08-10-2010, 10:52 AM
Ranking and Suggesting Popular Items
Abstract
Ranking the popularity of items and suggesting popular items based on user feedback. User feedback is obtained by iteratively presenting a set of suggested items, and users selecting items based on their own preferences either from this suggestion set or from the set of all possible items. The goal is to quickly learn the true popularity ranking of items (unbiased by the made suggestions), and suggest true popular items. The difficulty is that making suggestions to users can reinforce popularity of some items and distort the resulting item ranking. The described problem of ranking and suggesting items arises in diverse applications including search query suggestions and tag suggestions for social tagging systems. We propose and study several algorithms for ranking and suggesting popular items, provide analytical results on their performance, and present numerical results obtained using the inferred popularity of tags from a month-long crawl of a popular social bookmarking service. Our results suggest that lightweight, randomized update rules that require no special configuration parameters provide good performance.
Existing System
The learning of item popularity is complicated by the suggesting of items to users. Indeed, we expect that users would tend to select suggested items more frequently. This could be for various reasons.
Proposed System
In this paper, our goal is to propose algorithms and analyze their performance for suggesting popular items to users in a way that enables learning of the user’s true preference over items. The true preference refers to the preference over items that would be observed from the users’ selections over items without exposure to any suggestions. A simple scheme for ranking and suggesting popular items (that appears in common use in practice)
System Requirements:
Hardware Requirements
• SYSTEM : Pentium IV 2.4 GHz
• HARD DISK : 40 GB
• RAM : 256 MB
• KEYBOARD : 110 keys enhanced.
Software Requirements
• Operating system : Windows Family
• Front End : Java,J2EE(JSP, Servlets)
• Back End : MSSQL (or) MySQL
