York, NY, 15–23.
[15]
F. Goossen, W. IJntema, F. Frasincar, F. Hogenboom, and U. Kaymak. 2011. News personalization using the CF-IDF semantic recommender. In Proceedings of the International Conference on Web Intelligence, Mining, and Semantics(WIMS’11). ACM, New York, NY, Article 10, 12 pages.
[16]
J. L. Herlocker, J. A. Konstan, Al Borchers, and J. Riedl. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR’99). ACM, New York, NY, 230–237.
[17]
W. IJntema, F. Goossen, F. Frasincar, and F. Hogenboom. 2010. Ontology-based news recommendation. In Proceedings of the 2010 EDBT/ICDT Workshops (EDBT’10). ACM, New York, NY, 1–6.
[18]
M. K. Malik, T. Ahmed, S. Sulger, T. Bögel, A. Gulzar, G. Raza, S. Hussain, and M. Butt. 2010. Transliterating Urdu for a broad-coverage Urdu/Hindi LFG grammar. In Proceedings of the 7th International Conference on Language Resources and Evaluation. 2921–2927. http://www.lrec-conf.org/proceedings/lrec2010/pdf/194_Paper.pdf
[19]
S. Kanwal, K. Malik, K. Shahzad, F. Aslam, and Z. Nawaz. 2019. Urdu named entity recognition: Corpus generation and deep learning applications. ACM Transactions on Asian and Low-Resource Language Information Processing 19 (2019), 1–13.
[20]
M. Khan and K. Malik. 2019. Sentiment classification of customer’s reviews about automobiles in Roman Urdu. In Advances in Information and Communication Networks. Springer International Publishing, Cham, 630–640.
[21]
W. Khan, A. Daud, K. Khan, J. A. Nasir, M. Basheri, N. Aljohani, and F. S. Alotaibi. 2019. Part of speech tagging in Urdu: Comparison of machine and deep learning approaches. IEEE Access 7 (2019), 38918–38936.
[22]
A. Khattak, M. Z. Asghar, A. Saeed, I. A. Hameed, S. A. Hassan, and S. Ahmad. 2021. A survey on sentiment analysis in Urdu: A resource-poor language. Egyptian Informatics Journal 22, 1 (2021), 53–74
[23]
D. Khattar, V. Kumar, M. Gupta, and V. Varma. 2018. Neural content-collaborative filtering for news recommendation. In Proceedings of the NewsIR@ECIR Workshop at ECIR. 1–6.
[24]
Y. Kim. 2014. Convolutional neural networks for sentence classification. CoRR abs/1408.5882 (2014). http://arxiv.org/abs/1408.5882
[25]
X. Kong, M. Mao, W. Wang, J. Liu, and B. Xu. 2018. VOPRec: Vector representation learning of papers with text information and structural identity for recommendation. IEEE Transactions on Emerging Topics in Computing. Early Access, April 26, 2018.
[26]
Y. Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD’08). ACM, New York, NY, 426–434.
[27]
Y. Koren, R. Bell, and C. Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42 (2009), 30–37.
[28]
M. K. Malik. 2017. Urdu named entity recognition and classification system using artificial neural network. ACM Transactions on Asian and Low-Resource Language Information Processing 17 (2017), 1–13.
[29]
Khawar Mehmood, Daryl Essam, Kamran Shafi, and Muhammad Kamran Malik. 2019. Sentiment analysis for a resource poor language–Roman Urdu. ACM Transactions on Asian and Low-Resource Language Information Processing 19, 1 (2019), 1–15.
[30]
U. Muhammad, A. Saba, S. Zunaira, and M. Kamran. 2016. Urdu text classification using majority voting. International Journal of Advanced Computer Science and Applications 7 (2016), 265–273.
[31]
C. Musto, G. Semeraro, M. Degemmis, and P. Lops. 2015. Word embedding techniques for content-based recommender systems: An empirical evaluation. In RecSys PostersProceedings.
[32]
M. Quadrana, A. Karatzoglou, B. Hidasi, and P. Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the 11th ACM Conference on Recommender Systems(RecSys’17). ACM, New York, NY, 130–137.
[33]
J. Ren, J. Long, and Z. Xu. 2019. Financial news recommendation based on graph embeddings. Decision Support Systems125 (2019), 113115.
[34]
I. Vagliano, D. Monti, A. Scherp, and M. Morisio. 2017. Content recommendation through semantic annotation of user reviews and linked data—An extended technical report. arXiv:1709.09973 (2017).
[35]
D. Wang, Y. Liang, D. Xu, X. Feng, and R. Guan. 2018. A content-based recommender system for computer science publications. Knowledge-Based Systems157 (2018), 1–9.
[36]
H. Wang, F. Zhang, X. Xie, and M. Guo. 2018. DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the 27th International Conference on World Wide Web (WWW’18).
[37]
J. Zhang and C. Chow. 2018. SEMA: Deeply learning semantic meanings and temporal dynamics for recommendations. IEEE Access 6 (2018), 54106–54116.
[38]
L. Zheng, V. Noroozi, and P. S. Yu. 2017. Joint deep modeling of users and items using reviews for recommendation. CoRR abs/1701.04783 (2017). http://arxiv.org/abs/1701.04783
Index Terms
- SEEUNRS: Semantically Enriched Entity-Based Urdu News Recommendation System
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Association for Computing Machinery
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Publication History
Published: 09 March 2024
Online AM: 11 January 2024
Accepted: 24 December 2023
Revised: 18 December 2023
Received: 02 March 2022
Published in TALLIP Volume 23, Issue 3
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