Unsupervised tag recommendation for popular and cold products

  • Anand KonjengbamEmail author
  • Nagendra Kumar
  • Manish Singh


The rapid expansion of the Internet and its connectivity has given tremendous growth to e-commerce sites. Product reviews form an indispensable part of e-commerce sites. However, it is challenging and laborious to go through hundreds of reviews. In this paper, we address the problem of summarizing reviews by means of informative and readable tags. We present a novel unsupervised method of generating tags and rank them based on relevance. We refine the generated tags using NLP syntactic rules to make them more informative. Our proposed Tagging Product Review (TPR) system takes into consideration the opinions expressed on the product or its aspects. We also address the problem of tag generation for cold products, which have only a limited number of reviews and that too, with very short content. We use transfer learning to build a tag cloud from popular product reviews and use it to identify good tags from cold product reviews. We evaluate our proposed system using online reviews of twelve products of varying popularity, collected from Our result demonstrates the effectiveness of our approach at generating relevant tags compared to three popular baseline methods. Our proposed approach gives an average tag relevance score (NDCG) of around 79% for popular products and 85% for cold products. Our approach also gives an average precision of 89% for identifying correct tags. The results suggest that our TPR system successfully summarize reviews by means of tags.


Electronic commerce Tagging review Information search Review summarization Opinion mining User feedback 



