Search shortcuts using click-through data

In this paper we propose a query suggestion method for price comparison search engines. Query suggestion techniques are used for generating alternative queries to facilitate web users in information seeking; in this specific domain, suggestions provided to web users need to be properly generated taking into account that the suggested products must be still available for sale. We propose a novel approach based on a slightly variant of classical query-URL graphs: the query-product click-through bipartite graph. Information extracted both from search engine logs and specific domain features are exploited to build the graph, and one of the advantages of this model is that such a graph can be used to suggest not only related queries but also related products. Concepts used in the proposed method are not restricted to our context but are used in many other major e-commerce and search engine websites, we tested the model on several challenging datasets, and also compared with a recent query suggestion approach specifically designed for price comparison engines. Our solution outperforms the competing approach, achieving higher results in terms of relevance of the provided suggestions and coverage rates on top-8 suggestions.

In most major search engines, the interface for image search is the same as traditional Web search: a keyword query followed by a paginated, ranked list of results. Although many image search innovations have appeared in both the literature and on the Web, few have seen widespread use in practice. In this work, we explore the differences between image and general Web search to better support users’ needs. First, we describe some unique characteristics of image search derived through informal interviews with researchers, designers, and managers responsible for building and deploying a major Web search engine. Then, we present results from a large scale analysis of image and Web search logs showing the differences in user behaviour. Grounded in these observations, we present design recommendations for an image search engine supportive of the unique experience of image search. We iterate on a number of designs, and describe a functional prototype that we built.

Imagine a physician and a patient doing a search on antibiotic resistance. Or a chess amateur and a grandmaster conducting a search on Alekhine's Defence. Although the topic is the same, arguably the two users in each case will satisfy their information needs with very different texts. Yet today search engines mostly adopt the one-size-fits-all solution, where personalization is restricted to topical preference. We found that users do not uniformly prefer simple texts, and that the text comprehensibility level should match the user's level of preparedness. Consequently, we propose to model the comprehensibility of texts as well as the users' reading proficiency in order to better explain how different users choose content for further exploration. We also model topic-specific reading proficiency, which allows us to better explain why a physician might choose to read sophisticated medical articles yet simple descriptions of SLR cameras. We explore different ways to build user profiles, and use collaborative filtering techniques to overcome data sparsity. We conducted experiments on large-scale datasets from a major Web search engine and a community question answering forum. Our findings confirm that explicitly modeling text comprehensibility can significantly improve content ranking (search results or answers, respectively).

PurposeIt is important to measure the interaction between conversing in social media and searching on the web in order to understand the impact on electronic word-of-mouth marketing. The paper aims to discuss this issue.Design/methodology/approachThe authors research the relationship between social media conversing and web searching concerning brands on three major social soundtrack platforms (Instagram, Twitter, and Tumblr) and on a major web search engine (Google). The authors examine the effects of changes in both volume and attitude of conversing and volume of searching for two phases (Pre and Post) concerning brands in commercials aired during Super Bowl XLIX. The authors perform Granger causality testing and panel data regression analysis to determine the causal relationship between social media conversing and web searching.FindingsResults show that volume and attitude of social media conversing has a significant causality relationship to the volume of web searching. Each unit increase of volume on Twitter, Instagram, and Tumblr significantly increases Google search volume for the same brands by 4.7 times, 11.9 times, and 8.7 times, respectively. Each unit increase of attitude score on Twitter significantly increases web search volume 3.96 times, while for Tumblr, search volume significantly increases 0.95 times with each unit. Interestingly, search volume also has a significant causality relationship on the volume of social media postings.Originality/valueThis research seeks to understand the commercial impacts of the interaction among broadcast advertising, social media conversing, and web searching for which there is limited prior work, especially in the context of a major media event.

As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model support d by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world. Table of Contents: Introduction / Defining Exploratory Search / Related Work / Features of Exploratory Search Systems / Evaluation of Exploratory Search Systems / Future Directions and concluding Remarks

