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Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation

Published 29 Jul 2023 in cs.IR | (2307.16089v3)

Abstract: Recent neural news recommenders (NNRs) extend content-based recommendation (1) by aligning additional aspects (e.g., topic, sentiment) between candidate news and user history or (2) by diversifying recommendations w.r.t. these aspects. This customization is achieved by hardcoding additional constraints into the NNR's architecture and/or training objectives: any change in the desired recommendation behavior thus requires retraining the model with a modified objective. This impedes widespread adoption of multi-aspect news recommenders. In this work, we introduce MANNeR, a modular framework for multi-aspect neural news recommendation that supports on-the-fly customization over individual aspects at inference time. With metric-based learning as its backbone, MANNeR learns aspect-specialized news encoders and then flexibly and linearly combines the resulting aspect-specific similarity scores into different ranking functions, alleviating the need for ranking function-specific retraining of the model. Extensive experimental results show that MANNeR consistently outperforms state-of-the-art NNRs on both standard content-based recommendation and single- and multi-aspect customization. Lastly, we validate that MANNeR's aspect-customization module is robust to language and domain transfer.

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References (70)
  1. Neural news recommendation with long-and short-term user representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 336–345.
  2. Neural machine translation by jointly learning to align and translate. ICLR.
  3. XLM-T: Multilingual language models in twitter for sentiment analysis and beyond. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 258–266.
  4. Vincenzo Bonnici. 2020. Kullback-leibler divergence between quantum distributions, and its upper-bound. arXiv preprint arXiv:2008.05932.
  5. Translating embeddings for modeling multi-relational data. In Proceedings of the 26th International Conference on Neural Information Processing Systems-Volume 2, pages 2787–2795.
  6. Novelty and diversity in recommender systems. In Recommender systems handbook, pages 603–646. Springer.
  7. Location-aware personalized news recommendation with deep semantic analysis. IEEE Access, 5:1624–1638.
  8. Learning phrase representations using rnn encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724–1734.
  9. Do not read the same news! enhancing diversity and personalization of news recommendation. In Companion Proceedings of the Web Conference 2022, pages 1211–1215.
  10. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8440–8451.
  11. Jonathan L Freedman and David O Sears. 1965. Selective exposure. In Advances in experimental social psychology, volume 2, pages 57–97. Elsevier.
  12. Contextual hybrid session-based news recommendation with recurrent neural networks. IEEE Access, 7:169185–169203.
  13. Fine-grained deep knowledge-aware network for news recommendation with self-attention. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pages 81–88. IEEE.
  14. Alireza Gharahighehi and Celine Vens. 2023. Diversification in session-based news recommender systems. Personal and Ubiquitous Computing, 27(1):5–15.
  15. The adressa dataset for news recommendation. In Proceedings of the International Conference on Web Intelligence, pages 1042–1048.
  16. Supervised contrastive learning for pre-trained language model fine-tuning. In International Conference on Learning Representations.
  17. Benefits of diverse news recommendations for democracy: A user study. Digital Journalism, 10(10):1710–1730.
  18. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685.
  19. Graph neural news recommendation with unsupervised preference disentanglement. In Proceedings of the 58th annual meeting of the association for computational linguistics, pages 4255–4264.
  20. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management, pages 2333–2338.
  21. A survey on knowledge-aware news recommender systems. Semantic Web, 15(1):21–82.
  22. NewsRecLib: A PyTorch-lightning library for neural news recommendation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 296–310, Singapore. Association for Computational Linguistics.
  23. Simplifying content-based neural news recommendation: On user modeling and training objectives. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2384–2388.
  24. Supervised contrastive learning. In Proceedings of the 34th International Conference on Neural Information Processing Systems, pages 18661–18673.
  25. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. ICLR.
  26. Operationalizing a national digital library: The case for a norwegian transformer model. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), pages 20–29.
  27. Miner: Multi-interest matching network for news recommendation. In Findings of the Association for Computational Linguistics: ACL 2022, pages 343–352.
  28. Miaomiao Li and Licheng Wang. 2019. A survey on personalized news recommendation technology. IEEE Access, 7:145861–145879.
  29. KRED: Knowledge-aware document representation for news recommendations. In Proceedings of the 14th ACM Conference on Recommender Systems, pages 200–209.
  30. The interaction between political typology and filter bubbles in news recommendation algorithms. In Proceedings of the Web Conference 2021, pages 3791–3801.
