Modeling Multi-query Retrieval Tasks Using Density Matrix Transformation

Abstract

The quantum probabilistic framework has recently been applied to Information Retrieval (IR). A representative is the Quantum Language Model (QLM), which is developed for the ad-hoc retrieval with single queries and has achieved significant improvements over traditional language models. In QLM, a density matrix, defined on the quantum probabilistic space, is estimated as a representation of user's search intention with respect to a specific query. However, QLM is unable to capture the dynamics of user's information need in query history. This limitation restricts its further application on the dynamic search tasks, e.g., session search. In this paper, we propose a Session-based Quantum Language Model (SQLM) that deals with multi-query session search task. In SQLM, a transformation model of density matrices is proposed to model the evolution of user's information need in response to the user's interaction with search engine, by incorporating features extracted from both positive feedback (clicked documents) and negative feedback (skipped documents). Extensive experiments conducted on TREC 2013 and 2014 session track data demonstrate the effectiveness of SQLM in comparison with the classic QLM.

Exploration of Quantum Interference in Document Relevance Judgement Discrepancy

“…Recently, researchers applied QT in many areas, such as lexical semantic spaces [33], document re-rank by the quantum probability ranking principle (QPRP) [17], the quantum language model (QLM) [22], a QLM-based session search model [23], etc.…”

“…Sordoni et al [22] developed a quantum language model (QLM), which achieves an improvement over the traditional language model. More recently, Li et al [23] extended QLM and applied it to the session search. Xie et al [24] formulated the quantum entanglement in QLM and achieve good retrieval performance.…”

A quasi-current representation for information needs inspired by Two-State Vector Formalism

“…Then a session-based QLM [7] was also put forward to divide those interaction information into positive and negative feedback to model the evolution of users' information needs (IN). Later, with the inspiration of "quantum interference", the interactive information in a session was used to construct a new superposed 25 state of a document in the IN space [8].…”

“…A Quantum Language Model (QLM) [5] was proposed to model term dependencies in IR and gained good 120 performance for ad-hoc retrieval. Subsequently, a series of variants [6,7,23] are proposed based on the QLM in order to make an expansion in wider IR scenarios. For example, an adaptive contextual QLM model was developed [6] to model users' dynamic IN in the context of users interaction.…”

“…SQLM, a session-based QLM [7], which uses the transformation between density matrices to model the evolution of users' information needs. …”