A Survey on Personalized Recommendation Techniques

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A Wikipedia powered state-based approach to automatic search query enhancement

This paper describes the development and testing of a novel Automatic Search Query Enhancement (ASQE) algorithm, the Wikipedia N Sub-state Algorithm (WNSSA), which utilises Wikipedia as the sole data source for prior knowledge. This algorithm is built upon the concept of iterative states and sub-states, harnessing the power of Wikipedia\u27s data set and link information to identify and utilise reoccurring terms to aid term selection and weighting during enhancement. This algorithm is designed to prevent query drift by making callbacks to the user\u27s original search intent by persisting the original query between internal states with additional selected enhancement terms. The developed algorithm has shown to improve both short and long queries by providing a better understanding of the query and available data. The proposed algorithm was compared against five existing ASQE algorithms that utilise Wikipedia as the sole data source, showing an average Mean Average Precision (MAP) improvement of 0.273 over the tested existing ASQE algorithms

Word sense discrimination in information retrieval: a spectral clustering-based approach

International audienceWord sense ambiguity has been identified as a cause of poor precision in information retrieval (IR) systems. Word sense disambiguation and discrimination methods have been defined to help systems choose which documents should be retrieved in relation to an ambiguous query. However, the only approaches that show a genuine benefit for word sense discrimination or disambiguation in IR are generally supervised ones. In this paper we propose a new unsupervised method that uses word sense discrimination in IR. The method we develop is based on spectral clustering and reorders an initially retrieved document list by boosting documents that are semantically similar to the target query. For several TREC ad hoc collections we show that our method is useful in the case of queries which contain ambiguous terms. We are interested in improving the level of precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30) respectively. We show that precision can be improved by 8% above current state-of-the-art baselines. We also focus on poor performing queries

A qualitative analysis of the Wikipedia N-Substate Algorithm's Enhancement Terms

[EN] Automatic Search Query Enhancement (ASQE) is the process of modifying a user submitted search query and identifying terms that can be added or removed to enhance the relevance of documents retrieved from a search engine. ASQE differs from other enhancement approaches as no human interaction is required. ASQE algorithms typically rely on a source of a priori knowledge to aid the process of identifying relevant enhancement terms. This paper describes the results of a qualitative analysis of the enhancement terms generated by the Wikipedia NSubstate Algorithm (WNSSA) for ASQE. The WNSSA utilises Wikipedia as the sole source of a priori knowledge during the query enhancement process. As each Wikipedia article typically represents a single topic, during the enhancement process of the WNSSA, a mapping is performed between the user’s original search query and Wikipedia articles relevant to the query. If this mapping is performed correctly, a collection of potentially relevant terms and acronyms are accessible for ASQE. This paper reviews the results of a qualitative analysis process performed for the individual enhancement term generated for each of the 50 test topics from the TREC-9 Web Topic collection. The contributions of this paper include: (a) a qualitative analysis of generated WNSSA search query enhancement terms and (b) an analysis of the concepts represented in the TREC-9 Web Topics, detailing interpretation issues during query-to-Wikipedia article mapping performed by the WNSSA.Goslin, K.; Hofmann, M. (2019). A qualitative analysis of the Wikipedia N-Substate Algorithm's Enhancement Terms. Journal of Computer-Assisted Linguistic Research. 3(3):67-77. https://doi.org/10.4995/jclr.2019.11159SWORD677733Asfari, Ounas, Doan, Bich-liên, Bourda, Yolaine and Sansonnet, Jean-Paul. 2009. "Personalized Access to Information by Query Reformulation Based on the State of the Current Task and User Profile." Paper presented at Third International Conference on Advances in Semantic Processing, 113-116. IEEE. https://doi.org/10.1109/SEMAPRO.2009.17Bazzanella, Barbara, Stoermer, Heiko, and Bouquet, Paolo. 2010. "Searching for individual entities: A query analysis.", Paper presented at International Conference on Information Reuse & Integration, 115-120. IEEE. https://doi.org/10.1109/IRI.2010.5558955Gao, Jianfeng, Xu , Gu and Xu, Jinxi. 2013. Query expansion using path-constrained random walks. Paper presented at 36th international ACM SIGIR conference on Research and development in information retrieval (SIGIR '13), 563-572. ACM. https://doi.org/10.1145/2484028.2484058Goslin, Kyle, Hofmann, Markus. 2017. "A Comparison of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia as a Source of A Priori Knowledge." Paper presented at 9th Annual Meeting of the Forum for Information Retrieval Evaluation (FIRE'17), 6-13. ACM. https://doi.org/10.1145/3158354.3158356Goslin, Kyle, Hofmann, Markus. 2018. "A Wikipedia powered state-based approach to automatic search query enhancement." Journal of Information Processing & Management 54(4), 726-739. Elsevier. https://doi.org/10.1016/j.ipm.2017.10.001Jansen, Bernard, Spink, Amanda, Bateman, Judy and Saracevic, Tefko. 1998. "Real life information retrieval: a study of user queries on the Web." Paper presented at ACM SIGIR Forum 32, 5-17. ACM. https://doi.org/10.1145/281250.281253Mastora, Anna, Monopoli, Maria and Kapidakis, Sarantos. 2008. "Term selection patterns for formulating queries: a User study focused on term semantics." Paper presented at Third International Conference on Digital Information Management, 125-130. IEEE. https://doi.org/10.1109/ICDIM.2008.4746747Ogilvie, Paul, Voorhees, Ellen and Callan, Jamie. 2009. "On the number of terms used in automatic query expansion." Journal of Information Retrieval 12(6): 666. Springer. https://doi.org/10.1007/s10791-009-9104-1Voorhees, Ellen M. 1994. "Query expansion using lexical-semantic relations." Paper presented at the 17th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '94), 61-69. Springer-Verlag. https://doi.org/10.1007/978-1-4471-2099-5_

