Query Chains: Learning to Rank from Implicit Feedback

This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.Comment: 10 page

Implementation of White Hat SEO Techniques to Improve Digital Promotion of Village Potentials Product (Case Study: Kebun Kelapa Village)

This paper investigates the implementation of white hat SEO (search engine optimization) techniques to promote village potential products online, using the case study of coconut distributors in Kebun Kelapa Village, Medan, Indonesia. The specific SEO techniques explored include keyword optimization, quality content creation, meta tag and description refinement, alt text for images, internal linking between related posts, and optimizing for mobile devices. Initial results show that targeted keyword usage, organizing site structure, and highlighting unique value propositions in content can effectively improve site visibility and search engine rankings. Further analysis on user engagement and lead generation conversion is still required. Adoption of white hat SEO strategies has promising potential for rural businesses to expand their reach via digital platforms.

Testing the stability of “wisdom of crowds” judgments of search results over time and their similarity with the search engine rankings

PURPOSE: One of the under-explored aspects in the process of user information seeking behaviour is influence of time on relevance evaluation. It has been shown in previous studies that individual users might change their assessment of search results over time. It is also known that aggregated judgments of multiple individual users can lead to correct and reliable decisions; this phenomenon is known as the “wisdom of crowds”. The aim of this study is to examine whether aggregated judgments will be more stable and thus more reliable over time than individual user judgments. DESIGN/METHODS: In this study two simple measures are proposed to calculate the aggregated judgments of search results and compare their reliability and stability to individual user judgments. In addition, the aggregated “wisdom of crowds” judgments were used as a means to compare the differences between human assessments of search results and search engine’s rankings. A large-scale user study was conducted with 87 participants who evaluated two different queries and four diverse result sets twice, with an interval of two months. Two types of judgments were considered in this study: 1) relevance on a 4-point scale, and 2) ranking on a 10-point scale without ties. FINDINGS: It was found that aggregated judgments are much more stable than individual user judgments, yet they are quite different from search engine rankings. Practical implications: The proposed “wisdom of crowds” based approach provides a reliable reference point for the evaluation of search engines. This is also important for exploring the need of personalization and adapting search engine’s ranking over time to changes in users preferences. ORIGINALITY/VALUE: This is a first study that applies the notion of “wisdom of crowds” to examine the under-explored phenomenon in the literature of “change in time” in user evaluation of relevance

Analysis of change in users' assessment of search results over time

We present the first systematic study of the influence of time on user judgements for rankings and relevance grades of web search engine results. The goal of this study is to evaluate the change in user assessment of search results and explore how users' judgements change. To this end, we conducted a large-scale user study with 86 participants who evaluated two different queries and four diverse result sets twice with an interval of two months. To analyse the results we investigate whether two types of patterns of user behaviour from the theory of categorical thinking hold for the case of evaluation of search results: (1) coarseness and (2) locality. To quantify these patterns we devised two new measures of change in user judgements and distinguish between local (when users swap between close ranks and relevance values) and non-local changes. Two types of judgements were considered in this study: 1) relevance on a 4-point scale, and 2) ranking on a 10-point scale without ties. We found that users tend to change their judgements of the results over time in about 50% of cases for relevance and in 85% of cases for ranking. However, the majority of these changes were local

AN IMPROVEMENT TOWARDS CONSIDERING PREFERENCES OF WEB SEARCH

With the rising number of web users using Smartphone in addition to its individualized service under examination, the environment of Smartphone does not make available user’s search rankings suitable to personal inclinations. Ontology-based user profiles can productively confine users’ content as well as location preferences and make use of the preferences to make relevant results for users. A realistic design was introduced for Personalized Mobile Search Engine by adopting the approach of meta-search which relies on the commercial search engines, to carry out a genuine search. In Personalized Mobile Search Engine, ontologies were accepted to structure the concept space intended for the reason that they not only can stand up for concepts but also hold the relations between concepts. The design of personalized mobile search engine addressed the issues such as restricted computational power on mobile devices, and minimization of data transmission. Proposed design accept server-client model in which user queries are forwarded towards a personalized mobile search engine server for processing training as well as re-ranking rapidly

Accessibility-based reranking in multimedia search engines

Traditional multimedia search engines retrieve results based mostly on the query submitted by the user, or using a log of previous searches to provide personalized results, while not considering the accessibility of the results for users with vision or other types of impairments. In this paper, a novel approach is presented which incorporates the accessibility of images for users with various vision impairments, such as color blindness, cataract and glaucoma, in order to rerank the results of an image search engine. The accessibility of individual images is measured through the use of vision simulation filters. Multi-objective optimization techniques utilizing the image accessibility scores are used to handle users with multiple vision impairments, while the impairment profile of a specific user is used to select one from the Pareto-optimal solutions. The proposed approach has been tested with two image datasets, using both simulated and real impaired users, and the results verify its applicability. Although the proposed method has been used for vision accessibility-based reranking, it can also be extended for other types of personalization context