In recent years, the ever-increasing quantities of entities in large knowledge bases on the Web, such as DBpedia, Freebase and YAGO, pose new challenges but at the same time open up new opportunities for intelligent information access. These knowledge bases (KBs) have become valuable resources in many research areas, such as natural language processing (NLP) and information retrieval (IR). Recently, almost every major commercial Web search engine has incorporated entities into their search process, including Google’s Knowledge Graph, Yahoo!’s Web of Objects and Microsoft’s Satori Graph/Bing Snapshots. The goal is to bridge the semantic gap between natural language text and formalized knowledge. Within the context of globalization, multilingual and cross-lingual access to information has emerged as an issue of major interest. Nowadays, more and more people from different countries are connecting to the Internet, in particular the Web, and many users can understand more than one language. While the diversity of languages on the Web has been growing, for most people there is still very little content in their native language. As a consequence of the ability to understand more than one language, users are also interested in Web content in other languages than their mother tongue. There is an impending need for technologies that can help in overcoming the language barrier for multilingual and cross-lingual information access. In this thesis, we face the overall research question of how to allow for semantic-aware and cross-lingual processing of Web documents and user queries by leveraging knowledge bases. With the goal of addressing this complex problem, we provide the following solutions: (1) semantic annotation for addressing the semantic gap between Web documents and knowledge; (2) semantic search for coping with the semantic gap between keyword queries and knowledge; (3) the exploitation of cross-lingual semantics for overcoming the language barrier between natural language expressions (i.e., keyword queries and Web documents) and knowledge for enabling cross-lingual semantic annotation and search. We evaluated these solutions and the results showed advances beyond the state-of-the-art. In addition, we implemented a framework of cross-lingual semantic annotation and search, which has been widely used for cross-lingual processing of media content in the context of our research projects.

Query formulation is one of the most difficult and important aspects of information seeking and retrieval. Two techniques, term relevance feedback and query suggestion, provide methods to help users formulate queries, but each is limited in different ways. In this research we combine these two techniques by automatically creating query suggestions using term relevance feedback techniques. To evaluate our approach, we conducted an interactive information retrieval study with 55 subjects and 20 topics. Each subject completed four topics, half with a term suggestion system and half with a query suggestion system. We also investigated the source of the suggestions: approximately half of all subjects were provided with system-generated suggestions, while half were provided with user-generated suggestions. Results show that subjects used more query suggestions than term suggestions and saved more documents with these suggestions, even though there were no significant differences in performance. Subjects preferred the query suggestion system and rated it higher along a number of dimensions including its ability to help them think of new approaches to searching. Qualitative data provided insight into subjects' usage and ratings, and indicated that subjects often used the suggestions even when they did not click on them.

PurposeThe purpose of this study is to generate diversified results for temporally ambiguous queries and the candidate queries are ensured to have a high coverage of subtopics, which are derived from different temporal periods.Design/methodology/approachTwo novel time-aware query suggestion diversification models are developed by integrating semantics and temporality information involved in queries into two state-of-the-art explicit diversification algorithms (i.e. IA-select and xQuaD), respectively, and then specifying the components on which these two models rely on. Most importantly, first explored is how to explicitly determine query subtopics for each unique query from the query log or clicked documents and then modeling the subtopics into query suggestion diversification. The discussion on how to mine temporal intent behind a query from query log is also followed. Finally, to verify the effectiveness of the proposal, experiments on a real-world query log are conducted.FindingsPreliminary experiments demonstrate that the proposed method can significantly outperform the existing state-of-the-art methods in terms of producing the candidate query suggestion for temporally ambiguous queries.Originality/valueThis study reports the first attempt to generate query suggestions indicating diverse interested time points to the temporally ambiguous (input) queries. The research will be useful in enhancing users’ search experience through helping them to formulate accurate queries for their search tasks. In addition, the approaches investigated in the paper are general enough to be used in many domains; that is, experimental information retrieval systems, Web search engines, document archives and digital libraries.

Search systems in online content platforms are typically biased toward a minority of highly consumed items, reflecting the most common user behavior of navigating toward content that is already familiar and popular. Query suggestions are a powerful tool to support query formulation and to encourage exploratory search and content discovery. However, classic approaches for query suggestions typically rely either on semantic similarity, which lacks diversity and does not reflect user searching behavior, or on a collaborative similarity measure mined from search logs, which suffers from data sparsity and is biased by highly popular queries. In this work, we argue that the task of query suggestion can be modelled as a link prediction task on a heterogeneous graph including queries and documents, enabling Graph Learning methods to effectively generate query suggestions encompassing both semantic and collaborative information. We perform an offline evaluation on an internal Spotify dataset of search logs and on two public datasets, showing that node2vec leads to an accurate and diversified set of results, especially on the large scale real-world data. We then describe the implementation in an instant search scenario and discuss a set of additional challenges tied to the specific production environment. Finally, we report the results of a large scale A/B test involving millions of users and prove that node2vec query suggestions lead to an increase in online metrics such as coverage (+1.42% shown search results pages with suggestions) and engagement (+1.21% clicks), with a specifically notable boost in the number of clicks on exploratory search queries (+9.37%).

We conducted a user study to analyze how health literacy, topic familiarity and the terminology used in past queries affect query behavior in health searches. We found that users with inadequate health literacy have less success in web searches and show more difficulties in query formulation. These users and the ones not familiar with the topic use medico-scientific terminology less often than users with more health literacy and topic familiarity. We conclude that search engines should help these groups of users in query formulation and, since technical documents stimulate the use of medico-scientific terminology in query reformulation, mechanisms like query suggestion can have long-term benefits. © 2013 Springer-Verlag.

