scholarly journals Information retrieval models for recommender systems

2019 ◽  
Vol 53 (1) ◽  
pp. 44-45
Author(s):  
Daniel Valcarce
Keyword(s):  
Information Retrieval ◽  
Recommender Systems ◽  
Relevance Feedback ◽  
Information Needs ◽  
Ad Hoc ◽  
Information Overload ◽  
Group Formation ◽  
Retrieval Models ◽  
Information Retrieval Evaluation ◽  
Pseudo Relevance Feedback

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.

scholarly journals NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

2018 ◽  
Author(s):  
Canjia Li ◽  
Yingfei Sun ◽  
Ben He ◽  
Le Wang ◽  
Kai Hui ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Ad Hoc ◽  
Pseudo Relevance Feedback

Context Window Based Co-occurrence Approach for Improving Feedback Based Query Expansion in Information Retrieval

2015 ◽  
Vol 5 (4) ◽  
pp. 31-45 ◽  
Author(s):  
Jagendra Singh ◽  
Aditi Sharan
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Contextual Information ◽  
Optimal Combination ◽  
Query Term ◽  
Benchmark Data ◽  
First Pass ◽  
Baseline Approach ◽  
Pseudo Relevance Feedback

Pseudo-relevance feedback (PRF) is a type of relevance feedback approach of query expansion that considers the top ranked retrieved documents as relevance feedback. In this paper the authors focus is to capture the limitation of co-occurrence and PRF based query expansion approach and the authors proposed a hybrid method to improve the performance of PRF based query expansion by combining query term co-occurrence and query terms contextual information based on corpus of top retrieved feedback documents in first pass. Firstly, the paper suggests top retrieved feedback documents based query term co-occurrence approach to select an optimal combination of query terms from a pool of terms obtained using PRF based query expansion. Second, contextual window based approach is used to select the query context related terms from top feedback documents. Third, comparisons were made among baseline, co-occurrence and contextual window based approaches using different performance evaluating metrics. The experiments were performed on benchmark data and the results show significant improvement over baseline approach.

Exploring multiple diversification strategies for academic citation contexts recommendation

2020 ◽  
Vol 38 (4) ◽  
pp. 821-842
Author(s):  
Haihua Chen ◽  
Yunhan Yang ◽  
Wei Lu ◽  
Jiangping Chen
Keyword(s):  
Information Retrieval ◽  
Information Needs ◽  
Semantic Distance ◽  
Evaluation Framework ◽  
Data Set ◽  
Content Type ◽  
User Interests ◽  
Information Retrieval Evaluation ◽  
Diversification Strategies ◽  
Citation Contexts

Purpose Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing. Design/methodology/approach This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeerX data set and then evaluates the generated citation context lists by applying a user case study on 30 articles. Findings Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeerX using a list sorted by citation counts. Practical implications This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization. Originality/value The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.

scholarly journals Neural generative models and representation learning for information retrieval

2019 ◽  
Vol 53 (2) ◽  
pp. 97-97
Author(s):  
Qingyao Ai
Keyword(s):  
Information Retrieval ◽  
Theoretical Analysis ◽  
Language Processing ◽  
Ad Hoc ◽  
Representation Learning ◽  
Generative Models ◽  
Neural Models ◽  
Retrieval Models ◽  
Types Of Information ◽  
Text Images

