The relationship between IR effectiveness measures and user satisfaction

Al-Maskari, Azzah; Sanderson, Mark; Clough, Paul

doi:10.1145/1277741.1277902

Cited by 122 publications

(97 citation statements)

References 4 publications

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“…For instance, Al-Maskari et al analyzed how well classic IR evaluation metrics correlated with user satisfaction in recommender systems [297]. Gunawardana and Shani published a survey about accuracy metrics [224].…”

Section: Reproducibility and The Butterfly Effectmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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Section: Reproducibility and The Butterfly Effectmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…e results provide insights for both evaluation metrics study and user satisfaction understanding. (2) We investigate the di erences and applicabilities of di erent evaluation metrics in both homogeneous and heterogeneous search environment. We demonstrate that o ine metrics work be er in homogeneous search while online metrics outperform in heterogeneous search environment.…”

mentioning

confidence: 99%

Meta-evaluation of Online and Offline Web Search Evaluation Metrics

Chen

Zhou

Liu

et al. 2017

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

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As in most information retrieval (IR) studies, evaluation plays an essential part in Web search research. Both o ine and online evaluation metrics are adopted in measuring the performance of search engines. O ine metrics are usually based on relevance judgments of query-document pairs from assessors while online metrics exploit the user behavior data, such as clicks, collected from search engines to compare search algorithms. Although both types of IR evaluation metrics have achieved success, to what extent can they predict user satisfaction still remains under-investigated. To shed light on this research question, we meta-evaluate a series of existing online and o ine metrics to study how well they infer actual search user satisfaction in di erent search scenarios. We nd that both types of evaluation metrics signi cantly correlate with user satisfaction while they re ect satisfaction from di erent perspectives for di erent search tasks. O ine metrics be er align with user satisfaction in homogeneous search (i.e. ten blue links) whereas online metrics outperform when vertical results are federated. Finally, we also propose to incorporate mouse hover information into existing online evaluation metrics, and empirically show that they be er align with search user satisfaction than click-based online metrics.

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“…So we can compare this ranked list to Google ranked list which has been generated without applying E-IOWA. We have used Google search engine to retrieve top ten URLs because of its better efficiency among all other search engines [25][26][27]. For this comparison we use Kendall's tau distance between proposed system ranked list and Google ranked list to measure efficiency of this implemented system.…”

Section: Simulation Framework and Results Discussionmentioning

confidence: 99%