  Ranking functions used in information retrieval are primarily used in the
search engines and they are often adopted for various language processing
applications. However, features used in the construction of ranking functions
should be analyzed before applying it on a data set. This paper gives
guidelines on construction of generalized ranking functions with
application-dependent features. The paper prescribes a specific case of a
generalized function for recommendation system using feature engineering
guidelines on the given data set. The behavior of both generalized and specific
functions are studied and implemented on the unstructured textual data. The
proximity feature based ranking function has outperformed by 52% from regular
BM25.
