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Understanding cetacean echolocation behavior is important for effective population monitoring and conservation. Using passive acoustic monitoring (PAM), researchers can listen for the biosonar clicks produced by echolocating animals to estimate both diurnal and seasonal variations in their presence and activity. Furthermore, if species-specific click rates are known, cue counting techniques can be used to provide an estimate of population density. This study investigated the click rates of wild bottlenose dolphins tagged with sound and movement recording DTAG3s during health assessments over the West Florida Shelf in the Gulf of Mexico to quantify individual variability and explore factors influencing click production. We observed modest but significant differences in click rates across individuals, and higher click rates during dives compared to inter-dive surface intervals. Within dives, dive depth was the most important in shaping click rates, reflecting that dolphins adjust their echolocation behavior to tailor their acoustic field of view based on both predator-prey distance and their proximity to other large reflectors such as the ocean bottom. Click rates also showed subtle diurnal peaks at dawn and dusk, aligning with increased foraging efforts. The findings lay the groundwork for bottlenose dolphin density estimation using the cue counting technique and underscore the importance of incorporating region-specific information on foraging ecology and diving behavior into models of click rates. Our study provides the first estimate of bottlenose dolphin click rates but calls for further research to refine these click rate estimates to facilitate acoustic monitoring of delphinids.

The phenomenon of sponsored search advertising—where advertisers pay a fee to Internet search engines to be displayed alongside organic (nonsponsored) Web search results—is gaining ground as the largest source of revenues for search engines. Using a unique six-month panel data set of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different sponsored search metrics such as click-through rates, conversion rates, cost per click, and ranking of advertisements. Our paper proposes a novel framework to better understand the factors that drive differences in these metrics. We use a hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo methods. Using a simultaneous equations model, we quantify the relationship between various keyword characteristics, position of the advertisement, and the landing page quality score on consumer search and purchase behavior as well as on advertiser's cost per click and the search engine's ranking decision. Specifically, we find that the monetary value of a click is not uniform across all positions because conversion rates are highest at the top and decrease with rank as one goes down the search engine results page. Though search engines take into account the current period's bid as well as prior click-through rates before deciding the final rank of an advertisement in the current period, the current bid has a larger effect than prior click-through rates. We also find that an increase in landing page quality scores is associated with an increase in conversion rates and a decrease in advertiser's cost per click. Furthermore, our analysis shows that keywords that have more prominent positions on the search engine results page, and thus experience higher click-through or conversion rates, are not necessarily the most profitable ones—profits are often higher at the middle positions than at the top or the bottom ones. Besides providing managerial insights into search engine advertising, these results shed light on some key assumptions made in the theoretical modeling literature in sponsored search.

In order to improve the ability of quantitative evaluation of e-commerce advertising click rate, a model of e-commerce advertising click rate evaluation based on fuzzy genetic algorithm is proposed. The big data information sampling model of e-commerce advertisement click rate evaluation, based on the mining result of e-commerce advertisement click rate evaluation information, carries on the adaptive mining and fusion clustering processing to the e-commerce advertisement click rate evaluation data, extracts the similarity information of the e-commerce advertisement click rate distribution set, carries on the e-commerce advertisement click rate adaptive evaluation according to the similarity contrast method, carries on the feature coding in the e-commerce advertisement click rate evaluation process through the fuzzy genetic optimization method, establishes the e-commerce advertisement click rate reliability feature distribution function, combines the statistical feature analysis and the fuzzy feature cluster analysis method to evaluate and predict the e-commerce advertisement click rate. The simulation results show that this method has better adaptability, higher accuracy and better convergence.

The phenomenon of paid search advertising has now become the most predominant form of online advertising in the marketing world. However, we have little understanding of the impact of search engine advertising on consumers' responses in the presence of organic listings of the same firms. In this paper, we model and estimate the interrelationship between organic search listings and paid search advertisements. We use a unique panel data set based on aggregate consumer response to several hundred keywords over a three-month period collected from a major nationwide retailer store chain that advertises on Google. In particular, we focus on understanding whether the presence of organic listings on a search engine is associated with a positive, a negative, or no effect on the click-through rates of paid search advertisements, and vice versa for a given firm. We first build an integrated model to estimate the relationship between different metrics such as search volume, click-through rates, conversion rates, cost per click, and keyword ranks. A hierarchical Bayesian modeling framework is used and the model is estimated using Markov chain Monte Carlo methods. Our empirical findings suggest that click-throughs on organic listings have a positive interdependence with click-throughs on paid listings, and vice versa. We also find that this positive interdependence is asymmetric such that the impact of organic clicks on increases in utility from paid clicks is 3.5 times stronger than the impact of paid clicks on increases in utility from organic clicks. Using counterfactual experiments, we show that on an average this positive interdependence leads to an increase in expected profits for the firm ranging from 4.2% to 6.15% when compared to profits in the absence of this interdependence. To further validate our empirical results, we also conduct and present the results from a controlled field experiment. This experiment shows that total click-through rates, conversions rates, and revenues in the presence of both paid and organic search listings are significantly higher than those in the absence of paid search advertisements. The results predicted by the econometric model are also corroborated in this field experiment, which suggests a causal interpretation to the positive interdependence between paid and organic search listings. Given the increased spending on search engine-based advertising, our analysis provides critical insights to managers in both traditional and Internet firms.

