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A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews

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Abstract

In popular applications such as e-commerce sites and social media, users provide online reviews giving personal opinions about a wide array of items, such as products, services and people. These reviews are usually in the form of free text, and represent a rich source of information about the users’ preferences. Among the information elements that can be extracted from reviews, opinions about particular item aspects (i.e., characteristics, attributes or components) have been shown to be effective for user modeling and personalized recommendation. In this paper, we investigate the aspect-based top-N recommendation problem by separately addressing three tasks, namely identifying references to item aspects in user reviews, classifying the sentiment orientation of the opinions about such aspects in the reviews, and exploiting the extracted aspect opinion information to provide enhanced recommendations. Differently to previous work, we integrate and empirically evaluate several state-of-the-art and novel methods for each of the above tasks. We conduct extensive experiments on standard datasets and several domains, analyzing distinct recommendation quality metrics and characteristics of the datasets, domains and extracted aspects. As a result of our investigation, we not only derive conclusions about which combination of methods is most appropriate according to the above issues, but also provide a number of valuable resources for opinion mining and recommendation purposes, such as domain aspect vocabularies and domain-dependent, aspect-level lexicons.

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Notes

  1. Yelp Challenge dataset, https://www.yelp.com/dataset/challenge.

  2. Amazon reviews dataset, http://jmcauley.ucsd.edu/data/amazon.

  3. Aspect opinion resources, http://ir.ii.uam.es/aspects.

  4. AllMusic record reviews, https://www.allmusic.com.

  5. GameSpot Video Games reviews and news https://www.gamespot.com.

  6. WordNet lexical database, https://wordnet.princeton.edu.

  7. Penn Treebank, http://web.mit.edu/6.863/www/PennTreebankTags.html.

  8. Double negations of adjectives in sentences are also recognized by our method.

  9. The identification of nouns includes compound nouns, by means of the compound, nn and nmod relations.

  10. Thesaurus.com - synonyms and antonyms, http://www.thesaurus.com.

  11. TripAdvisor travel and restaurant review site, https://www.tripadvisor.com.

  12. British National Corpus, http://www.natcorp.ox.ac.uk.

  13. RankSys recommender systems evaluation framework, http://ranksys.org.

  14. RiVal recommender system evaluation toolkit, http://rival.recommenders.net.

References

  • Aciar, S., Zhang, D., Simoff, S., Debenham, J.: Informed recommender: basing recommendations on consumer product reviews. IEEE Intell. Syst. 22(3), 37–47 (2007)

  • Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.). Recommender Systems Handbook, pp. 191–226. Springer (2015)

  • Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  • Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB’94, pp. 487–499 (1994)

  • Bafna, K., Toshniwal, D.: Feature based summarization of customers reviews of online products. Procedia Comput. Sci. 22, 142–151 (2013)

    Article  Google Scholar 

  • Bauman, K., Liu, B., Tuzhilin, A.: 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, KDD’17, pp. 717–725. ACM (2017)

  • Bellogín, A., Castells, P., Cantador, I.: Precision-oriented evaluation of recommender systems: an algorithmic comparison. In: Proceedings of the 5th ACM Conference on Recommender Systems, RecSys’11, pp. 333–336. ACM (2011)

  • Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  • Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., Rossiello, G.: SABRE: a sentiment aspect-based retrieval engine. In: Information Filtering and Retrieval, pp. 63–78. Springer (2017)

  • Castells, P., Hurley, N.J., Vargas, S.: Novelty and diversity in recommender systems. In: Recommender Systems Handbook, pp. 881–918. Springer (2015)

  • Chen, G., Chen, L.: Recommendation based on contextual opinions. In: Proceedings of the 22nd Conference on User Modelling, Adaptation and Personalization, UMAP’14, pp. 61–73. Springer (2014)

  • Chen, D., Manning, C.: A fast and accurate dependency parser using neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP’14, pp. 740–750. ACL (2014)

  • Chen, L., Wang, F.: Preference-based clustering reviews for augmenting e-commerce recommendation. Knowl. Based Syst. 50, 44–59 (2013)

    Article  Google Scholar 

  • Chen, L., Wang, F.: Sentiment-enhanced explanation of product recommendations. In: Proceedings of the 23rd International Conference on World Wide Web, WWW’2014, pp. 239–240. ACM (2014)

  • Chen, T., Zheng, Z., Lu, Q., Zhang, W., Yu, Y.: Feature-based matrix factorization (2011). arXiv preprint arXiv:1109.2271

  • Chen, L., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Model. User Adapt. Interact. 25(2), 99–154 (2015)

    Article  Google Scholar 

  • Chen, X., Qin, Z., Zhang, Y., Xu, T.: Learning to rank features for recommendation over multiple categories. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’16, pp. 305–314. ACM (2016)

  • Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the 4th ACM Conference on Recommender Systems, RecSys’10, pp. 39–46. ACM (2010)

  • Diao, Q., Qiu, M., Wu, C.Y., Smola, A.J., Jiang, J., Wang, C.: Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14. ACM (2014)

  • Dong, R., Schaal, M., O’Mahony, M.P., McCarthy, K., Smyth, B.: Opinionated product recommendation. In: Proceedings of the 21st International Conference on Case-Based Reasoning, ICCBR’13, pp. 44–58. Springer (2013)

  • Esuli, A., Sebastiani, F.: SentiWordNet: a high-coverage lexical resource for opinion mining. Evaluation 17, 1–26 (2007)

    Google Scholar 

  • Etzioni, O., Cafarella, M., Downey, D., Popescu, A.M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Unsupervised named-entity extraction from the web: an experimental study. Artif. Intell. 165(1), 91–134 (2005)

    Article  Google Scholar 

  • Ganu, G., Kakodkar, Y., Marian, A.: Improving the quality of predictions using textual information in online user reviews. Inf. Syst. 38(1), 1–15 (2013)

    Article  Google Scholar 

  • García Esparza, S., O’Mahony, M.P., Smyth, B.: A multi-criteria evaluation of a user generated content based recommender system. In: Proceedings of 3rd Workshop on Recommender Systems and the SocialWeb, RSWEB’11 (2011)

  • Gunawardana, A., Meek, C.: A unified approach to building hybrid recommender systems. In: Proceedings of the 4th ACM Conference on Recommender Systems, RecSys’09, pp. 117–124. ACM (2009)

  • He, R., McAuley, J.: VBPR: visual Bayesian personalized ranking from implicit feedback. In: Proceedings of the 13th AAAI Conference on Artificial Intelligence, AAAI’16, pp. 144–150 (2016)

  • Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: Analgorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’99, pp. 230–237. ACM (1999)

  • Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1), 177–196 (2001)

    Article  MATH  Google Scholar 

  • Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’04, pp. 168–177. ACM (2004a)

  • Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceedings of the 19th National Conference on Artificial Intelligence, AAAI’14, pp. 755–760. AAAI Press (2004b)

  • Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining, ICDM’08, pp. 263–272. IEEE (2008)

  • Jakob, N., Gurevych, I.: Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP’10, pp. 1035–1045. ACL (2010)

  • Jakob, N., Weber, S.H., Müller, M.C., Gurevych, I.: Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. In: Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion, TSA’09, pp. 57–64. ACM (2009)

  • Jamroonsilp, S., Prompoon, N.: Analyzing software reviews for software quality-based ranking. In: Proceedings of the 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON’10, pp. 1–6. IEEE (2013)

  • Jannach, D., Adomavicius, G.: Recommendations with a purpose. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys’16, pp. 7–10. ACM (2016)

  • Ko, M., Kim, H.W., Mun, Y.Y., Song, J., Liu, Y.: Moviecommenter: aspect-based collaborative filtering by utilizing user comments. In: Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom’11, pp. 362–371. IEEE (2011)

  • Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  • Kumar, K.P.V., Kumar, G.S.C., Aruna, M., Srinivas, B.: Mining online customer reviews for product feature-based ranking. Int J Adv Res Comput Sci 6(3), 23–27 (2015)

    Google Scholar 

  • Kumar, S., Gao, X., Welch, I.: Co-clustering for dual topic models. In: Proceedings of the 9th Australasian Joint Conference on Advances in Artificial Intelligence, AI’16, pp. 390–402. Springer (2016)

  • Levi, A., Mokryn, O., Diot, C., Taft, N.: Finding a needle in a haystack of reviews: cold start context-based hotel recommender system. In: Proceedings of the 6th ACM Conference on Recommender Systems, RecSys’12, pp. 115–122. ACM (2012)

  • Li, S., Zha, Z.J., Ming, Z., Wang, M., Chua, T.S., Guo, J., Xu, W.: Product comparison using comparative relations. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’11, pp. 1151–1152. ACM (2011)

  • Lippert, C., Weber, S.H., Huang, Y., Tresp, V., Schubert, M., Kriegel, H.P.: Relation prediction in multi-relational domains using matrix factorization. In: Proceedings of the NIPS 2008 Workshop ’Structured Input–Structured Output’ (2008)

  • Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael (2012)

    Book  Google Scholar 

  • Liu, B., Zhang, L.: In: In: Aggarwal, C., Zhai, C. (eds.). Mining Text Data, (ed.) A survey of opinion mining and sentiment analysis, pp. 415–463. Springer (2012)

  • Liu, K., Xu, L., Zhao, J.: Opinion target extraction using word-based translation model. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL ’12, pp. 1346–1356. ACL (2012)

