Diversity, Intent, and Aggregated Search

Major search engines perform what is known as Aggregated Search (AS). They integrate results coming from different vertical search engines (images, videos, news, etc.) with typical Web search results. Aggregated search is relatively new and its advantages need to be evaluated. Some existing works have already tried to evaluate the interest (usefulness) of aggregated search as well as the effectiveness of the existing approaches. However, most of evaluation methodologies were based (i) on what we call relevance by intent (i.e. search results were not shown to real users), and (ii) short text queries. In this paper, we conducted a user study which was designed to revisit and compare the interest of aggregated search, by exploiting both relevance by intent and content, and using both short text and fixed need queries. This user study allowed us to analyze the distribution of relevant results across different verticals, and to show that AS helps to identify complementary relevant sources for the same information need. Comparison between relevance by intent and relevance by content showed that relevance by intent introduces a bias in evaluation. Discussion about the results also allowed us to identify some useful thoughts concerning the evaluation of AS approaches.

The aim of this research proposal is to understand the extent in which Information Scent, part of Information Foraging Theory, influence the perception of relevance of retrieved search engine result pages (SERPs) in different environments (desktop and mobile). %We intend to study this influence by observing information seekers' behavior when they are searching for topics on a typical search engine. Firstly, we planned to investigate how previously found correlations between information scent and search behavior on desktop SERPs may also applicable on mobile. Secondly, we investigate if the differences in search behavior are also caused by SERP visibility and/or physical input behavior in different environments. Lastly, we would like to observe the extent in which changing snippet length can affect web search behavior. The perception of relevance on SERPs might be similar in both environments because the arrangement of SERPs on both mobile and desktop are identical; or different because the visibility of search results is less on mobile compared to desktop, due to limited screen sizes. We wanted to study the extent of which the physical limitations might significantly impact search behavior in different environments due to expected ``hidden scent. We conducted two user studies to observe these phenomenon and seek to understand search behavior by manipulating the SERPs presented to users. We observed both similarities and differences in web search behavior between the environments. On the desktop, increasing information scent led to lower positions of search results saved as relevant and lower positions of mouse hover. However, on mobile, increasing relevant search results beyond the initial screen size reduced number of documents examined.

Aggregating search results from a variety of diverse verticals such as news, images, videos and Wikipedia into a single interface is a popular web search presentation paradigm. Although several aggregated search (AS) metrics have been proposed to evaluate AS result pages, their properties remain poorly understood. In this paper, we compare the properties of existing AS metrics under the assumptions that (1) queries may have multiple preferred verticals; (2) the likelihood of each vertical preference is available; and (3) the topical relevance assessments of results returned from each vertical is available. We compare a wide range of AS metrics on two test collections. Our main criteria of comparison are (1) discriminative power, which represents the reliability of a metric in comparing the performance of systems, and (2) intuitiveness, which represents how well a metric captures the various key aspects to be measured (i.e. various aspects of a user's perception of AS result pages). Our study shows that the AS metrics that capture key AS components (e.g., vertical selection) have several advantages over other metrics. This work sheds new lights on the further developments and applications of AS metrics.

Search engines have become an important channel for increasing SMEs' global reach as well as competing with larger companies. Therefore, SMEs are enhancing their business visibility through search engine optimization (SEO). With effective SEO techniques, small companies can appear ahead of large, well-known companies on search results. However, in addition increasing the ranking on a search engine results page (SERP), there may be other benefits that SMEs may enjoy while engaging in an SEO campaign. This study uses multiple firms as cases to illustrate how SEO initiatives can impact performance for SMEs. The authors found that in addition to increasing the rankings on SERPs, SMEs saw an increase in number of visitors to the site, the average time duration of users visiting the site, more user engagement and an increase of annual sales revenue.

In this paper, a two-level deep learning framework is presented to model human information foraging behavior with search engines. A recurrent neural network architecture is designed using LSTM as the base unit to explicitly consider the temporal and spatial dependencies of information scents, the key concept in Information Foraging Theory. The target is to predict several major search behaviors, such as query abandonment, query reformulation, number of clicks, and information gain. The memory capability and the sequence structure of LSTM allow to naturally mimic not only what users are perceiving and performing at the moment but also what they have seen and learned from the past during the search dynamics. The promising results indicate that our information scent models with different input variations were better, compared to the state-of-the art neural click models, at predicting some search behaviors. When incorporating the knowledge from a previous query in the same search session, the prediction of current query abandonment, pagination, and information gain has been improved. Compared to the well known neural click models that model search behaviors under a single search query thread, this study takes a broader view to consider an entire search session which may contain multiple queries. More importantly, our model takes the search result relevance pattern on the Search Engine Results Pages (SERP) as a whole as the information scent input to the deep learning model, instead of considering one search result at each step. The results have insights on the impact of information scents on how people forage for information, which has implications for designing or refining a set of design guidelines for search engines.