Information Retrieval (IR) concerns about the structure, analysis, organization, storage, and retrieval of information. Among different retrieval models proposed in the past decades, generative retrieval models, especially those under the statistical probabilistic framework, are one of the most popular techniques that have been widely applied to Information Retrieval problems. While they are famous for their well-grounded theory and good empirical performance in text retrieval, their applications in IR are often limited by their complexity and low extendability in the modeling of high-dimensional information. Recently, advances in deep learning techniques provide new opportunities for representation learning and generative models for information retrieval. In contrast to statistical models, neural models have much more flexibility because they model information and data correlation in latent spaces without explicitly relying on any prior knowledge. Previous studies on pattern recognition and natural language processing have shown that semantically meaningful representations of text, images, and many types of information can be acquired with neural models through supervised or unsupervised training. Nonetheless, the effectiveness of neural models for information retrieval is mostly unexplored. In this thesis, we study how to develop new generative models and representation learning frameworks with neural models for information retrieval. Specifically, our contributions include three main components: (1) Theoretical Analysis : We present the first theoretical analysis and adaptation of existing neural embedding models for ad-hoc retrieval tasks; (2) Design Practice : Based on our experience and knowledge, we show how to design an embedding-based neural generative model for practical information retrieval tasks such as personalized product search; And (3) Generic Framework : We further generalize our proposed neural generative framework for complicated heterogeneous information retrieval scenarios that concern text, images, knowledge entities, and their relationships. Empirical results show that the proposed neural generative framework can effectively learn information representations and construct retrieval models that outperform the state-of-the-art systems in a variety of IR tasks.

A Pseudo-relevance feedback framework combining relevance matching and semantic matching for information retrieval

2020 ◽  
Vol 57 (6) ◽  
pp. 102342 ◽  
Author(s):  
Junmei Wang ◽  
Min Pan ◽  
Tingting He ◽  
Xiang Huang ◽  
Xueyan Wang ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Semantic Matching ◽  
Pseudo Relevance Feedback

CIRCLE 2020

2020 ◽  
Vol 54 (2) ◽  
pp. 1-9
Author(s):  
Iván Cantador ◽  
Max Chevalier ◽  
Massimo Melucci ◽  
Josiane Mothe
Keyword(s):  
Social Media ◽  
Information Retrieval ◽  
Recommender Systems ◽  
Research Work ◽  
User Profiling ◽  
Joint Conference ◽  
Information Retrieval Evaluation ◽  
Wide Range

The Joint Conference of the Information Retrieval Communities in Europe (CIRCLE 2020) is the first joint conference of the French, Italian, Spanish, and Swiss information retrieval communities. Although these communities had conceived the CIRCLE conference as a meeting and networking venue, because of the COVID-19 pandemic, they had to make the conference as fully virtual event. Nonetheless, the three days of conference gathered interesting studies and research work on a wide range of topics on information retrieval, such as topic and document modelling, query and ranking refinement, information retrieval in e-government, social media, recommender systems, information retrieval evaluation, indexing and annotation, user profiling and interaction, frameworks and systems, and semantic extraction.

Predicting Search Intent Based on In-Search Context for Exploratory Search

2019 ◽  
Vol 11 (3) ◽  
pp. 53-75 ◽  
Author(s):  
Vikram Singh
Keyword(s):  
Information System ◽  
Relevance Feedback ◽  
Information Needs ◽  
Superior Performance ◽  
Exploratory Search ◽  
Query Term ◽  
Estimation Strategy ◽  
Modern Information ◽  
F Measure ◽  
Pseudo Relevance Feedback

Modern information systems are expected to assist users with diverse goals, via exploiting the topical dimension (‘what' the user is searching for) of information needs. However, the intent dimension (‘why' the user is searching) has preferred relatively lesser for the same intention. Traditionally, the intent is an ‘immediate reason, purpose, or goal' that motivates the user search, and captured in search contexts (Pre-search, In-search, Pro-Search), an ideal information system would be able to use. This article proposes a novel intent estimation strategy; based on the intuition that captured intent, and proactively extracts likely results. The captured ‘Pre-search' context adapts query term proximities within matched results beside document-term statistics and pseudo-relevance feedback with user-relevance feedback for In-search. The assessment asserts the superior performance of the proposed strategy over the equivalent on tradeoffs, e.g., novelty, diversity (coverage, topicality), retrieval (precision, recall, F-measure) and exploitation vs exploration.

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