FP-Rank: An Effective Ranking Approach Based on Frequent Pattern Analysis

MPhil Thesis Defence


Title: "FP-Rank: An Effective Ranking Approach Based on
Frequent Pattern Analysis"

By

Mr. Yuanfeng Song


Abstract

Ranking documents in terms of their relevance to a given query is fundamental 
to many real-life applications such as document retrieval and recommendation 
systems. Extensive studies in this area have focused on developing efficient 
ranking models. While ranking models are usually trained based on given 
training datasets, besides model training algorithms, the quality of the 
document features selected for model training also plays a very important 
aspect on the model performance. The main objective of this thesis is to 
present an approach to discover “significant” document features for learning to 
rank (LTR) problem.

We conduct a systematic exploration of frequent pattern-based ranking. First, 
we formally analyze the effectiveness of frequent patterns for ranking. 
Combined features, which constitute a large portion of frequent patterns, 
perform better than single features in terms of capturing rich underlying 
semantics of the documents and hence provide good feature candidates for 
ranking. Based on our analysis, we propose a new ranking approach called 
FP-Rank. Essentially, FP-Rank adopts frequent pattern mining algorithms to mine 
frequent patterns, and then a new pattern selection algorithm is adopted to 
select an optimal set of patterns with high overall significance and low 
redundancy. Our experiments on the real datasets confirm that, by incorporating 
effective frequent patterns to train a ranking model, such as RankSVM, the 
performance of the ranking model can be substantially improved.


Date:			Friday, 27 July 2012

Time:			10:00am - 12:00noon

Venue:			Room 3408
 			Lifts 17/18

Committee Members:	Dr. Wilfred Ng (Supervisor)
 			Dr. Lei Chen (Chairperson)
 			Dr. Raymond Wong


**** ALL are Welcome ****