Understanding how users examine result pages across a broad range of information needs is critical for search engine design. Cursor movements can be used to estimate visual attention on search engine results page (SERP) components, including traditional snippets, aggregated results, and advertisements. However, these signals can only be leveraged for SERPs where cursor tracking was enabled, limiting their utility for informing the design of new SERPs. In this work, we develop robust, log-based mouse movement models capable of estimating searcher attention on novel SERP arrangements. These models can help improve SERP design by anticipating searchers' engagement patterns given a proposed arrangement. We demonstrate the efficacy of our method using a large set of mouse-tracking data collected from two independent commercial search engines.

Googling for Health Information

The study of search satisfaction is one of the prime concerns in search performance evaluation research. Most existing works on search satisfaction primarily rely on the hypothesis that all results on search engine result pages (SERPs) are homogeneous. However, a variety of heterogeneous vertical results such as videos, images and instant answers are aggregated into SERPs by search engines to improve the diversity and quality of search results. In this paper, we carry out a lab-based user study with specifically designed SERPs to determine how verticals with different qualities and presentation styles affect search satisfaction. Users' satisfaction feedback and external assessors' satisfaction annotations are both collected to make a comparison regarding the perception of search satisfaction. Mouse click-through / movement data and eye movement information are also collected such that we can investigate the influence of vertical results from the perspectives of both benefit and cost. Finally, a vertical-aware learning-based prediction method is proposed to predict search satisfaction on aggregated SERPs. To the best of our knowledge, this paper is the first to analyze the effect of verticals on search satisfaction. The results show that verticals with different qualities, presentation styles and positions have different effects on search satisfaction, among which Encyclopedia verticals, as well as Download verticals, will bring the largest improvement. Furthermore, our proposed vertical-aware prediction method outperforms state-of-the-art methods that are designed for search satisfaction prediction in homogeneous environment.

PurposeGood abandonment behavior refers to users obtaining direct answers via search engine results pages (SERPs) without clicking any search result, which occurs commonly in mobile search. This study aims to better understand users' good abandonment behavior and perception, and then construct a good abandonment prediction model for mobile search with improved performance.Design/methodology/approachIn this study, an in situ user mobile search experiment (N = 43) and a crowdsourcing survey (N = 1,379) were conducted. Good abandonment behavior was analyzed from a quantitative perspective, exploring users' search behavior characteristics from four aspects: session and query, SERPs, gestures and eye-tracking data.FindingsUsers show less engagement with SERPs in good abandonment, spending less time and using fewer gestures, and they pay more visual attention to answer-like results. It was also found that good abandonment behavior is often related to users' perceived difficulty of the searching tasks and trustworthiness in the search engine. A good abandonment prediction model in mobile search was constructed with a high accuracy (97.14%).Originality/valueThis study is the first to explore eye-tracking characteristics of users' good abandonment behavior in mobile search, and to explore users' perception of their good abandonment behavior. Visual attention features are introduced into good abandonment prediction in mobile search for the first time and proved to be important predictors in the proposed model.

Estimation of session duration for an e-commerce search engine is important for various downstream applications, including user satisfaction prediction, personalization, and diversification of search results. It has been shown in previous studies that search session length has a strong correlation with user's explore vs specific purchase intent. Based on previous work [14], we hypothesize that early prediction of session length distribution can be used to control the degree of explore vs exploit (loosely related to diversification v/s personalization) for Search Engine Result Pages (SERPs) in the user's session to follow. In this work, we try to early predict the user's session length, which will enable the control on explore v/s exploit of the search results. Towards this end, based on previous work and strong empirical evidence, we hypothesize session lengths are Weibull distributed and propose its parameters being modeled by a Recurrent Neural Network over actions in user's search sessions. Through experimentation, we demonstrate that our method performs better as compared to strong baselines for the same.

This research focuses on what users know about search engine optimization (SEO) and how well they can identify results that have potentially been influenced by SEO. We conducted an online survey with a sample representative of the German online population (N = 2,012). We found that 43% of users assume a better ranking can be achieved without paying money to Google. This is in stark contrast to the possibility of influence through paid advertisements, which 79% of internet users are aware of. However, only 29.2% know how ads differ from organic results. The term ‘search engine optimization’ is known to 8.9% of users but 14.5% can correctly name at least one SEO tactic. Success in labelling results that can be influenced through SEO varies by search engine result page (SERP) complexity and devices: participants achieved higher success rates on SERPs with simple structures than on the more complex SERPs. SEO results were identified better on the small screen than on the large screen. 59.2% assumed that SEO has a (very) strong impact on rankings. SEO is more often perceived as positive (75.2%) than negative (68.4%). The insights from this study have implications for search engine providers, regulators, and information literacy.

