A Systematic Literature Review on the Product Ranking Methods

Ahmad Choirun Najib(1*), Nur Aini Rakhmawati(2),

(1) Information Systems Department, Institut Teknologi Sepuluh Nopember
(2) Information Systems Department, Institut Teknologi Sepuluh Nopember
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v5i1.8029

Abstract

The vast amount of online products data such as product properties, or product reviews plays an essential role in providing better information to the consumers to make a purchase decision. Thus, product ranking is a valuable research topic while many methods proposed by researchers in different approaches and case studies. This paper aims to develop a Systematic Literature Review (SLR) to summarise existing research and finding new gaps in product ranking research. We develop SLR by defining inclusion criteria, initiating preliminary findings, selecting primary studies and summarizing the outcome of results. We proposed three dimensions as research questions. It consists of ranking item types of product ranking, approaches of product ranking and dataset characteristics of each study. First, we found three ranking item types of product ranking that indicate what will be rank in the studies. It consists of product ranking, aspect ranking, and review ranking. Second, there are four approaches, namely: collaborative filtering, content-based recommendation, hybrid-based and knowledge-based. Third, datasets characteristics summarise the information of datasets like the type of data and statistics. Also, we found new gaps by identifying each dimension to positioning for further research in the future.

Keywords

product ranking; aspect ranking; review ranking; ranking methods; a systematic review

Full Text:

PDF

References

D. Sorokina and E. Cantú-paz, “Amazon Search : The Joy of Ranking Products,” Proc. 39th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., 2016.

Yin-Fu Huang and Heng Lin, “Web product ranking using opinion mining,” in 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 2013, pp. 184–190.

B. B. Alengadan and S. S. Khan, “Modified aspect/feature based opinion mining for a product ranking system,” in 2018 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), 2018, pp. 1–5.

Y. Liu, J. W. Bi, and Z. P. Fan, “Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory,” Inf. Fusion, 2017.

A. K. J and S. Abirami, “Aspect-based opinion ranking framework for product reviews using a Spearman’s rank correlation coefficient method,” Inf. Sci. (Ny)., 2018.

E. Najmi, K. Hashmi, Z. Malik, A. Rezgui, and H. U. Khan, “CAPRA: a comprehensive approach to product ranking using customer reviews,” Computing, 2015.

R. Krestel and N. Dokoohaki, “Diversifying customer review rankings,” Neural Networks, vol. 66, pp. 36–45, Jun. 2015.

M. Scholz, J. Pfeiffer, and F. Rothlauf, “Using PageRank for non-personalized default rankings in dynamic markets,” Eur. J. Oper. Res., 2017.

B. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature reviews in Software Engineering Version 2.3,” Engineering, 2007.

M. Zhang, X. Guo, and G. Chen, “Prediction uncertainty in collaborative filtering: Enhancing personalized online product ranking,” Decis. Support Syst., 2016.

R. Krestel and N. Dokoohaki, “Diversifying customer review rankings,” Neural Networks, 2015.

X. Yang, G. Yang, and J. Wu, “Integrating rich and heterogeneous information to design a ranking system for multiple products,” Decis. Support Syst., 2016.

C. L. Sabharwal and B. Anjum, “An SVD-Entropy and bilinearity based product ranking algorithm using heterogeneous data,” J. Vis. Lang. Comput., 2017.

G. Kaur and R. Bhatia, “Semantic Product Ranking Model (SePRaM) using PNN over the Heuristic Product Data,” Int. J. Comput. Appl., 2016.

Zheng-Jun Zha, Jianxing Yu, Jinhui Tang, Meng Wang, and Tat-Seng Chua, “Product Aspect Ranking and Its Applications,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 5, pp. 1211–1224, May 2014.

M. Arun Manicka Raja, S. G. Winster, R. Saravanan, and S. Swamynathan, “ProRankSys: Ranking consumer products by predicting opinion’s weight on reviews,” in Proceedings of IEEE International Conference on Computer Communication and Systems ICCCS14, 2014, pp. 033–038.

N. R. Bhamre and N. N. Patil, “Aspect rating analysis based product ranking,” in 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016, pp. 197–202.

S. A. A. A. Alrababah, K. H. Gan, and T.-P. Tan, “Product aspect ranking using sentiment analysis and TOPSIS,” in 2016 Third International Conference on Information Retrieval and Knowledge Management (CAMP), 2016, pp. 13–19.

T. Sangeetha, N. Balaganesh, and K. Muneeswaran, “Aspects based opinion mining from online reviews for product recommendation,” in 2017 International Conference on Computational Intelligence in Data Science(ICCIDS), 2017, pp. 1–6.

M. Shahbazi, M. Wiley, and V. Hristidis, “IRanker: Query-specific ranking of reviewed items,” in Proceedings - International Conference on Data Engineering, 2017.

Y. H. Kuo and W. H. Hsu, “Feature Learning with Rank-Based Candidate Selection for Product Search,” in Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 2018.

R. Zhang, M. Gao, X. He, and A. Zhou, “Learning user credibility for product ranking,” Knowl. Inf. Syst., 2016.

S. Li, Z. Ming, Y. Leng, and J. Guo, “Product ranking using hierarchical aspect structures,” J. Intell. Inf. Syst., 2017.

Z. P. Fan, Y. Xi, and Y. Liu, “Supporting consumer’s purchase decision: a method for ranking products based on online multi-attribute product ratings,” Soft Comput., 2018.

J. S. J. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” Proc. 14th Conf. Uncertain. Artif. Intell., 1998.

Article Metrics

Abstract view(s): 922 time(s)
PDF: 446 time(s)

Refbacks

  • There are currently no refbacks.