An Approach For Extracting and Predicting Instance-specific Attribute Values from E-commerce Sites for Used Products

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

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.


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References


D.H Park, H D. Kim, C.X Zhai and L Guo, ” Retrieval of relevant Opinion Sentences 58 S. Takahashi, A. Bossard, T. Matsuo, T. Fukushima

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

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

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.

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, IEEE, 2012, pp. 144–153.

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

H. Gao, J. Tang, X. Hu, and H. Liu.”Exploring temporal effects for location recommendation on location-based social networks”. In Recsys, ACM 1013, pp. 93–100.

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

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”, 59(1):98–110, 2008.

I. Bhattacharya, S. Godbole, and S. Joshi.” Structured entity identication and document categorization: two tasks with one joint model” In Proceedings of ACM KDD 2008, pp. 25–33,20–08.

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, pp. 140–150, 2011.

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

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 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 2015, Conference on Research and Development in Information Retrieval, pp. 233–242.

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

Used product details on ” http://www.amazon.com/gp/offerlisting/B00VHSXBUA/ref=dp olp used?ie=UTF8&condition=used”

Extracted product description Data from ” http://www.amazon.com/gp/offer–listing/B00VHSXBUA/ref =dp olp used?ie=UTF8&condition=used”.


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