A Hybrid Recommendation Approach for One-and-Only Items
Many mechanisms have been developed to deliver only relevant information to the web users and prevent information overload. The most popular recent developments in the e-commerce domain are the user-preference based personalization and recommendation techniques. However, the existing techniques have a major drawback – poor accuracy of recommendation on one-and-only items – because most of them do not understand the item’s semantic features and attributes. Thus, in this study, we propose a novel Semantic Product Relevance model and its attendant personalized recommendation approach to assist Export business selecting the right international trade exhibitions for market promotion. A recommender system, called Smart Trade Exhibition Finder (STEF), is developed to tailor the relevant trade exhibition information to each particular business user. STEF reduces significantly the time, cost and risk faced by exporters in selecting, entering and developing international markets. In particular, the proposed model can be used to overcome the drawback of existing recommendation techniques.
KeywordsRecommender System Semantic Similarity Collaborative Filter Mean Absolute Error Recommendation Technique
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- 1.Accenture, eGovernment leadership: high performance, maximum value, Fifth Annual Accenture eGovernment Study (2004), Available at, http://www.accenture.com/xdoc/en/industries/government/gove_egov_value.pdf
- 2.Aggarwal, C.C., Wolf, J., Wu, K., Yu, P.S.: Horting hatches an egg: an new graph-theoretic approach to collaborative filtering. In: Presented at Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA (1999)Google Scholar
- 3.Amoroso, D.L., Reinig, B.A.: Personalization management systems: minitrack introduction. In: Presented at Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS 2004), Big Island, Hawaii (2004)Google Scholar
- 6.Comm. ACM, Special issue on recommender system. Communications of the ACM 40, 5 (1997)Google Scholar
- 7.Condliff, M., Lewis, D.D., Madigan, D., Posse, C.: Bayesian mixed-effects models for recommender systems. In: Presented at Proceedings of the SIGIR 1999 Workshop on Recom-mender Systems: Algorithms and Evaluation, Berkeley, CA (1999)Google Scholar
- 12.Pretschner, A., Gauch, S.: Ontology based personalized search. In: Presented at Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence, Chicago, Illinois (1999)Google Scholar
- 14.Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for E-commerce. In: Presented at Proceedings of the 2nd ACM Conference on Electronic Commerce, Minneapolis, Minnesota, USA (2000)Google Scholar
- 15.Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithm. In: Presented at Proceedings of the 10th International World Wide Web Conference, Hong Kong, China (2001)Google Scholar
- 16.Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B., Riedl, J.: Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system. In: Presented at Proceedings of the 1998 ACM Conference on Computer Support Cooperative Work, Seattle, WA, USA (1998)Google Scholar