Article Info/Makale BilgisiNowadays, it can be said that studies on the intersection of "big data-network analysis" in the field of tourism have intensified, and the usage of Google and other social media platforms as the main big data sources has increased.This intersection study topic is important for determining tourist attraction centers and tourist routes, optimizing selection-decision systems, monitoring satisfaction and behavior, and thus determining tourist actions and usage patterns.In this regard, the main aim of the study is to analyze the spatial network structure created by the tourist attractions in Edirne city center and to evaluate it using the network analysis method.In this context, the locations defined in the "Things To-Do" category for Edirne city center on the Google Maps platform were considered.Depending on the content of the Google Search Engine Results Page (SERP), the rating level of the locations and the "People Also Searching For (PASF)" information, which is an important content in the field of Search Engine Optimization (SEO) today, were collected.PASF information reveals other places that users are searching for depending on a location, and as a result, a tourism search network is formed.Network analysis was applied, and evaluations were made considering the spatial dimensions of the locations.As a result, tourism places with high centrality value for Edirne city center, thematic neighborhood groups were identified, and route suggestions were presented.It is expected that the study will contribute to literature in terms of method and evaluation perspective. Gnmzde turizm alannda "byk veri-a analizi" kesiiminde almalarn younlat, Google ve dier sosyal medya platformlarnn temel byk veri kaynaklar kullanmnn artt sylenebilmektedir. Bu ara kesiim alma temas ise turistik cazibe merkezleri ve turistik rotalarn belirlenebilmesi, seim-karar sistemlerinin optimize edilebilmesi, memnuniyet ve davranlarn izlenebilmesi dolays turist davran ve kullanm rntlerinin tespit edilebilmesi iin nemlidir. Bu bakmdan almann temel amac Edirne kent merkezindeki turistik ekici noktalarn yaratm olduu meknsal a yapsnn zmlenmesi ve a analizi yntemi ile deerlendirilmesidir. Bu kapsamda Google Maps platformunda Edirne kent merkezi iin "ThingsTo-Do" kategorisinde tanmlanan konumlar ele alnmtr.Google Search Engine Results Page (SERP) ieriine bal olarak, konumlarn rating dzeyi ile gnmzde Search Engine Optimization (SEO) alannda nemli bir ierik olan "People Also Search For (PASF)" bilgisi toplanmtr.PASF bilgisi, kullanclarn bir konuma bal olarak aradklar dier konumlar ortaya koymakta sonucunda ise bir turizm arama a elde edilmektedir

When we search information on the Web, we usually enter a query that best represents our information need, and then modify it as the search results are shown. Many thus have studied search queries to understand how users express and find their information need on the Web. For example, studies have shown that query length affects search performance (Belkin et al, 2003). Then, researchers have begun to examine image search queries on the Web. They analyzed search logs from general or commercial search engines or studied specific image database systems based on log analysis (Goodrum & Sprink, 2001; Jorgensen & Jorgensen, 2005; Goodrum, Bejune, & Siochi, 2003; Pu, 2005; Cunningham, Bainbridge, & Masoodian, 2004). While these studies provide basic understanding of image query formulation, further characteristics of user's queries in the interactive searching process have not been explored. This study thus builds on previous studies on image search queries by examining the patterns of query formulation and modification in visual information search. Specifically, first, it investigates the effects of tasks and other contextual factors on query length and query changes; and second, it compares the query length and changes with the user's satisfaction of search results. Participants Twenty-nine college students were recruited for data collection during the spring and summer semesters of 2008, mostly from the Department of Media Studies at the Catholic University of America (CUA). They included 22 from Media Studies and 7 from other fields taking courses in the department; 22 were females and 7 males; and an average age of 21 years. Background information collected were years of computer experience, hours per day using the Web, self-rated online searching expertise, and self-rated digital image searching experience. The seven-point Likert scale was used for self-rating variables. Each participant took part in three different search sessions with his or her own search topic at CUA with the researcher's laptop that had an Internet connection. The university's homepage was set as the default home for the Web browser. Each participant filled out a pre-search questionnaire to collect a search task, topic, self-rated topic familiarity using 7-Likert scale. The participants chose their own preferred browser between Internet Explorer and Firefox. The search session was recorded by Camtasia v5.0 as screen capture. After the session, a post-search questionnaire was given to the participants to assess the level of satisfaction and confidence of their searching using 7-Likert scale. There was no time limit to complete a search session. The participants were free to end a search session at any time. Search Tasks The participants brought their own task and topics to find images. There were three types of search tasks identified: academic task, work-related task, and personal interest (Table 1). Search queries were acquired from screen capture log files. A total of 979 queries were obtained as the entire string of terms submitted by the participants. Table 2 shows a distribution of search queries on a general search engine, a search engine's image search, and other specific sites. Findings showed that Google Images Search was the most frequent source for forming a search query. An average word per query was 3.07 during the first session, 2.95 during the second session, and 2.89 for the last session. This finding of short queries is similar to that of previous studies on Web image queries (Goodrum & Spink, 2001; Jorgensen & Jorgensen, 2005). Analysis of data is in progress. A poster will summarize detail findings and provide answers to the following questions: What are the patterns of query modification? Are there any differences of query length and number of query change based on the task and topic familiarities? Is there any relationship between query length and number of query change and users' satisfaction and confidence with search results? Do different stages of a user's particular task affect query length and number of query change? The author would like to thank OCLC/ALISE for their generous funding for this project.

