FP-rank : an effective ranking approach based on frequent pattern analysis

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