Constructing an Interaction Behavior Model for Web Image Search

Xie, Xiaohui; Mao, Jiaxin; Rijke, Maarten de; Zhang, Ruizhe; Zhang, Min; Ma, Shaoping

doi:10.1145/3209978.3209990

Cited by 23 publications

(27 citation statements)

References 46 publications

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“…The observation "Middle bias" in this paper accords with their ndings. Also, interaction behavior such as cursor hovering has been shown to be a valuable additional signal for relevance [22,35]. User behavior that is unique to image search has motivated various attempts at user behavior modeling that aim to improve the performance of image search engines [14,35,37].…”

Section: Image Searchmentioning

confidence: 99%

“…As shown in [35,39], di erent within-row directions have little impact on user behavior modeling in image search. De ne the rank position in a grid by following the top to bottom and left to right order.…”

Section: "Slower Decay"mentioning

confidence: 99%

“…where r is the relevance score of the i-th result. ERR and DCG have been used in previous evaluation tasks on image search [35,39].…”

Section: Evaluation Of User Satisfaction Correlationmentioning

confidence: 99%

“…The reason may be that ERR focuses more on the user gain rather than the examined position. As shown in [11,35], users' judgments Table 6: Outcomes of Experiment 2. Pearson's Correlation between evaluation metrics (DCG and ERR @top 10 rows) and user satisfaction in eld study dataset.…”

Section: Evaluation Of User Satisfaction Correlationmentioning

confidence: 99%

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Grid-based Evaluation Metrics for Web Image Search

Xie

Mao

Liu

et al. 2019

The World Wide Web Conference

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Compared to general web search engines, web image search engines display results in a di erent way. In web image search, results are typically placed in a grid-based manner rather than a sequential result list. In this scenario, users can view results not only in a vertical direction but also in a horizontal direction. Moreover, pagination is usually not (explicitly) supported on image search search engine result pages (SERPs), and users can view results by scrolling down without having to click a "next page" button. These di erences lead to di erent interaction mechanisms and user behavior patterns, which, in turn, create challenges to evaluation metrics that have originally been developed for general web search. While considerable e ort has been invested in developing evaluation metrics for general web search, there has been relatively little e ort to construct grid-based evaluation metrics. To inform the development of grid-based evaluation metrics for web image search, we conduct a comprehensive analysis of user behavior so as to uncover how users allocate their attention in a grid-based web image search result interface. We obtain three ndings: (1) "Middle bias": Con rming previous studies, we nd that image results in the horizontal middle positions may receive more attention from users than those in the leftmost or rightmost positions. (2) "Slower decay": Unlike web search, users' attention does not decrease monotonically or dramatically with the rank position in image search, especially within a row. (3) "Row skipping": Users may ignore particular rows and directly jump to results at some distance. Motivated by these observations, we propose corresponding user behavior assumptions to capture users' search interaction processes and evaluate their search performance. We show how to derive new metrics from these assumptions and demonstrate * Corresponding author This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

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Section: Image Searchmentioning

confidence: 99%

Section: "Slower Decay"mentioning

confidence: 99%

“…where r is the relevance score of the i-th result. ERR and DCG have been used in previous evaluation tasks on image search [35,39].…”

Section: Evaluation Of User Satisfaction Correlationmentioning

confidence: 99%

Section: Evaluation Of User Satisfaction Correlationmentioning

confidence: 99%

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Grid-based Evaluation Metrics for Web Image Search

Xie

Mao

Liu

et al. 2019

The World Wide Web Conference

Self Cite

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“…The advantage of PGM-based click models is that they intuitively describe user click behavior and can predict future clicks based on past observations [9]. Some click models take into account the order in which a user interacts with the results in order to better model and predict clicks [33,43,44,49,51]. However, such models either do not aim at predicting click sequences [33,43,44,49] or consider only very short sequences of clicks [51].…”

Section: Modeling User Interactionsmentioning

confidence: 99%

A Click Sequence Model for Web Search

Borisov

Wardenaar

Markov

et al. 2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Getting a better understanding of user behavior is important for advancing information retrieval systems. Existing work focuses on modeling and predicting single interaction events, such as clicks. In this paper, we for the first time focus on modeling and predicting sequences of interaction events. And in particular, sequences of clicks.We formulate the problem of click sequence prediction and propose a click sequence model (CSM) that aims to predict the order in which a user will interact with search engine results. CSM is based on a neural network that follows the encoder-decoder architecture. The encoder computes contextual embeddings of the results. The decoder predicts the sequence of positions of the clicked results. It uses an attention mechanism to extract necessary information about the results at each timestep. We optimize the parameters of CSM by maximizing the likelihood of observed click sequences.We test the effectiveness of CSM on three new tasks: (i) predicting click sequences, (ii) predicting the number of clicks, and (iii) predicting whether or not a user will interact with the results in the order these results are presented on a search engine result page (SERP). Also, we show that CSM achieves state-of-the-art results on a standard click prediction task, where the goal is to predict an unordered set of results a user will click on. CCS CONCEPTS• Information systems → Users and interactive retrieval; KEYWORDS Click model, User behavior, Web search ACM Reference Format: Alexey Borisov, Martijn Wardenaar, Ilya Markov, and Maarten de Rijke. 2018.

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Comparing point‐wise and pair‐wise relevance judgment with brain signals

Zhu,

Xie,

et al. 2024

Asso for Info Science & Tech

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How to collect relevance judgment has long been an important problem in Information Retrieval (IR). A popular method is to collect relevance judgment in a point‐wise manner, in which assessors examine and give an absolute relevance score for each item independently of the others. As an alternative, pair‐wise relevance judgment, also named preference judgment, allows an assessor to compare two items side‐by‐side and express their preference for one over the other. Previous work has explored the differences between these two paradigms of relevance judgments from many different aspects. Most of these works are conducted through explicit/implicit feedback. However, few works investigate the underlying neurological mechanisms of the two paradigms. In this paper, we conduct a lab study to investigate and compare point‐wise and pair‐wise relevance judgment in image search scenarios. We study the neurological mechanisms of the two paradigms through an event‐related potential (ERP) analysis of the users' brain signals while viewing images during a search process. We have obtained several observations, such as search engine users tend to pay more attention to preferred items in the point‐wise paradigm but unpreferred items in the pair‐wise paradigm. Furthermore, we test the adoption of brain signals as implicit feedback for predicting pair‐wise relevance judgment, highlighting the feasibility of leveraging brain signals to understand users' relevance judgments.

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Constructing an Interaction Behavior Model for Web Image Search

Cited by 23 publications

References 46 publications

Grid-based Evaluation Metrics for Web Image Search

Grid-based Evaluation Metrics for Web Image Search

A Click Sequence Model for Web Search

Comparing point‐wise and pair‐wise relevance judgment with brain signals

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