INEX Tweet Contextualization Task: Evaluation, Results and Lesson Learned

Microblogging platforms such as Twitter are increasingly used for on-line client and market analysis. This motivated the proposal of a new track at CLEF INEX lab of Tweet Contextualization. The objective of this task was to help a user to understand a tweet by providing him with a short explanatory summary (500 words). This summary should be built automatically using resources like Wikipedia and generated by extracting relevant passages and aggregating them into a coherent summary. Running for four years, results show that the best systems combine NLP techniques with more traditional methods. More precisely the best performing systems combine passage retrieval, sentence segmentation and scoring, named entity recognition, text part-of-speech (POS) analysis, anaphora detection, diversity content measure as well as sentence reordering. This paper provides a full summary report on the four-year long task. While yearly overviews focused on system results, in this paper we provide a detailed report on the approaches proposed by the participants and which can be considered as the state of the art for this task. As an important result from the 4 years competition, we also describe the open access resources that have been built and collected. The evaluation measures for automatic summarization designed in DUC or MUC were not appropriate to evaluate tweet contextualization, we explain why and depict in detailed the LogSim measure used to evaluate informativeness of produced contexts or summaries. Finally, we also mention the lessons we learned and that it is worth considering when designing a task

Evaluation of information retrieval systems using structural equation modeling

The interpretation of the experimental data collected by testing systems across input datasets and model parameters is of strategic importance for system design and implementation. In particular, finding relationships between variables and detecting the latent variables affecting retrieval performance can provide designers, engineers and experimenters with useful if not necessary information about how a system is performing. This paper discusses the use of Structural Equation Modeling (SEM) in providing an in-depth explanation of evaluation results and an explanation of failures and successes of a system; in particular, we focus on the case of evaluation of Information Retrieval systems

A WEB PERSONALIZATION ARTIFACT FOR UTILITY-SENSITIVE REVIEW ANALYSIS

Online customer reviews are web content voluntarily posted by the users of a product (e.g. camera) or service (e.g. hotel) to express their opinions about the product or service. Online reviews are important resources for businesses and consumers. This dissertation focuses on the important consumer concern of review utility, i.e., the helpfulness or usefulness of online reviews to inform consumer purchase decisions. Review utility concerns consumers since not all online reviews are useful or helpful. And, the quantity of the online reviews of a product/service tends to be very large. Manual assessment of review utility is not only time consuming but also information overloading. To address this issue, review helpfulness research (RHR) has become a very active research stream dedicated to study utility-sensitive review analysis (USRA) techniques for automating review utility assessment. Unfortunately, prior RHR solution is inadequate. RHR researchers call for more suitable USRA approaches. Our current research responds to this urgent call by addressing the research problem: What is an adequate USRA approach? We address this problem by offering novel Design Science (DS) artifacts for personalized USRA (PUSRA). Our proposed solution extends not only RHR research but also web personalization research (WPR), which studies web-based solutions for personalized web provision. We have evaluated the proposed solution by applying three evaluation methods: analytical, descriptive, and experimental. The evaluations corroborate the practical efficacy of our proposed solution. This research contributes what we believe (1) the first DS artifacts to the knowledge body of RHR and WPR, and (2) the first PUSRA contribution to USRA practice. Moreover, we consider our evaluations of the proposed solution the first comprehensive assessment of USRA solutions. In addition, this research contributes to the advancement of decision support research and practice. The proposed solution is a web-based decision support artifact with the capability to substantially improve accurate personalized webpage provision. Also, website designers can apply our research solution to transform their works fundamentally. Such transformation can add substantial value to businesses