Improved Feature-Specific Collaborative Filtering Model for the Aspect-Opinion Based Product Recommendation

  • J. SangeethaEmail author
  • V. Sinthu Janita Prakash
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 750)


Utilizing the benefits of Internet services for the online purchase and online advertising has increased tremendously in the recent year. Therefore, the customer reviews of the product play a major role in the product sale and effectively describe the features quality. Thus, the large size of words and phrases in an unstructured data is converted into numerical values based on the opinion prediction rule. This paper proposes the Novel Product Recommendation Framework (NPRF) for the prediction of overall opinion and estimates the rating of the product based on the user reviews. Initially, preprocessing the set of large size customer reviews to extract the relevant keywords with the help of stop word removal, PoS tagger, Slicing, and the normalization processes. SentiWordNet library database is applied to categorize the keywords which are in the form of positive and negative based polarity. After extracting the related keywords, the Inclusive Similarity-based Clustering (ISC) method is performed to cluster the user reviews based on the positive and negative polarity. The proposed Improved Feature-Specific Collaborative Filtering (IFSCF) model for the feature-specific clusters is used to evaluate the product strength and weakness and predict the corresponding aspects and its opinions. If the user query is matched with the cache memory then shows the opinion or else extract from the knowledge database. This optimal memory access process is termed as the Memory Management Model (MMM). Then, the overall opinion of the products is determined based on the Novel Product Feature-based Opinion Score Estimation (NPF-OSE) process. Finally, the top quality query result and the recommended solution are retrieved. Thus the devised NPRF method enriches its capability in outperforming other prevailing methodologies in terms of precision, recall, F-measure, RMSE, and the MAE.


Customer reviews SentiWordNet Collaborative filtering Aspects Opinions Clustering Memory management model 


  1. 1.
    Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)CrossRefGoogle Scholar
  2. 2.
    Sivapalan, S., Sadeghian, A., Rahnama, H., Madni, A.M.: Recommender systems in e-commerce. In: World Automation Congress (WAC), pp. 179–184. IEEE (2014)Google Scholar
  3. 3.
    Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)CrossRefGoogle Scholar
  4. 4.
    Rodrigues, F., Ferreira, B.: Product recommendation based on shared customer’s behaviour. Procedia Comput. Sci. 100, 136–146 (2016)CrossRefGoogle Scholar
  5. 5.
    Riyaz, P., Varghese, S.M.: A scalable product recommendations using collaborative filtering in hadoop for bigdata. Procedia Technology 24, 1393–1399 (2016)CrossRefGoogle Scholar
  6. 6.
    Yang, S., Korayem, M., AlJadda, K., Grainger, T., Natarajan, S.: Combining content-based and collaborative filtering for job recommendation system: a cost-sensitive Statistical Relational Learning approach. Knowl.-Based Syst. 136, 37–45 (2017)CrossRefGoogle Scholar
  7. 7.
    Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41, 1–10 (2014)CrossRefGoogle Scholar
  8. 8.
    Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. GeoInformatica 19(3), 525–565 (2015)CrossRefGoogle Scholar
  9. 9.
    Lei, X., Qian, X., Zhao, G.: Rating prediction based on social sentiment from textual reviews. IEEE Trans. Multimedia 18(9), 1910–1921 (2016)CrossRefGoogle Scholar
  10. 10.
    Salehan, M., Kim, D.J.: Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics. Decis. Support Syst. 81, 30–40 (2016)CrossRefGoogle Scholar
  11. 11.
    Pourgholamali, F., Kahani, M., Bagheri, E., Noorian, Z.: Embedding unstructured side information in product recommendation. Electron. Commer. Res. Appl. 25, 70–85 (2017)CrossRefGoogle Scholar
  12. 12.
    Opinion Mining, Sentiment Analysis, and Opinion Spam Detection (May 15, 2004).
  13. 13.
    Sangeetha, J., Prakash, V.S.J., Bhuvaneswari, A.: Dual access cache memory management recommendation model based on user reviews. IJCSRCSEIT 169–179 (2017)Google Scholar
  14. 14.
    Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 425–434. ACM (2017)Google Scholar
  15. 15.
    Ren, Z., Liang, S., Li, P., Wang, S., de Rijke, M.: Social collaborative viewpoint regression with explainable recommendations. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp 485–494. ACM (2017)Google Scholar
  16. 16.
    Najafabadi, M.K., Mahrin, MNr, Chuprat, S., Sarkan, H.M.: Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Comput. Hum. Behav. 67, 113–128 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Cauvery College for WomenTrichyIndia
  2. 2.Department of Computer ScienceCauvery College for WomenTrichyIndia

Personalised recommendations