An Approach for Extracting and Predicting Instance–specific Attribute Values from E–commerce Sitesfor Used Products

Hettiarachchige Dona Nidhana Harshika; Kihaya Sugiura; Naoki Yamada; Masahiro Nishi; Naoki Fukuta

doi:10.52731/ijscai.v1.i2.97

Hettiarachchige Dona Nidhana Harshika Shizuoka University
Kihaya Sugiura Shizuoka University
Naoki Yamada Shizuoka University
Masahiro Nishi Shizuoka University
Naoki Fukuta Shizuoka University

DOI: https://doi.org/10.52731/ijscai.v1.i2.97

Keywords: E-Commerce, Used Products, Instance–Specific Attributes Values, Prototype System, User Interface

Abstract

To obtain valuable products, reviews, rating, and other important product information given by sellers are important issues for consumers for their purchasing decisions. However, in the used–products purchasing scenario via e-commerce sites, consumers may consider much more attributes about the products than that for purchasing new products. This is due to the need for understanding instance-specific conditions before optimize their purchasing decision. Thus, the available descriptions for a used product may differ in each other, and it may drop some important information to make a decision for consumers. In this paper, we proposed a design and implementation of a system that supports users to investigate instance-specific attribute values by extracting and predicting attributes and values of used items that are selling on e-commerce sites. Our key idea is preparing a system to identify instance-specific attributes as well as their values from the descriptions of items while browsing the e-commerce sites. Also we have implemented floating style user interface to present the extracted and predicted instance-specific attributes. Our system also applies machine learning techniques to predict missing attributes values.

Author Biography

Hettiarachchige Dona Nidhana Harshika, Shizuoka University

Department Of Informatics, 2nd Grade Master Student.

References

D.H Park, H D. Kim, C.X Zhai and L Guo, Retrieval of relevant Opinion Sentences for new products , ACM SIGIR, pages 393–402, 2015.

Xutao.Li, Gao Cong, Xiao.Li, uan.Anh and Shonali Krishnaswamy, Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation , ACM SIGIR, pages 433–442, 2015.

M. Ye, P. Yin, W.C. Lee, and D.L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation, In SIGIR, pages 325–334. ACM–2011.

C. Cheng, H. Yang, I. King, and M.R. Lyu. Fused matrix factorization with geographical and social influence in location-based social networks, In AAAI–2012, pages 17–23.

A. Noulas, S. Scellato, N. Lathia, and C. Mascolo. A random walk around the city: New venue recommendation in location-based social networks, In PASSAT, pages 144–153. IEEE–2012.

J. J. Levandoski, M. Sarwat, A. Eldawy, and M. F. Mokbel, LARS: A location-aware recommender system, In ICDE, pages 450–461. IEEE–2012.

H. Gao, J. Tang, X. Hu, and H. Liu, Exploring temporal effects for location recommendation on location-based social networks, In Recsys, pages 93–100. ACM–2013

B. Liu, Y. Fu, Z. Yao, and H. Xiong, Learning geographical preferences for point-ofinterest recommendation, In SIGKDD, pages 1043–1051. ACM–2013

A Beykikhoshk , O Arandjelovic , D Phung and S Venkatesh, Overcoming Data Scarcity of Twitter: Using Tweets as Bootstrap with Application to Autism-Related Topic Content Analysis , International conference on advances in Social networks analysis and Mining : IEEE/ACM–2015.

L. Zhou and P. Chaovalit, Ontology-supported polarity mining. Journal of the American Society for Information Science and technology, pages 98–110, January 2008.

I. Bhattacharya, S. Godbole, and S. Joshi, Structured entity identification and document categorization: two tasks with one joint model, In Proceedings of ACM KDD–2008, pages 25–33.

J. Yu, Z. J. Zha, M. Wang, K. Wang, and T. S. Chua, Domain-assisted product aspect hierarchy generation: towards hierarchical organization of unstructured consumer reviews, In Proceedings of EMNLP 2011, pages 140–50, 2011.

D. H. Park, C. Zhai and L. Guo, Speclda: Modeling product reviews and specifications to generate augmented specifications, In Proceedings of the 2015 SIAM International Conference on Data Mining. SIAM, 2015, pages 411–418.

T. Ozono, S. Shiramatsu, and T. Shintani, A Stable Layered Canvas Mechanism for Collaborative Web Applications, Proc. 2015 IEEE/WIC/ACM WI2015, pp.101–106, 2015.

J. Ben Schafer, Joseph A. Konstan and John Riedl, E–Commerce Recommendation Applications, GroupLens Research Project–2001–Department of Computer Science and Engineering University of Minnesota Minneapolis, MN 55455 1-612-625-4002, pp.01–24.

A. Fayazi, K. Lee, J. Caverlee, and A. Squicciarini, Uncovering Crowdsourced Manipulation of Online Reviews, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval–2015, pp. 233–242.

D. kotzias, M. Denil, N. de Freitas and P. Smyth, From Group to Individual Labels Using Deep Features, Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining–2015, pp. 597–606.

Used Products details on, http://www.amazon.com/gp/offerlisting/B00VHSXBUA/ref=dp_olp_used?ie=UTF8&condition=used.

Extracted products description data on, http://www.amazon.com/gp/offer–listing/B00VHSXBUA/ref=dp_olp_used?ie=UTF8&condition=used.

J. Ben Schafer, Joseph A. Konstan and John Riedl, E–Commerce Recommendation Applications, GroupLens Research Project Department of Computer Science and Engineering University of Minnesota Minneapolis, 2001–MN 55455 1–612–625–4002, pp.01–24.

Extracted Product items information on, https://www.amazon.com/gp/offerlisting/B00F3J4B5S/ref=olp_twister_child_ie=UTF8&mv_color_name=2&mv_size_name=0&qid=1500971645&sr=1-2.