Combining Word Embedding Interactions and LETOR Feature Evidences for Supervised QPP

In information retrieval, query performance prediction aims to predict whether a search engine is likely to succeed in retrieving potentially relevant documents to a user’s query. This problem is usually cast into a regression problem where a machine should predict the effectiveness (in terms of an information retrieval measure) of the search engine on a given query. The solutions range from simple unsupervised approaches where a single source of information (e.g., the variance of the retrieval similarity scores in NQC), predicts the search engine effectiveness for a given query, to more involved ones that rely on supervised machine learning making use of several sources of information, e.g., the learning to rank (LETOR) features, word embedding similarities etc. In this paper, we investigate the combination of two different types of evidences into a single neural network model. While our first source of information corresponds to the semantic interaction between the terms in queries and their top-retrieved documents, our second source of information corresponds to that of LETOR features

Improved query performance prediction using standard deviation

Query performance prediction (QPP) is an important task in information retrieval (IR). In this paper, we (1) develop a new predictor based on the standard deviation of scores in a variable length ranked list, and (2) we show that this new predictor outperforms state-of-the-art approaches without the need for tuning

Recommender system performance evaluation and prediction: information retrieval perspective

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 201