Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendations

These days users are able to save their time and effort by purchasing products online via various e-commerce websites. Their experience with a product exists in the form of textual reviews/feedbacks provided by them. Recommender systems offer personalized choices to users by capturing their interests and preferences. Through this paper identification of underlying topics using existing topic modeling techniques in user provided reviews of Moto e5 mobile on e-commerce website Amazon has been done and these techniques contrasted. Topic modeling is unsupervised learning technique used to identify hidden topics from a document (all the reviews of a product in this paper’s context). Coherence score, a measure of goodness of a topic reflecting the quality of human judgment compares these techniques. The higher the coherence score, the topic is more coherent. Experiments performed reveal that LDA technique performed better on the scrapped dataset.

[1]  Ruslan Salakhutdinov,et al.  Evaluation methods for topic models , 2009, ICML '09.

[2]  Fernando Diaz,et al.  A User-Centered Approach to Evaluating Topic Models , 2004, ECIR.

[3]  Dongwon Lee,et al.  l-Injection: Toward Effective Collaborative Filtering Using Uninteresting Items , 2019, IEEE Trans. Knowl. Data Eng..

[4]  Divakar Ch,et al.  PROBABILISTIC TOPIC MODELING AND ITS VARIANTS – A SURVEY , 2018 .

[5]  Bhagyashree Vyankatrao Barde,et al.  An overview of topic modeling methods and tools , 2017, 2017 International Conference on Intelligent Computing and Control Systems (ICICCS).

[6]  Rui Duan,et al.  Hybrid collaborative filtering for high-involvement products: A solution to opinion sparsity and dynamics , 2015, Decis. Support Syst..

[7]  SangKeun Lee,et al.  Examining the performance of topic modeling techniques in Twitter trends extraction , 2014, The International Conference on Information Networking 2014 (ICOIN2014).

[8]  Xiaolong Wang,et al.  A comparative study of topic models for topic clustering of Chinese web news , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[9]  Satish R. Devane,et al.  Recommendation Systems: Past, Present and Future , 2018, 2018 Eleventh International Conference on Contemporary Computing (IC3).

[10]  Wray L. Buntine,et al.  Topic Model : Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon , 2014 .

[11]  Kweku-Muata Osei-Bryson,et al.  RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers , 2019, Information Systems Frontiers.

[12]  George A. Sielis,et al.  Recommender Systems Review of Types, Techniques, and Applications , 2015 .

[13]  Sergey I. Nikolenko,et al.  Topic modelling for qualitative studies , 2017, J. Inf. Sci..

[14]  Yong Wang,et al.  A hybrid user similarity model for collaborative filtering , 2017, Inf. Sci..

[15]  Jaeki Song,et al.  An Empirical Comparison of Four Text Mining Methods* , 2010, J. Comput. Inf. Syst..

[16]  Hong Yu,et al.  Sentiment based matrix factorization with reliability for recommendation , 2019, Expert Syst. Appl..

[17]  John D. Lafferty,et al.  Correlated Topic Models , 2005, NIPS.

[18]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[19]  Francisco J. Peña,et al.  Unsupervised Context-Driven Recommendations Based On User Reviews , 2017, RecSys.

[20]  LiangKun,et al.  Hybrid collaborative filtering for high-involvement products , 2015 .

[21]  Kenneth E. Shirley,et al.  LDAvis: A method for visualizing and interpreting topics , 2014 .

[22]  Ajeet Kumar Pandey,et al.  Topic Model Based Opinion Mining and Sentiment Analysis , 2018, 2018 International Conference on Computer Communication and Informatics (ICCCI).

[23]  Gourav,et al.  Various Types of Image Noise and De-noising Algorithm , 2017 .

[24]  Khalid Alfalqi,et al.  A Survey of Topic Modeling in Text Mining , 2015 .

[25]  Mehrdad Jalali,et al.  Combining trust in collaborative filtering to mitigate data sparsity and cold-start problems , 2014, 2014 4th International Conference on Computer and Knowledge Engineering (ICCKE).

[26]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[27]  Magdalini Eirinaki,et al.  Aspect-based opinion mining and recommendationsystem for restaurant reviews , 2014, RecSys '14.

[28]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[29]  Chih-Ya Shen,et al.  A Consumer Review-Driven Recommender Service for Web E-Commerce , 2017, 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA).

[30]  Dunja Mladenic,et al.  Data Sparsity Issues in the Collaborative Filtering Framework , 2005, WEBKDD.

[31]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[32]  Yuanyuan Wang,et al.  A Recommender System based on Detected Users' Complaints by Analyzing Reviews , 2018, IUI Companion.

[33]  Luigi Di Caro,et al.  Text Segmentation with Topic Modeling and Entity Coherence , 2016, HIS.

[34]  Pasquale Lops,et al.  A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users' Reviews , 2017, RecSys.

[35]  Tsvi Kuflik,et al.  Visualizing Reviews Summaries as a Tool for Restaurants Recommendation , 2018, IUI.

[36]  Michael Röder,et al.  Exploring the Space of Topic Coherence Measures , 2015, WSDM.

[37]  Tein-Yaw Chung,et al.  Testing and evaluating recommendation algorithms in internet of things , 2016, Journal of Ambient Intelligence and Humanized Computing.

[38]  Andrew McCallum,et al.  Optimizing Semantic Coherence in Topic Models , 2011, EMNLP.

[39]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[40]  John K. Debenham,et al.  Recommender System Based on Consumer Product Reviews , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[41]  Diego Fernández,et al.  Comparison of collaborative filtering algorithms , 2011, ACM Trans. Web.

[42]  Hyung Jun Ahn,et al.  A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..

[43]  Weiqing Gu,et al.  Expert Opinion and Coherence Based Topic Modeling , 2018 .

[44]  Chetan Kalyan,et al.  Recommendation of High Quality Representative Reviews in e-commerce , 2017, RecSys.

[45]  Tao Mei,et al.  Exploring Users' Internal Influence from Reviews for Social Recommendation , 2019, IEEE Transactions on Multimedia.

[46]  Danial Hooshyar,et al.  Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review , 2018, J. Inf. Sci..

[47]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[48]  Li Yang,et al.  A trust-based collaborative filtering algorithm for E-commerce recommendation system , 2018, Journal of Ambient Intelligence and Humanized Computing.

[49]  Bijendra Kumar,et al.  A Survey on Journey of Topic Modeling Techniques from SVD to Deep Learning , 2017 .

[50]  Pushpendra Kumar,et al.  A new approach for rating prediction system using collaborative filtering , 2019 .

[51]  Luiz Pereira Calôba,et al.  Effects of Data Sparsity on Recommender Systems based on Collaborative Filtering , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[52]  Pradnya Bhagat,et al.  A comparative study of feature extraction methods from user reviews for recommender systems , 2018, COMAD/CODS.

[53]  Younghee Park,et al.  Impact of reviewer social interaction on online consumer review fraud detection , 2017, Journal of Big Data.

[54]  Guoqing Chen,et al.  Finding users preferences from large-scale online reviews for personalized recommendation , 2016, Electronic Commerce Research.

[55]  M. Jalili,et al.  Evaluating Collaborative Filtering Recommender Algorithms: A Survey , 2018, IEEE Access.

[56]  Yue Lu,et al.  Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA , 2011, Information Retrieval.

[57]  Xia Feng,et al.  Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey , 2017, Multimedia Tools and Applications.