Search engines are web-based systems used for retrieving information from the Internet. They have economic power because of their positioning between information providers and information seekers. Search engines can influence the flow of information – possible business transactions – by the way information is indexed, stored, and portrayed as search results. If a search engine provides good coverage of website content from one group of information providers (grouped by country or language) to the detriment of another group, it will have economic implications for both groups. It is known that certain developed countries and the language(s) of these countries, have better coverage than other developed countries and their languages. This study investigates for the first time the website coverage of a developing country, South Africa, and one of its indigenous languages, Afrikaans. What does the existence of search engine country bias and/or linguistic bias imply for developing countries such as South Africa? South African information providers would have reason for concern if information seekers’ attention were continually routed abroad by biased search engines. South African information seekers would also be done an injustice if they cannot find local web content (or content in their indigenous languages) due to poor search engine coverage. Biased search results guide them away from cheaper, more convenient local information due to poor coverage of local content. Search engines are negatively impacted in turn, when users become tired of the poor local coverage and unwanted international search results and turn to other tools, such as local search engines, for information retrieval. How severe are the effects of search engine bias on developing countries such as South Africa? The body of knowledge discussing search engine bias is very limited, and this study is motivated by stating that, given the possibility of negative economic implications of such bias for developing economies, more research on this topic is justified and urgently needed. The study revealed that Western website content enjoys better coverage than South African website content. After further investigation it was also found that English website content enjoys better coverage than website content in Afrikaans. There is, therefore, a proven search engine bias in favour of Western developed countries and the English language. Website visibility is also studied as a possible cause of search engine bias. It would seem plausible that a relationship may exist between the coverage of websites by search engines and how visible these websites are to a search engine’s crawlers. For the determination of website visibility, the number of in links towards each sample domain was determined. This study shows that the higher visibility of websites from developed countries is a cause of search engine bias in favour of these websites. With website visibility proven as a cause of bias, the study indicates that South Africa, and possibly other developing countries, is lagging far behind in the race to create highly visible websites surrounded by well-covered hyperlink structures – the kind of websites most likely to be covered by search engine crawlers. It is the responsibilty of information providers from developing countries to create more hyperlinks between websites from their countries, as well as creating visible website content in their indigenous languages. Search engine coverage bias has negative economic implications for developing countries such as South Africa. This paper investigates the severity of country related coverage bias against websites from the South African domain(s) and the correlation between website coverage and website visibility. Other possible causes of coverage bias found in literature include indexing algorithms, ranking algorithms and lexicons struggling with non-English content. Information providers’ lack of knowledge about website coverage and search engine tools is discussed as another possible cause of country bias.

By focusing on the point where the document triage process interacts with a search engine results page (SERP), this experiment extends our knowledge about both SERP design and document triage behavior. Prior SERP work has shown that longer meta descriptions in SERPs improve people's ability to answer information based questions, while document triage research has shown the importance of abstracts in making relevancy decisions. Using eye tracking equipment this work employed a repeated measure within factors experimental design method replacing the existing Google Scholar (GS) SERP meta descriptions with the abstracts of the corresponding retrieved articles. Undergraduate freshmen participants were asked to use two different GS SERPs, one with a control design and one with the experimental design and determine which resources are relevant to their assigned research task. The findings show that the participants changed how long they looked at the expanded meta description, while noticeably reducing how long they gazed at other parts of the page supporting other research findings. The addition of abstracts changed user behavior by reducing how often they made surrogate level document transitions, but did not change how often they sought out full-text documents, supporting the principle of least effort. The addition of abstracts did not contribute to changes in total time on task or participant's relevancy accuracy. This study's findings conflict with other work that found that longer meta descriptions corresponded with a reduction in total task time and an improvement in accuracy for informational tasks. Further research is needed to determine if this conflict was due to task differences or if the document triage task was not challenging enough.