Incorporating rich features to boost information retrieval performance: A SVM-regression based re-ranking approach

Document ranking is an essential problem in the field of information retrieval (IR). Traditional weighting models such as BM25 and Language model can only take advantage of query terms. IR is a complex process that may be affected by a series of heterogeneous features. It is necessary to refine first-pass retrieval results by taking rich features into account. Traditional heuristic re-ranking approaches can only take advantage of a small number of homogeneous features that may affect information retrieval performance. In this paper, we propose and evaluate a regression-based document re-ranking approach for IR, in which we use SVM regression model to learn a re-ranking function automatically. Under this regression-based framework, we can take advantage of rich features to re-rank the firs-pass retrieved documents by traditional weighting models. We conduct a series of experiments on four standard IR collections in two different languages. The experimental results show that our proposed approach can significantly improve the retrieval performance over the first-pass retrieval. Moreover, by refining the first-pass retrieved document set, the traditional pseudo relevant feedback approaches can also be enhanced.