Personalized query suggestion based on user behavior

With the exponential growth of information on the internet, users have been relying on search engines for finding the precise documents. However, user queries are often short. The inherent ambiguity of short queries imposes great challenges for search engines to understand user intent. Query suggestion is one key technique for search engines to augment user queries so that they can better understand user intent. In the past, query suggestions have been relying on either term-frequency--based methods with little semantic understanding of the query, or word-embedding--based methods with little personalization efforts. Here, we present a sequence-to-sequence-model--based query suggestion framework that is capable of modeling structured, personalized features and unstructured query texts naturally. This capability opens up the opportunity to better understand query semantics and user intent at the same time. As the largest professional network, LinkedIn has the advantage of utilizing a rich amount of accurate member profile information to personalize query suggestions. We applied this framework in the LinkedIn production traffic and showed that personalized query suggestions significantly improved member search experience as measured by key business metrics at LinkedIn.

The great progress in recommendation system help users discover more interesting items that satisfy their appetites. Considering the video recommendation is an increasing popular sub-field of recommendation, but the traditional recommendation techniques such as Collaborative Filtering and Content-based model simply exploit one information source that limits its performance. In this paper, we proposed a Multi-info fusion based recommendation system which integrates several different information sources to comprehensively model the similarity between videos. The information sources including the common user-item rating data and video’s textual content that consists of video’s genres and textual description. Experimental results on a public dataset show that the proposed system is of high quality and achieves significant improvements over the traditional Collaborative Filtering techniques.

BackgroundAcute pancreatitis is a prevalent condition in China. A plethora of information pertaining to acute pancreatitis is readily available on the Internet, including on major search engines and various short video applications. The objective of this study was to evaluate and compare the quality, content completeness, and accuracy of information related to acute pancreatitis on web pages of the four major search engines and short videos on the two major short video platforms.Materials and methodsA search was conducted on four major search engines (Bing, Baidu, Haosou, and Sougou) and two short video apps (TikTok and BiliBili) using the Chinese keyword “acute pancreatitis.” The sources can be divided into four categories: scientific resources, news/media reports, industrial/commercial profit organizations, and healthcare providers. The quality of the web pages and short videos was evaluated using the DISCERN instrument, the Global Quality Score (GQS), and the Journal of the American Medical Association (JAMA). In accordance with established guidelines and reviews of acute pancreatitis, two lists and scoring systems were devised for the evaluation of content comprehensiveness and accuracy.ResultsA total of 120 unique web pages and 120 unique videos were identified using four search engines and two short video applications, respectively. The most prevalent identity among those producing short videos was that of healthcare providers. With regard to the source of the web pages, the most prevalent category was that of industrial/commercial profit organizations. The median DISCERN total score, median GQS score, and median JAMA score were 26, 3, and 2, respectively. Web pages exhibited significantly higher ratings than short videos (p < 0.001, p = 0.003, and p < 0.001). The median overall content score for the web page was 8 (interquartile range [IQR] 4–13), and the median guideline-related score was 2 (IQR 0–4), indicating that the web page’s content was deficient in terms of completeness and accuracy. In comparison, the performance of the short video was found to be even more deficient.ConclusionIn China, most web pages pertaining to acute pancreatitis were produced by industrial/commercial profit organizations, but the quality of the information provided by these entities was found to be the lowest. The majority of short videos were created by healthcare providers, but the overall quality of these videos was found to be inadequate. In general, the quality of both web pages and short videos is suboptimal. Nevertheless, the quality of web pages was found to be superior to that of short videos. In terms of completeness and accuracy, both the web page and the video exhibit significant deficiencies that are cause for concern.

Due to the dynamic nature of web and the complex architectures of modern commercial search engines, top results in major search engines can change dramatically over time. Our experimental data shows that, for all three major search engines (Google, Bing and Yahoo!), approximately 90% of queries have their top 10 results altered within a period of ten days. Although this instability is expected in some situations such as in news-related queries, it is problematic in general because it can dramatically affect retrieval performance measurements and negatively affect users' perception of search quality (for instance, when users cannot re-find a previously found document). In this work we present the first large scale study on the degree and nature of these changes. We introduce several types of query instability, and several metrics to quantify it.We then present a quantitative analysis using 12,600 queries collected from a commercial web search engine over several weeks. Our analysis shows that the results from all major search engines have similar levels of instability, and that many of these changes are temporary. We also identified classes of queries with clearly different instability profiles - for instance, navigational queries are considerably more stable than non-navigational, while longer queries are significantly less stable than shorter ones.