Cited By
View all- Chew LHaw SSubramaniam S(2021)A hybrid recommender system based on data enrichment on the ontology modellingF1000Research10.12688/f1000research.73060.110(937)Online publication date: 17-Sep-2021
Recommender System for Retail Domain: An Insight on Techniques and Evaluations
Pages 9 - 13
Abstract
Recommender system has been developed as a useful tool especially when we reached the era of big data and in the meanwhile the internet has been overwhelming with lots of choices. There is a need for people to filter the information to search for their needs and wants efficiently. E-commerce website such as Amazon and Netflix have been using recommender system to build and boost their sales through the personalization recommendation. With the success in the e-commerce area, researchers are keen on finding a method to boost traditional offline retailer sales thru the recommender system. Therefore, in this paper, we introduced the existing recommender system and discuss the method of filtering of each method. Then, we provide the overview of the recent paper in retailer and e-commerce domain to provide the insight and trends such as the filtering techniques and evaluation metric used. Several possible research direction has been discussed based on the current trends and problems.
References
[1]
Linden, G., Smith, B. and York, J. 2003. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing. 7, 1, 76--80.
[2]
Schafer, J.B., Konstan, J. and Riedi, J. 1999. Recommender systems in e-commerce. Proceedings of the 1st ACM conference on Electronic commerce - EC 99.
[3]
Bobadilla, J., Ortega, F., Hernando, A. and Alcalá, J. 2011. Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-Based Systems. 24, 8, 1310--1316.
[4]
Goldberg, D., Nichols, D., Oki, B.M. and Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Communications of the ACM. 35, 12, 61--70. http://doi.org/10.1145/138859.138867
[5]
Harper, F.M. and Konstan, J.A. 2016. The MovieLens Datasets. ACM Transactions on Interactive Intelligent Systems. 5, 4, 1--19. http://doi.org/10.1145/2827872
[6]
Gomez-Uribe, C. and Hunt, N. 2016. The Netflix Recommender System. ACM Transactions on Management Information Systems 6, 4, 1--19. http://doi.org/10.1145/2843948
[7]
Hwangbo, H., Kim, Y. and Cha, K. 2018. Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications 28, 94--101. http://doi.org/10.1016/j.elerap.2018.01.012
[8]
Y. Xia, G. D. Fabbrizio, S. Vaibhav, and A. Datta. 2017. A Content-based Recommender System for E-commerce Offers and Coupons. eCOM@SIGIR.
[9]
Pripužić, K., Zarko, I., Podobnik, V., et al. 2013. Building an IPTV VoD recommender system: An experience report. Proceedings of the 12th International Conference on Telecommunications, 155--162.
[10]
Bahramian, Z. and Ali Abbaspour, R. 2015. An Ontology-Based Tourism Recommender System Based On Spreading Activation Model. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5, 83--90. http://doi.org/10.5194/isprsarchives-xl-1-w5-83-2015
[11]
Razia Sulthana, A. and Ramasamy, S. 2019. Ontology and context based recommendation system using Neuro-Fuzzy Classification. Computers & Electrical Engineering 74, 498--510. http://doi.org/10.1016/j.compeleceng.2018.01.034
[12]
Li, Y., Lin, L. and Ho, C. 2017. A social route recommender mechanism for store shopping support. Decision Support Systems 94, 97--108. http://doi.org/10.1016/j.dss.2016.11.004
[13]
Beel, J., Gipp, B., Langer, S. and Breitinger, C. 2015. Research-paper recommender systems: a literature survey. International Journal on Digital Libraries 17, 4, 305--338. http://doi.org/10.1007/s00799-015-0156-0
[14]
Sharma, R. and Singh, R. 2016. Evolution of Recommender Systems from Ancient Times to Modern Era: A Survey. Indian Journal of Science and Technology 9, 20. http://doi.org/10.17485/ijst/2016/v9i20/88005
[15]
Tarus, J., Niu, Z. and Mustafa, G. 2017. Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artificial Intelligence Review 50, 1, 21--48.
[16]
Colombo-Mendoza, L., Valencia-García, R., Rodríguez-González, A., Alor-Hernández, G. and Samper-Zapater, J. 2015. RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes. Expert Systems with Applications 42, 3, 1202--1222. http://doi.org/10.1016/j.eswa.2014.09.016
[17]
Obeid, C., Lahoud, I., Khoury, H.E., and Champin, P.-A. 2018. Ontology-based Recommender System in Higher Education. Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW 18. http://doi.org/10.1145/3184558.3191533
[18]
Yanes, N., Sassi, S.B., and Ghezala, H.H.B. 2017. Ontology-based recommender system for COTS components. Journal of Systems and Software 132, 283--297. http://doi.org/10.1016/j.jss.2017.07.031
[19]
Sheikh, A.A., Arif, T. and Malik, M.B. 2018. Framework for Opinion Based Product Recommender System. National Conference on Recent Advances in Computer Science and IT (NCRACIT) International Journal of Scientific Research in Computer Science, Engineering and Information Technology 4, 1: 17--21
[20]
Osman, N.A. 2019. Contextual Sentiment Based Recommender System to Provide Recommendation in the Electronic Products Domain. International Journal of Machine Learning and Computing 9, 4 (2019), 425--431.