  • Liu, H., He, J., Wang, T., Song, W., Du, X.: Combining user preferences and user opinions for accurate recommendation. Electron. Commer. Res. Appl. 12(1), 14–23 (2013)

    Article  Google Scholar 

  • McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys’13, pp. 165–172. ACM (2013)

  • McAuley, J., Yang, A.: Addressing complex and subjective product-related queries with customer reviews. In: Proceedings of the 25th International Conference on World Wide Web, WWW’16, International World Wide Web Conferences Steering Committee, pp. 625–635 (2016)

  • McAuley, J., Leskovec, J., Jurafsky, D.: Learning attitudes and attributes from multi-aspect reviews. In: Proceedings of the 12th IEEE International Conference on Data Mining, ICDM’12, pp. 1020–1025. IEEE (2012)

  • McCallum, A.K.: Mallet: a machine learning for language toolkit (2002). http://mallet.cs.umass.edu. Accessed 15 Dec 2017

  • Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  • Moshfeghi, Y., Piwowarski, B., Jose, J.M.: Handling data sparsity in collaborative filtering using emotion and semantic based features. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’11, pp. 625–634. ACM (2011)

  • Musto, C., Semeraro, G., Polignano, M.: A comparison of lexicon-based approaches for sentiment analysis of microblog posts. In: Proceedings of the 8th International Workshop on Information Filtering and Retrieval, DART’14, pp. 59–68 (2014)

  • Musto, C., de Gemmis, M., Semeraro, G., Lops, P.: A multi-criteria recommender system exploiting aspect-based sentiment analysis of users’ reviews. In: Proceedings of the 11th ACM Conference on Recommender Systems, RecSys’17, pp. 321–325. ACM (2017)

  • Nie, Y., Liu, Y., Yu, X.: Weighted aspect-based collaborative filtering. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’14, pp. 1071–1074. ACM (2014)

  • Ning, X., Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 37–76. Springer (2015)

  • Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)

    Article  Google Scholar 

  • Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction. In: Proceedings of the 21st International Conference on User Modeling, Adaptation, and Personalization, UMAP’13, pp. 1–13. Springer (2013)

  • Pilászy, I., Zibriczky, D., Tikk, D.: Fast als-based matrix factorization for explicit and implicit feedback datasets. In: Proceedings of the 4th ACM Conference on Recommender Systems, RecSys’10, pp. 71–78. ACM (2010)

  • Poirier, D., Tellier, I., Fessant, F., Schluth, J.: Towards text-based recommendations. In: Proceedings of the 9th International Conference on Adaptivity, Personalization and Fusion of Heterogeneous Information, RIAO’10, pp. 136–137 (2010)

  • Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the 2007 Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT’05, pp. 9–28. Springer (2005)

  • Poria, S., Cambria, E., Ku, L.W., Gui, C., Gelbukh, A.: A rule-based approach to aspect extraction from product reviews. In: Proceedings of the 2nd Workshop on Natural Language Processing for Social Media, SocialNLP’14, pp. 28–37 (2014)

  • Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Comput. Linguist. 37(1), 9–27 (2011)

    Article  Google Scholar 

  • Raghavan, S., Gunasekar, S., Ghosh, J.: Review quality aware collaborative filtering. In: Proceedings of the 6th ACM Conference on Recommender Systems, RecSys’12, pp. 123–130. ACM (2012)

  • Rana, T.A., Cheah, Y.N.: Aspect extraction in sentiment analysis: comparative analysis and survey. Artif. Intell. Rev. 46(4), 459–483 (2016)

    Article  Google Scholar 

  • Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, AUAI’09, pp. 452–461. AUAI Press (2009)

  • Salakhutdinov, R.R., Mnih, A.: Probabilistic matrix factorization. In: Proceedings of the 20th International Conference on Neural Information Processing Systems, NIPS’07, pp. 1257–1264 (2007)

  • Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., Jin, C.: Red opal: product-feature scoring from reviews. In: Proceedings of the 8th ACM Conference on Electronic Commerce, EC’07, pp. 182–191. ACM (2007)

  • Schuster, S., Manning, C.D.: Enhanced English universal dependencies: an improved representation for natural language understanding tasks. In: Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC’16 (2016)

  • Seroussi, Y., Bohnert, F., Zukerman, I.: Personalised rating prediction for new users using latent factor models. In: Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia, HT’11, pp. 47–56. ACM (2011)

  • Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP’13, pp. 1631–1642 (2013)

  • Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  • Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, WWW’08, pp. 111–120. ACM (2008a)

  • Titov, I., McDonald, R.T.: A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, ACL’08, vol. 1, pp. 308–316. ACL (2008b)

  • Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL-HLT’03, vol. 1, pp. 173–180. ACL (2003)

  • Van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS’13, vol. 2, pp. 2643–2651 (2013)

  • Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’11, pp. 448–456. ACM (2011)

  • Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’10, pp. 783–792. ACM (2010)

  • Wang, Y., Liu, Y., Yu, X.: Collaborative filtering with aspect-based opinion mining: a tensor factorization approach. In: Proceedings of the 12th IEEE International Conference on Data Mining, ICDM’12, pp. 1152–1157. IEEE (2012)

  • Wang, F., Pan, W., Chen, L.: Recommendation for new users with partial preferences by integrating product reviews with static specifications. In: Proceedings of the 21st Conference on User Modeling, Adaptation, and Personalization, UMAP’13, pp. 281–288. Springer (2013)

  • Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the 3rd Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT-EMNLP’05, pp. 347–354. ACL (2005)

  • Wu, Y., Ester, M.: Flame: a probabilistic model combining aspect based opinion mining and collaborative filtering. In: Proceedings of the 8th ACM International Conference on Web Search and Data Mining, WSDM’15, pp. 199–208. ACM (2015)

  • Wu, C.Y., Diao, Q., Qiu, M., Jiang, J., Wang, C.: Jointly modeling aspects, ratings and sentiments for movie recommendation. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’14, pp. 193–202. ACM (2014)

  • Yates, A., Joseph, J., Popescu, A.M., Cohn, A.D., Sillick, N.: Shopsmart: product recommendations through technical specifications and user reviews. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM’08, pp. 1501–1502. ACM (2008)

  • Zhang, L., Liu, B., Lim, S.H., O’Brien-Strain, E.: Extracting and ranking product features in opinion documents. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING ’10, pp. 1462–1470. ACL (2010)

  • Zhang, W., Ding, G., Chen, L., Li, C., Zhang, C.: Generating virtual ratings from chinese reviews to augment online recommendations. ACM Trans. Intell. Syst. Technol. 4(1), 9 (2013)

    Article  Google Scholar 

  • Zhang, S., Yao, L., Sun, A.: Deep learning based recommender system: a survey and new perspectives (2017). arXiv preprint arXiv:1707.07435

  • Zhao, W.X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP’10, pp. 56–65. ACL (2010a)

  • Zhou, T., Kuscsik, Z., Liu, J.G., Medo, M., Wakeling, J.R., Zhang, Y.C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Natl. Acad. Sci. 107(10), 4511–4515 (2010b)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P). The authors thank the reviewers for their thoughtful comments and suggestions.

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  1. BBVA Data and Analytics, Madrid, Spain

    María Hernández-Rubio

  2. Departamento de Ingeniería Informática, Universidad Autónoma de Madrid, Madrid, Spain

    Iván Cantador & Alejandro Bellogín

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A Appendix

A Appendix

For the sake of reproducibility, in Table 9 we present the optimal parameter values found for the recommendation methods presented in Sect. 6, and, specifically, for the results reported in Tables 5 and 7.

These parameters were obtained by running all the possible method combinations, and selecting the best performing ones according to P@5. In particular, a grid search was conducted based on the following values of the parameters:

  • Number of neighbors (rec column for ub, cbib, and cbub): 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100.

  • Number of latent factors (rec column for mf): 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100.

  • Threshold to select terms (met column when asp is sab): 0.1, 0.05, 0.03, 0.01, 0.005, 0.003, 0.001.

  • Top terms (met column when asp is dp or dpp): 10, 20, 50, 100, 200, 500.

  • Number of latent topics (met column when asp is lda): 5, 10, 20, 50, 100.

  • Maximum number of words from the corpus (rec column for hft): 5 K, 50 K, 500 K. The regularizers for the latent topic (0, 0.1, 0.5) and MF (0.1, 0.5, 1) as well as the number of latent factors/topics (5, 10) were also tested but no important differences were observed, as in the original paper; hence, 0, 0.1 and 5 were used for these parameters in every dataset.

Note that the non-personalized techniques such as rnd and ipop do not use any parameter (denoted as \(-\) in the table); furthermore, pure collaborative filtering algorithms (ib, ub, mf) do not need any parameter regarding the aspect extraction method because they do not exploit aspect opinion information. It should also be noted that the cb pure content-based method and the voc vocabulary-based aspect extraction method do not have parameters either. Additionally, as a representative example, in Table 10 we show the extracted aspects by the Double Propagation and SABRE methods using top 20 terms and 0.01 threshold, respectively.

Table 9 Parameter values of the recommenders (rec column) and aspect extraction methods (asp column) whose results are reported in Tables 5 and 7
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Table 10 Extracted aspects with Double Propagation and SABRE
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Hernández-Rubio, M., Cantador, I. & Bellogín, A. A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews. User Model User-Adap Inter 29, 381–441 (2019). https://doi.org/10.1007/s11257-018-9214-9

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