Yangtze finless porpoises use high-frequency clicks to navigate, forage, and communicate. The way in which click production may vary depending on social or environmental context has never been investigated. A group of five captive Yangtze finless porpoises was monitored for one year, and 107 h of audio recordings was collected under different conditions. Using a MATLAB-generated interface, we extracted click density (i.e., number of clicks per minute) from these recordings and analyzed its variation depending on the context. As expected, click density increased as the number of animals present increased. The click density did not exhibit diurnal variations but did have seasonal variations, with click density being highest in summer and fall. Yangtze finless porpoises produced more clicks when socially separated than when not (136% more), during training/feeding sessions than outside of such sessions (312% more), when enrichment was provided (265% more on average), and when noisy events occurred rather than when no unusual event occurred (22% more). The click density decreased when many visitors were present in the facility (up to 35% less). These results show that Yangtze finless porpoises modulate their click production depending on the context and suggest that their echolocation activity and their emotional state may be linked to these changes. Such context-dependent variations also indicate the potential usefulness of monitoring acoustical activity as part of a welfare assessment tool in this species. Additionally, the click density variation found in captivity could be useful for understanding click rate variations of wild populations that are hardly visible.

In anurans, male clasps can elicit release calls from either sex. Male release calls have been observed in many anuran genera and this vocal response is thus highly conserved. Female release calls, however, are not as prevalent, suggesting that evolutionary trajectories for anuran release calls differ by sex. We analyzed male and female release calls in all available species of Xenopus, a fully aquatic African genus. Phylogenetic relationships in this genus include three species groups, two of which are clades and one of which is characterized by a reticulated phylogeny due in part to hybridizations between species with different ploidy levels (Evans et al., 2004; Evans, 2008). In all species, males produce release calls when clasped by another male. Females in the reticulated group do not produce release calls, but females in the rest of the genus do. Release calls consist of click trains of variable durations and inter-call intervals. In both sexes, inter-click interval divides the genus into groups with different click rates and these groups are phylogenetically related. In general, inter-click interval is shorter in male than in female release calls. Across species and sexes, release calls are characterized by a single, low (∼1000 Hz) dominant frequency. In X. laevis Congo and X. borealis, clasp duration is longer for male-female than for male-male pairs and clasp duration is correlated with the number, but not the duration, of release calls in male-male pairs. We discuss evolutionary scenarios for release call traits as well as the sex difference in occurrence.

Photos posted by consumers on social media, like Instagram, often include brands. Despite the substantial increase in such photos, there have been few investigations into how prospective consumers respond to this visual UGC . We begin to address this gap by investigating the role of the color compositions of visual UGC in consumer response. Consumer response is operationalized as the click-rate for a photo by a consumer when it is curated on the online site of the brand that it includes. This is the proportion of visitors who click on it for an enlarged view. Composition is operationalized as the specific combination of levels of the photo’s color attributes: hue , chroma , and brightness . Our goal is to identify the color compositions of photos, ceteris paribus , which get more clicks when they are curated. Data for our investigation comes from clicks over a one-year period on photos posted on Instagram curated by fifteen brands in six product categories on their sites. We assume Beta distributed proportions and calibrate a Beta regression using MCMC methods for our investigation. We find that click-rates are higher for photos that include higher proportions of green and lower proportions of red and cyan. We also find that chroma of red and blue are higher in photos with higher click-rates. Findings from our research led the sponsoring firm to modify its proprietary curation algorithm for client brands. The firm informed us that, post-modification, there has been a substantial increase in click-rates of curated photos for brands in several categories.

PurposeThis study aims to analyze whether average video watch time or click-through rates (CTR) on YouTube videos are more closely associated with high numbers of views per subscriber using linear regressions.Design/methodology/approachIn 2018, YouTube began releasing CTR data to its video creators. Since 2012, YouTube has emphasized how it favors watch time over clicks in its recommendations to viewers. To the best of the author’s knowledge, this is the first academic study looking at that CTR data to test what matters more for views on YouTube. Is watch time or CTR more important to getting views on YouTube?FindingsThe author analyzed new video releases on YouTube. This paper finds almost no or limited evidence that higher percent audience retention or total average watch time per view, respectively, are associated with more views on YouTube. Instead, videos with higher CTR got significantly more views.Originality/valueThe author knows no other study that tests the relative importance of CTR or watch time per view in predicting views for new videos on YouTube.

Recently, click-through rate (CTR) prediction is a challenge problem in the aspect of online advertising. Some researchers have proposed deep learning-based models that follow a similar embedding and MLP paradigm. However, the corresponding approaches generally ignore the importance of capturing the latent user interest behind user behaviour data. In this paper, we present a novel attentive deep interest-based network model called ADIN. Specifically, we capture the interest sequence in the interest extractor layer, and the auxiliary losses are employed to produce the interest state with the deep supervision. First, we model the dependency between behaviours by using a bidirectional gated recurrent unit (Bi-GRU). Next, we extract the interest evolving process that is related to the target and propose an interest evolving layer. At the same time, attention mechanism is embedded into the sequential structure. Then, the model learns highly non-linear interactions of features based on stack autoencoders. An experiment has been done using four real-world datasets, the proposed model achieves superior performance than the existing state-of-the-art models.

Click-through rate (CTR) prediction plays a pivotal role in developing effective recommendation systems across industries. While existing models like DeepFM primarily focus on low-order and high-order feature interactions, they often fail to sufficiently account for the heterogeneous importance distribution among individual features. To address this limitation, we propose FLSEST, a novel architecture integrating a squeeze-excitation and soft threshold (SEST) mechanism that dynamically amplifies discriminative features while suppressing noise from less informative ones. Drawing inspiration from FLEN’s design philosophy, we additionally introduce a feature-weighting bilinear interaction (FWBI) layer to resolve gradient coupling phenomena during feature interaction learning. Extensive experimental evaluations on multiple public datasets demonstrate that our FLSEST model achieves superior prediction performance compared to state-of-the-art shallow and deep recommendation models. Moreover, integrating our proposed SEST network with mainstream models such as FwFM and DeepFM further enhances their predictive capabilities, confirming the versatility and effectiveness of our approach.