  1. Belém, F., Santos, R., Almeida, J., Gonçalves, M. (2013). Topic diversity in tag recommendation. In Proceedings of the 7th ACM conference on recommender systems (pp. 141–148): ACM.Google Scholar
  2. Belém, F.M., Martins, E.F., Almeida, J., Gonçalves, M.A. (2014). Personalized and object-centered tag recommendation methods for web 2.0 applications. Information Processing & Management, 50(4), 524–553.CrossRefGoogle Scholar
  3. Blei, D.M., Ng, A.Y., Jordan, M.I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993–1022.zbMATHGoogle Scholar
  4. Bullock, B.N., Hotho, A., Stumme, G. (2018). Accessing information with tags: search and ranking. In Social information access (pp. 310–343): Springer.Google Scholar
  5. Cao, H., Xie, M., Xue, L., Liu, C., Teng, F., Huang, Y. (2009). Social tag prediction base on supervised ranking model. In Proceeding of ECML/PKDD 2009 discovery challenge workshop (pp. 35–48).Google Scholar
  6. Chen, Y.L., Chang, C.L., Yeh, C.S. (2017). Emotion classification of Youtube videos. Decision Support Systems, 101, 40–50.CrossRefGoogle Scholar
  7. Cook, J., Kenthapadi, K., Mishra, N. (2013). Group chats on Twitter. In Proceedings of the 22nd international conference on world wide web (pp. 225–236): ACM.Google Scholar
  8. El-Kishky, A., Song, Y., Wang, C., Voss, C.R., Han, J. (2014). Scalable topical phrase mining from text corpora. Proceedings of the VLDB Endowment, 8(3), 305–316.CrossRefGoogle Scholar
  9. Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 1(2), 1–14.Google Scholar
  10. Feng, W., & Wang, J. (2012). Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1276–1284): ACM.Google Scholar
  11. Ferragina, P., Piccinno, F., Santoro, R. (2015). On analyzing hashtags in Twitter. In International conference on web and social media (ICWSM) (pp. 110–119): AAAI Press.Google Scholar
  12. Garg, N., & Weber, I. (2008). Personalized, interactive tag recommendation for flickr. In Proceedings of the 2008 ACM conference on recommender systems (pp. 67–74): ACM.Google Scholar
  13. Godin, F., Slavkovikj, V., De Neve, W., Schrauwen, B., Van de Walle, R. (2013). Using topic models for twitter hashtag recommendation. In Proceedings of the 22nd international conference on world wide web (pp. 593–596): ACM.Google Scholar
  14. Heymann, P., Ramage, D., Garcia-Molina, H. (2008). Social tag prediction. In Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval (pp. 531–538): ACM.Google Scholar
  15. Hu, M., Lim, E.P., Jiang, J. (2010). A probabilistic approach to personalized tag recommendation. In 2010 IEEE second international conference on social computing (pp. 33–40): IEEE.Google Scholar
  16. Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 168–177): ACM.Google Scholar
  17. Järvelin, K., & Kekäläinen, J. (October 2002). Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems, 20(4), 422–446. Scholar
  18. Jäschke, R, Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G. (2007). Tag recommendations in folksonomies. In European conference on principles of data mining and knowledge discovery (pp. 506–514): Springer.Google Scholar
  19. Johnson, R., & Zhang, T. (2014). Effective use of word order for text categorization with convolutional neural networks. arXiv:1412.1058.
  20. Konjengbam, A., Ghosh, S., Kumar, N., Singh, M. (2018). Debate stance classification using word embeddings. In International conference on big data analytics and knowledge discovery (pp. 382–395): Springer.Google Scholar
  21. Krestel, R., Fankhauser, P., Nejdl, W. (2009). Latent dirichlet allocation for tag recommendation. In Proceedings of the third ACM conference on recommender systems (pp. 61–68): ACM.Google Scholar
  22. Kumar, N., Mudda, K.Y., Trishal, G., Konjengbam, A., Singh, M., et al. (2019). Where to post: routing questions to right community in community question answering systems. In Proceedings of the ACM india joint international conference on data science and management of data (pp. 136–142): ACM.Google Scholar
  23. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1–167.CrossRefGoogle Scholar
  24. Liu, K., Fang, B., Zhang, W. (2010). Speak the same language with your friends: augmenting tag recommenders with social relations. In Proceedings of the 21st ACM conference on hypertext and hypermedia (pp. 45–50): ACM.Google Scholar
  25. Lu, Y.T., Yu, S.I., Chang, T.C., Hsu, J.Y.J. (2009). A content-based method to enhance tag recommendation. In Twenty-first international joint conference on artificial intelligence.Google Scholar
  26. Menezes, G.V., Almeida, J.M., Belém, F, Gonçalves, M.A., Lacerda, A., De Moura, E.S., Pappa, G.L., Veloso, A., Ziviani, N. (2010). Demand-driven tag recommendation. In Joint European conference on machine learning and knowledge discovery in databases (pp. 402–417): Springer.Google Scholar
  27. Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D.B., Amde, M., Owen, S., et al. (2016). Mllib: machine learning in apache spark. The Journal of Machine Learning Research, 17(1), 1235–1241.MathSciNetzbMATHGoogle Scholar
  28. Mishne, G. (2006). Autotag: a collaborative approach to automated tag assignment for weblog posts. In Proceedings of the 15th international conference on world wide web (pp. 953–954): ACM.Google Scholar
  29. Norvig, P. (2007). How to write a spelling corrector. De:
  30. Perkins, J. (2010). Python text processing with NLTK 2.0 cookbook. Packt Publishing Ltd.Google Scholar
  31. Ramage, D., Dumais, S., Liebling, D. (2010). Characterizing microblogs with topic models. In Fourth international AAAI conference on Weblogs and social media.Google Scholar
  32. Rendle, S., & Schmidt-Thieme, L. (2010). Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the third ACM international conference on web search and data mining (pp. 81–90): ACM.Google Scholar
  33. Ribeiro, I.S., Santos, R.L., Gonçalves, M.A., Laender, A.H. (2015). On tag recommendation for expertise profiling: a case study in the scientific domain. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 189–198): ACM.Google Scholar
  34. Shen, Z., Arslan Ay, S., Kim, S.H., Zimmermann, R. (2011). Automatic tag generation and ranking for sensor-rich outdoor videos. In Proceedings of the 19th ACM international conference on multimedia (pp. 93–102): ACM.Google Scholar
  35. Si, X., & Sun, M. (2008). Tag-lda for scalable real-time tag recommendation. Journal of Information & Computational Science, 6(2), 1009–1016.Google Scholar
  36. Sigurbjörnsson, B., & Van Zwol, R. (2008). Flickr tag recommendation based on collective knowledge. In Proceedings of the 17th international conference on world wide web (pp. 327–336): ACM.Google Scholar
  37. Song, Y., Zhang, L., Giles, C.L. (2011). Automatic tag recommendation algorithms for social recommender systems. ACM Transactions on the Web (TWEB), 5(1), 4.Google Scholar
  38. Sood, S., Owsley, S., Hammond, K.J., Birnbaum, L. (2007). Tagassist: automatic tag suggestion for blog posts. In ICWSM.Google Scholar
  39. Toba, H., Ming, Z.Y., Adriani, M., Chua, T.S. (2014). Discovering high quality answers in community question answering archives using a hierarchy of classifiers. Information Sciences, 261, 101–115.MathSciNetCrossRefGoogle Scholar
  40. Venetis, P., Koutrika, G., Garcia-Molina, H. (2011). On the selection of tags for tag clouds. In Proceedings of the fourth ACM international conference on web search and data mining (pp. 835–844): ACM.Google Scholar
  41. Wang, H., Lu, Y., Zhai, C. (2010). 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 (pp. 783–792): ACM.Google Scholar
  42. Wang, J., Hong, L., Davison, B.D. (2009). Rsdc’09: tag recommendation using keywords and association rules. In DC@PKDD/ECML.Google Scholar
  43. Wang, X., McCallum, A., Wei, X. (2007). Topical n-grams: phrase and topic discovery, with an application to information retrieval. In ICDM (pp. 697–702): IEEE.Google Scholar
  44. Wu, L., Yang, L., Yu, N., Hua, X.S. (2009). Learning to tag. In Proceedings of the 18th international conference on world wide web (pp. 361–370): ACM.Google Scholar
  45. Wu, Y., Zhang, Q., Huang, X., Wu, L. (2009). Phrase dependency parsing for opinion mining. In Proceedings of the 2009 conference on empirical methods in natural language processing, (Vol. 3 pp. 1533–1541): Association for Computational Linguistics.Google Scholar
  46. Xia, X., Lo, D., Wang, X., Zhou, B. (2013). Tag recommendation in software information sites. In Proceedings of the 10th working conference on mining software repositories (pp. 287–296): IEEE Press.Google Scholar
  47. Yang, B., & Manandhar, S. (2014). Tag-based expert recommendation in community question answering. In 2014 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2014) (pp. 960–963): IEEE.Google Scholar
  48. Zhang, N., Zhang, Y., Tang, J. (2009). A tag recommendation system based on contents. ECML PKDD Discovery Challenge 2009 (DC09), pp. 285.Google Scholar
  49. Zhang, X., Zhao, J., LeCun, Y. (2015). Character-level convolutional networks for text classification. In Advances in neural information processing systems (pp. 649–657).Google Scholar

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Authors and Affiliations

  1. 1.Indian Institute of Technology HyderabadSangareddyIndia

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