A semantic-expansion approach to personalized knowledge recommendation

Decision Support Systems

Volumn 45, Issue 3, 2008, Pages 401-412

  Liang, Ting Peng ^a   Yang, Yung Fang ^b   Chen, Deng Neng ^c   Ku, Yi Cheng ^d  

^a NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

^b Institute Information Industry   (Taiwan)

^c NATIONAL PINGTUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^d PROVIDENCE UNIVERSITY   (Taiwan)

Author keywords

Customization knowledge management; Electronic commerce; Internet recommendation systems; Personalization; Semantic expansion method

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; COMPUTER NETWORKS; CUSTOMER SATISFACTION; INDUSTRIAL MANAGEMENT; INDUSTRY; INFORMATION MANAGEMENT; INFORMATION THEORY; INTERNET; KNOWLEDGE MANAGEMENT; MANAGEMENT; MANAGEMENT INFORMATION SYSTEMS; MARKETING; SEMANTICS; SOCIETIES AND INSTITUTIONS; TECHNOLOGY;

(OTDR) TECHNOLOGY; CUSTOMER PROFILES; CUSTOMIZED DELIVERY; ELSEVIER (CO); EMPIRICAL STUDIES; FAST RESPONSE; PERSONALIZED SERVICES (PS); RAPID PROPAGATION; RECOMMENDATION SYSTEMS; RELATED PRODUCTS; USER INTERESTS; USER PROFILING;

INFORMATION TECHNOLOGY;

EID: 44849134222     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2007.05.004     Document Type: Article

References (56)

