Pseudo-relevance Feedback Models Research Articles

Pseudo-Relevance Feedback Method Based on the Topic Relevance Model

In the field of information retrieval, most pseudo-relevance feedback models select candidate terms from the top k documents returned by the first-pass retrieval, but they cannot identify the reliability of these documents. This paper proposed a new approach to obtain feedback information more comprehensively by constructing four corresponding models. Firstly, the algorithm incorporated topic-based relevance information into the relevance model RM3 and constructed a topic-based relevance model, denoted as TopRM3, with two corresponding variants. TopRM3 estimated the reliability of a feedback document in language modeling when executing pseudo-relevance feedback from both term and topic-based perspectives. Secondly, the algorithm introduced topic-based relevance information into Rocchio’s model and constructed the corresponding model, denoted as TopRoc, with two corresponding variants. Experimental results on the five TREC collections show that the proposed TopRM3 and TopRoc are effective and generally superior to the state-of-the-art pseudo-relevance feedback models with optimal parameter settings in terms of mean average precision.

Information retrieval models for recommender systems

Information retrieval addresses the information needs of users by delivering relevant pieces of information but requires users to convey their information needs explicitly. In contrast, recommender systems offer personalized suggestions of items automatically. Ultimately, both fields help users cope with information overload by providing them with relevant items of information. This thesis aims to explore the connections between information retrieval and recommender systems. Our objective is to devise recommendation models inspired in information retrieval techniques. We begin by borrowing ideas from the information retrieval evaluation literature to analyze evaluation metrics in recommender systems [2]. Second, we study the applicability of pseudo-relevance feedback models to different recommendation tasks [1]. We investigate the conventional top-N recommendation task [5, 4, 6, 7], but we also explore the recently formulated user-item group formation problem [3] and propose a novel task based on the liquidation of long tail items [8]. Third, we exploit ad hoc retrieval models to compute neighborhoods in a collaborative filtering scenario [9, 10, 12]. Fourth, we explore the opposite direction by adapting an effective recommendation framework to pseudo-relevance feedback [13, 11]. Finally, we discuss the results and present our conclusions. In summary, this doctoral thesis adapts a series of information retrieval models to recommender systems. Our investigation shows that many retrieval models can be accommodated to deal with different recommendation tasks. Moreover, we find that taking the opposite path is also possible. Exhaustive experimentation confirms that the proposed models are competitive. Finally, we also perform a theoretical analysis of some models to explain their effectiveness. Advisors : Álvaro Barreiro and Javier Parapar. Committee members : Gabriella Pasi, Pablo Castells and Fidel Cacheda. The dissertation is available at: https://www.dc.fi.udc.es/~dvalcarce/thesis.pdf.

Word-embedding-based pseudo-relevance feedback for Arabic information retrieval

Pseudo-relevance feedback (PRF) is a very effective query expansion approach, which reformulates queries by selecting expansion terms from top k pseudo-relevant documents. Although standard PRF models have been proven effective to deal with vocabulary mismatch between users’ queries and relevant documents, expansion terms are selected without considering their similarity to the original query terms. In this article, we propose a method to incorporate word embedding (WE) similarity into PRF models for Arabic information retrieval (IR). The main idea is to select expansion terms using their distribution in the set of top pseudo-relevant documents along with their similarity to the original query terms. Experiments are conducted on the standard Arabic TREC 2001/2002 collection using three neural WE models. The obtained results show that our PRF extensions significantly outperform their baseline PRF models. Moreover, they enhanced the baseline IR model by 22% and 68% for the mean average precision (MAP) and the robustness index (RI), respectively.