A software system for buzz-based recommendations

ABSTRACT

In this paper, we present an outline of a software system for buzz-based recommendations. This system is based on a large source of queries in an eCommerce application. The buzz events are detected based on query bursts linked to external entities like news and inventory information. A semantic neighborhood of the chosen buzz query is selected and appropriate recommendations are made on products that relate to this neighborhood. The system follows the paradigm of limited quantity merchandizing, in the sense that on a per-day basis the system shows recommendations around a single buzz query with the intent of increasing user curiosity and promoting user activity and stickiness. The system demonstrates the deployment of an interesting application based on KDD principles applied to a high volume industrial context.

REFERENCES

• 1: http://www.nucifora.com/art_203.html
• 2: Carl W. What's All the Buzz about? Management Communication Quarterly, Vol.19. No.4, SAGE Publications.
• 3: http://en.wikipedia.org/wiki/Woot.com
• 4: Jon Kleinberg, Bursty and hierarchical structure in streams, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775061]
• 5: Nish Parikh , Neel Sundaresan, Scalable and near real-time burst detection from eCommerce queries, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008, Las Vegas, Nevada, USA [doi>10.1145/1401890.1402006]
• 6: Zhiyong Zhang , Olfa Nasraoui, Mining search engine query logs for query recommendation, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1136004]
• 7: Mehran Sahami , Timothy D. Heilman, A web-based kernel function for measuring the similarity of short text snippets, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135834]
• 8: http://pages.ebay.com/aboutebay.html
• 9: http://en.wikipedia.org/w/index.php?title=EBay&oldid=219265742
• 10: http://developer.ebay.com/common/api/
• 11: http://mark-shepherd.com/blog/springgraph-flex-component/
• 12: http://labs.adobe.com/technologies/flex/

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

General Terms:
Design, Experimentation, Human Factors

Keywords:
large-scale buzz detection, semantic relatedness, surprise a day