1. Adomavicius G., and Tuzhilin A. Personalization techniques: a process-oriented perspective. Communications of the ACM 48 10 (2005) 83-90
2. Ahn H.J. Utilizing popularity characteristics for product recommendation. International Journal of Electronic Commerce 11 2 (2006) 59-80
3. Ansari A., Essegaier S., and Kohli R. Internet recommendation systems. Journal of Marketing Research 37 3 (2000) 363-375
4. Awad N.F., and Krishnan M.S. The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quarterly 30 1 (2006) 13-28
5. Baeza-Yates R., and Ribeiro-Neto B. Modern Information Retrieval (1999), ACM Press, Addison-Wesley
6. Balabanović M., and Shoham Y. Fab: content-based, collaborative recommendation. Communications of the ACM 40 3 (March 1997) 66-72
7. Beyah G., Xu P., Woo H.G., Mohan K., and Straub D. Development of an instrument to study the use of recommendation systems. Proceedings of the Ninth Americas Conference on Information Systems, Tampa, FL, USA (2003) 269-279
8. Billsus D., and Pazzani M.J. A personal news agent that talks, learns and explains. Proceedings of the Third Annual Conference on Autonomous Agents, Seattle, Washington, USA (1999) 268-275
9. Cheung K.W., Kwok J.T., Law M.H., and Tsui K.C. Mining customer product ratings for personalized marketing. Decision Support Systems 35 2 (2003) 231-243
10. Cheung K.W., Tsui K.C., and Liu J.M. Extended latent class models for collaborative recommendation. IEEE Transactions on Systems, Man, and Cybernetics (Part A) 34 1 (2004) 143-148
11. Chiaramella Y., and Defude B. A prototype of an intelligent system for information retrieval: IOTA. Information Processing & Management 23 4 (1987) 285-303
12. Collins A.M., and Loftus E.F. A spreading-activation theory of semantic processing. Psychological Review 82 6 (1975) 407-428
13. Crestani F. Application of spreading activation techniques in information retrieval. Artificial Intelligence Review 11 6 (December 1997) 453-482
14. Crestani F., and Lee P.L. Searching the web by constrained spreading activation. Information Processing & Management 36 4 (July 2000) 585-605
15. Fan W., Gordon M.D., and Pathak P. Effective profiling of consumer information retrieval needs: a unified framework and empirical comparison. Decision Support Systems 40 (2005) 213-233
16. Fuhr N., and Hüther H. Optimum probability estimation from empirical distributions. Information Processing & Management 25 5 (1989) 493-507
17. Garfinkel R., Gopal R., Tripathi A., and Yin F. Design of a shopbot and recommender system for bundle purchases. Decision Support Systems 42 (2006) 1974-1986
18. Hanani U., Shapira B., and Shoval P. Information filtering: overview of issues, research and systems. User Modeling and User-Adapted Interaction 11 (August 2001) 203-259
19. Herlocker J.L., Konstan J.A., Borchers A., and Riedl J. An algorithmic framework for performing collaborative filtering. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, California, USA (1999) 230-237
20. Hung L.P. A personalized recommendation system based on product taxonomy for one-to-one marketing online. Expert Systems with Applications 29 2 (2005) 383-392
21. Im I., and Hars A. Finding information just for you: knowledge reuse using collaborative filtering systems. Proceedings of the Twenty-Second International Conference on Information Systems, New Orleans, Louisiana, USA (2001) 349-360
22. Kim J.W., Lee K.K., Shaw M.J., Chang H.L., Nelson M., and Easley R.F. A preference scoring technique for personalized advertisements on Internet storefronts. Mathematical and Computer Modeling 44 (2006) 3-15
23. Konstan J.A., Miller B.N., Maltz D., Herlocker J.L., Gordon L.R., and Riedl J. GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM 40 3 (March 1997) 77-87
24. Lai H.J., Liang T.P., and Ku Y.C. Customized Internet News Services Based on Customer Profiles. Proceedings of the Fifth International Conference on Electronic Commerce, Pittsburgh, PA, USA (2003) 225-229
25. Lang K., et al. NewsWeeder: learning to filter netnews. Proceedings of the 12th International Conference on Machine Learning, Tahoe City, California (1995) 331-339
26. Lee W.P. Applying domain knowledge and social information to product analysis and recommendations: an agent-based decision support systems. Expert Systems 21 3 (2004) 138-148
27. Liang T.P., and Lai H.J. Discovering user interests from web browsing behavior: an application to Internet news services. Proceedings of the 35th Annual Hawaii International Conference on System Sciences, Big Island, Hawaii, USA (2002) 203-212
28. Liang T.P., Lai H.J., and Ku Y.C. Personalized content recommendation and user satisfaction: theoretical synthesis and empirical findings. Journal of Management Information Systems 23 3 (2006) 45-70
29. In: Lin S.H., Shih C.S., Chen M.C., Ho J.M., Ko M.T., and Huang Y.M. (Eds). Extracting classification knowledge of Internet documents with mining term associations: a semantic approach, ACM SIGIR'98 (1998) 241-249
30. Linden G., Smith B., and York J. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7 1 (2003) 76-80
31. Liu D.R., and Shih Y.Y. Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. Journal of Systems and Software 77 2 (2005) 181-191
32. Maybury M., Greiff W., Boykin S., Ponte J., McHenry C., and Ferro L. Personalcasting: tailored broadcast news, user modeling and user-adapted interaction. User Modeling and User-Adapted Interaction 14 (2004) 119-144
33. Miller G.A. WordNet: a lexical database for English. Communications of the ACM 38 11 (1995) 39-41
34. Mock K.J., and Vemuri V.R. Information filtering via hill climbing, WordNet, and index patterns. Information Processing & Management 33 5 (1997) 633-644
35. Montaner M., Lopez B., and Mosa L.D.A. A taxonomy of recommender agents and the Internet. Artificial Intelligence Review 19 (2003) 285-330
36. Mooney R.J., and Roy L. Content-based book recommending using learning for text categorization. Proceedings of the fifth ACM conference on Digital libraries, San Antonio, Texas, United States (2000) 195-204
37. Mukherjee R., Sajja N., and Sen S. A movie recommendation system - an application of voting theory in user modeling. User Modeling and User-Adapted Interaction 13 (2003) 5-33
38. Murthi B.P.S., and Sarkar S. The role of the management sciences in research on personalization. Management Science 49 10 (2003) 1344-1362
39. Nikolaeva R., and Sriram S. The moderating role of consumer and product characteristics on the value of customerized on-line recommendation. International Journal of Electronic Commerce 11 2 (2006) 101-123
40. Oard D.W., and Kim J. Implicit feedback for recommender systems. Proceedings of AAAI Workshops on Recommender Systems, Madison, WI (1998) 81-83
41. Paice C.D. A thesaural model of information retrieval. Information Processing & Management 27 5 (1991) 433-447
42. Paik H., and Benatallah B. Building adaptive e-catalog communities based on user interaction patterns. IEEE Intelligent Systems 17 6 (2002) 44-52
43. Pazzani M.J. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13 5-6 (1999) 393-408
44. In: Rocchio J. (Ed). Relevance feedback in information retrieval, The SMART Retrieval System-Experiments in Automatic Document Processing (1971), Prentice Hall Inc 313-323
45. Sakagami H., and Kamba T. Learning personal preferences on online newspaper articles from user behaviors. Computer Networks and ISDN Systems 29 (1997) 1447-1455
46. Salton G., and McGill M.J. Introduction to Modern Information Retrieval (1983), McGraw-Hill, New York
47. Schafer J.B., Konstan J.A., and Riedl J. E-commerce recommendation applications. Data Mining and Knowledge Discovery 5 1 (2001) 115-153
48. Senecal S., and Nantel J. The influence of online product recommendations on consumers' online choices. Journal of Retailing 80 (2004) 159-169
49. Singh S.N., and Dalal N.P. Web home pages as advertisements. Communications of the ACM 42 8 (1999) 91-98
50. Tam K.Y., and .Ho S.Y. Web personalization as a persuasion strategy: an elaboration likelihood model perspective. Information Systems Research 16 3 (2005) 271-291
51. Tu H.C., and Hsiang J. An architecture and category knowledge for intelligent information retrieval agents. Decision Support Systems 28 3 (2000) 255-268
52. Weng S.S., and Liu M.J. Feature-based recommendation for one-to-one marketing. Expert Systems with Applications 26 4 (2004) 493-508
53. Wei C.P., Shaw M.J., and Easley R.F. A survey of recommendation systems in electronic commerce. In: Rust R.T., and Kannan P.K. (Eds). e-Service: New Directions in Theory and Practice (2002), M.E. Sharpe Publisher
54. Wei J., Bressan S., and Ooi B.C. Mining term association rules for automatic global query expansion: methodology and preliminary results. Proceedings of the First International Conference on Web Information Systems Engineering, Hong Kong, China (2000) 366-373
55. J. Xu, Solving the Word Mismatch Problem through Automatic Text Analysis, PhD Thesis, Department of Computer Science, University of Massachusetts at Amherst, MA, USA (1997).
56. Xu J., and Croft W.B. Query expansion using local and global document analysis. Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Zurich, Switzerland (1996) 4-11