[21]
Lin, K.-P., Shen, C.-Y., Chang, T.-L., and Chang, T.-M. 2017. A Consumer Review-Driven Recommender Service for Web E-Commerce. 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA). http://doi.org/10.1109/soca.2017.35
[22]
Jing, N., Jiang, T., Du, T. and Sugumaran, V. 2017. Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese ecommerce website. Electronic Commerce Research 18, 1: 159--179. http://doi.org/10.1007/s10660-017-9275-6
[23]
Kouki, P., Fountalis, I., Vasiloglou, N., et al. 2019. Product collection recommendation in online retail. Proceedings of the 13th ACM Conference on Recommender Systems. http://doi.org/10.1145/3298689.3347003
[24]
Ding, L., Han, B., Wang, S., Li, X., and Song, B. 2017. User-centered recommendation using US-ELM based on dynamic graph model in E-commerce. International Journal of Machine Learning and Cybernetics (IJMLC)10, 4: 693--703.
[25]
Agarwal, P., Vempati, S. and Borar, S. 2018. Personalizing Similar Product Recommendations in Fashion E-commerce. arXiv preprint arXiv:1806.11371.
[26]
Shaikh, S., Rathi, S., and Janrao, P. 2017. Recommendation System in E-Commerce Websites: A Graph Based Approached. 2017 IEEE 7th International Advance Computing Conference (IACC). http://doi.org/10.1109/iacc.2017.0189
[27]
Luo, F., Ranzi, G., Wang, X. and Dong, Z. 2019. Social Information Filtering-Based Electricity Retail Plan Recommender System for Smart Grid End Users. IEEE Transactions on Smart Grid 10, 1, 95--104.
[28]
Dimyati, H. and Agasi, R. 2018. Collaborative Filtering in an Offline Setting Case Study: Indonesia Retail Business. Communications in Computer and Information Science Data Mining, 223--232.
[29]
Mettouris, C., Achilleos, A., Kapitsaki, G., and Papadopoulos, G.A. 2018. The UbiCARS Model-Driven Framework: Automating Development of Recommender Systems for Commerce. Lecture Notes in Computer Science Ambient Intelligence, 37--53. http://doi.org/10.1007/978-3-030-03062-9_3
[30]
Poulopoulos, D. and Kyriazis, D. 2017. Collaborative Filtering for Producing Recommendations in the Retail Sector. Information Systems Lecture Notes in Business Information Processing: 662--669.
[31]
Jia, R., Li, R., Yu, M. and Wang, S. 2017. E-commerce purchase prediction approach by user behavior data. 2017 International Conference on Computer, Information and Telecommunication Systems (CITS).
[32]
Saini, S., Saumya, S., and Singh, J.P. 2017. Sequential Purchase Recommendation System for E-Commerce Sites. Computer Information Systems and Industrial Management Lecture Notes in Computer Science, 366--375. http://doi.org/10.1007/978-3-319-59105-6_31
[33]
Nilashi, M., Ibrahim, O., and Bagherifard, K. 2018. A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications 92, 507--520. http://doi.org/10.1016/j.eswa.2017.09.058
[34]
Prévost, B., Janssen, J.L., Camacaro, J.R., and Bessega, C. 2018. Deep inventory time translation to improve recommendations for real-world retail. Proceedings of the 12th ACM Conference on Recommender Systems.
[35]
Selvarajah, K., Kobti, Z., and Kargar, M. 2019. A Cultural Algorithm for Determining Similarity Values Between Users in Recommender Systems. Applications of Evolutionary Computation Lecture Notes in Computer Science: 270--283.
[36]
Chu, P.-M. and Lee, S.-J. 2017. A novel recommender system for E-commerce. 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). http://doi.org/10.1109/cisp-bmei.2017.8302310
[37]
Ramesh, B. and Reeba, R. 2017. Secure recommendation system for E-commerce website. 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT).
[38]
Gaikwad, R., Udmale, S. and Sambhe, V., 2017. E-commerce Recommendation System Using Improved Probabilistic Model. Information and Communication Technology for Sustainable Development, pp. 277--284.
[39]
Hanke, J., Hauser, M., Dürr, A. and Thiesse, F. 2018. Redefining the Offline Retail Experience: Designing Product Recommendation Systems for Fashion Stores. ECIS.
[40]
Wang, F., Wen, Y., Guo, T., Chen, J. and Cao, B. 2018. Personalized Commodity Recommendations of Retail Business Using User Feature Based Collaborative Filtering. 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications.
Index Terms
- Recommender System for Retail Domain: An Insight on Techniques and Evaluations
Recommendations
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
Exploiting Social Tagging in a Web 2.0 Recommender System
Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take ...
Comments
Information & Contributors
Information
Published In
June 2020
219 pages
ISBN:9781450377034
DOI:10.1145/3408066
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Central Queensland University
- DUT: Dalian University of Technology
- University of Wollongong, Australia
- Swinburne University of Technology
- University of Technology Sydney
- National Tsing Hua University: National Tsing Hua University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 August 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCMS '20
ICCMS '20: The 12th International Conference on Computer Modeling and Simulation
June 22 - 24, 2020
QLD, Brisbane, Australia
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 187Total Downloads
- Downloads (Last 12 months)11
- Downloads (Last 6 weeks)0
Reflects downloads up to 20 Feb 2025
Other Metrics
Citations
Cited By
View all- Chew LHaw SSubramaniam S(2021)A hybrid recommender system based on data enrichment on the ontology modellingF1000Research10.12688/f1000research.73060.110(937)Online publication date: 17-Sep-2021
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in