  31. RoBERTa: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
  32. Beyond optimizing for clicks: Incorporating editorial values in news recommendation. In Proceedings of the 28th ACM conference on user modeling, adaptation and personalization, pages 145–153.
  33. Cornelius A Ludmann. 2017. Recommending news articles in the clef news recommendation evaluation lab with the data stream management system odysseus. In CLEF (Working Notes).
  34. Dan Saattrup Nielsen. 2023. ScandEval: A benchmark for scandinavian natural language processing. In The 24rd Nordic Conference on Computational Linguistics.
  35. Embedding-based news recommendation for millions of users. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1933–1942.
  36. Eli Pariser. 2011. The filter bubble: What the Internet is hiding from you. penguin UK.
  37. Personalized news recommendation with knowledge-aware interactive matching. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 61–70.
  38. PP-Rec: News recommendation with personalized user interest and time-aware news popularity. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5457–5467.
  39. News recommendation with candidate-aware user modeling. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1917–1921.
  40. HieRec: Hierarchical user interest modeling for personalized news recommendation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5446–5456.
  41. Taxonomy based personalized news recommendation: novelty and diversity. In Web Information Systems Engineering–WISE 2013: 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part I 14, pages 209–218. Springer.
  42. Shaina Raza. 2023. Bias reduction news recommendation system. Digital, 4(1):92–103.
  43. Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982–3992.
  44. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
  45. Mete Sertkan and Julia Neidhardt. 2022. Exploring expressed emotions for neural news recommendation. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, pages 22–28.
  46. Mete Sertkan and Julia Neidhardt. 2023. On the effect of incorporating expressed emotions in news articles on diversity within recommendation models. decision-making, 3:11.
  47. Heng-Shiou Sheu and Sheng Li. 2020. Context-aware graph embedding for session-based news recommendation. In Proceedings of the 14th ACM Conference on Recommender Systems, pages 657–662.
  48. DCAN: Diversified news recommendation with coverage-attentive networks. arXiv preprint arXiv:2206.02627.
  49. A location-based news article recommendation with explicit localized semantic analysis. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pages 293–302.
  50. Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-sne. Journal of machine learning research, 9(11).
  51. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 6000–6010.
  52. Radio*–an introduction to measuring normative diversity in news recommendations. ACM Transactions on Recommender Systems.
  53. DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the 2018 world wide web conference, pages 1835–1844.
  54. News recommendation via multi-interest news sequence modelling. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 7942–7946. IEEE.
  55. Neural news recommendation with attentive multi-view learning. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 3863–3869.
  56. NPA: neural news recommendation with personalized attention. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2576–2584.
  57. Neural news recommendation with topic-aware news representation. In Proceedings of the 57th Annual meeting of the association for computational linguistics, pages 1154–1159.
  58. Neural news recommendation with multi-head self-attention. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pages 6389–6394.
  59. Rethinking InfoNCE: How many negative samples do you need? In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pages 2509–2515. International Joint Conferences on Artificial Intelligence Organization.
  60. Personalized news recommendation: Methods and challenges. ACM Transactions on Information Systems, 41(1):1–50.
  61. SentiRec: Sentiment diversity-aware neural news recommendation. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 44–53.
  62. Empowering news recommendation with pre-trained language models. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1652–1656.
  63. End-to-end learnable diversity-aware news recommendation. arXiv preprint arXiv:2204.00539.
  64. Is news recommendation a sequential recommendation task? In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2382–2386.
  65. Removing ai’s sentiment manipulation of personalized news delivery. Humanities and Social Sciences Communications, 9(1):1–9.
  66. MIND: A large-scale dataset for news recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3597–3606.
  67. Group-based personalized news recommendation with long-and short-term fine-grained matching. ACM Transactions on Information Systems.
  68. Why do we click: visual impression-aware news recommendation. In Proceedings of the 29th ACM International Conference on Multimedia, pages 3881–3890.
  69. Efficient-fedrec: Efficient federated learning framework for privacy-preserving news recommendation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2814–2824.
  70. Avoiding monotony: improving the diversity of recommendation lists. In Proceedings of the 2008 ACM conference on Recommender systems, pages 123–130.
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