Clarifying Ambiguous Keywords with Personal Word Embeddings for Personalized Search

Personalized search tailors document ranking lists for each individual user based on her interests and query intent to better satisfy the user’s information need. Many personalized search models have been proposed. They first build a user interest profile from the user’s search history, and then re-rank the documents based on the personalized matching scores between the created profile and candidate documents. In this article, we attempt to solve the personalized search problem from an alternative perspective of clarifying the user’s intention of the current query. We know that there are many ambiguous words in natural language such as “Apple.” People with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, we propose a personalized search model with personal word embeddings for each individual user that mainly contain the word meanings that the user already knows and can reflect the user interests. To learn great personal word embeddings, we design a pre-training model that captures both the textual information of the query log and the information about user interests contained in the click-through data represented as a graph structure. With personal word embeddings, we obtain the personalized word and context-aware representations of the query and documents. Furthermore, we also employ the current session as the short-term search context to dynamically disambiguate the current query. Finally, we use a matching model to calculate the matching score between the personalized query and document representations for ranking. Experimental results on two large-scale query logs show that our designed model significantly outperforms state-of-the-art personalization models.

“What Can I Cook with these Ingredients?” - Understanding Cooking-Related Information Needs in Conversational Search

As conversational search becomes more pervasive, it becomes increasingly important to understand the users’ underlying information needs when they converse with such systems in diverse domains. We conduct an in situ study to understand information needs arising in a home cooking context as well as how they are verbally communicated to an assistant. A human experimenter plays this role in our study. Based on the transcriptions of utterances, we derive a detailed hierarchical taxonomy of diverse information needs occurring in this context, which require different levels of assistance to be solved. The taxonomy shows that needs can be communicated through different linguistic means and require different amounts of context to be understood. In a second contribution, we perform classification experiments to determine the feasibility of predicting the type of information need a user has during a dialogue using the turn provided. For this multi-label classification problem, we achieve average F1 measures of 40% using BERT-based models. We demonstrate with examples which types of needs are difficult to predict and show why, concluding that models need to include more context information in order to improve both information need classification and assistance to make such systems usable.

I Know What You Need: Investigating Document Retrieval Effectiveness with Partial Session Contexts

Reducing user effort in finding relevant information is one of the key objectives of search systems. Existing approaches have been shown to effectively exploit the context from the current search session of users for automatically suggesting queries to reduce their search efforts. However, these approaches do not accomplish the end goal of a search system—that of retrieving a set of potentially relevant documents for the evolving information need during a search session. This article takes the problem of query prediction one step further by investigating the problem of contextual recommendation within a search session. More specifically, given the partial context information of a session in the form of a small number of queries, we investigate how a search system can effectively predict the documents that a user would have been presented with had he continued the search session by submitting subsequent queries. To address the problem, we propose a model of contextual recommendation that seeks to capture the underlying semantics of information need transitions of a current user’s search context. This model leverages information from a number of past interactions of other users with similar interactions from an existing search log. To identify similar interactions, as a novel contribution, we propose an embedding approach that jointly learns representations of both individual query terms and also those of queries (in their entirety) from a search log data by leveraging session-level containment relationships. Our experiments conducted on a large query log, namely the AOL, demonstrate that using a joint embedding of queries and their terms within our proposed framework of document retrieval outperforms a number of text-only and sequence modeling based baselines.

Topic Difficulty: Collection and Query Formulation Effects

Several recent studies have explored the interaction effects between topics, systems, corpora, and components when measuring retrieval effectiveness. However, all of these previous studies assume that a topic or information need is represented by a single query. In reality, users routinely reformulate queries to satisfy an information need. In recent years, there has been renewed interest in the notion of “query variations” which are essentially multiple user formulations for an information need. Like many retrieval models, some queries are highly effective while others are not. This is often an artifact of the collection being searched which might be more or less sensitive to word choice. Users rarely have perfect knowledge about the underlying collection, and so finding queries that work is often a trial-and-error process. In this work, we explore the fundamental problem of system interaction effects between collections, ranking models, and queries. To answer this important question, we formalize the analysis using ANalysis Of VAriance (ANOVA) models to measure multiple components effects across collections and topics by nesting multiple query variations within each topic. Our findings show that query formulations have a comparable effect size of the topic factor itself, which is known to be the factor with the greatest effect size in prior ANOVA studies. Both topic and formulation have a substantially larger effect size than any other factor, including the ranking algorithms and, surprisingly, even query expansion. This finding reinforces the importance of further research in understanding the role of query rewriting in IR related tasks.

Pengaruh Struktur Modal, Likuiditas, Profitabilitas dan Umur Perusahaan terhadap Kualitas Laba:

This study aims to prove the effect of capital structure, liquidity, profitability and firm age on earnings quality. The type of research used is quantitative research and secondary data. The sample of this research is mining companies listed on the Indonesia Stock Exchange (IDX) in 2017-2020 using purposive sampling. This study shows that capital structure has no significant effect on earnings quality, liquidity has no significant effect on earnings quality, profitability has no significant effect on earnings quality and firm age has no significant effect on earnings quality. The implications of this research are related to earnings quality. Investors and other users of financial statement information, need to consider the liquidity factor because this factor has a significant impact on the quality of earnings in the company. This shows that users of financial statements, especially investors, need to consider the liquidity factor when making investment decisions in affiliated companies. Keywords: Capital Structure, Liquidity, Profitability, Company Age, Earnings Quality

PENGARUH MEDIA SOSIAL TIKTOK TERHADAP KEBUTUHAN INFORMASI SEKS EDUKASI PADA GENERASI Z

Sex education is still a topic that is considered taboo in Indonesia, through Tiktok which is a popular application in the world in 2020, it is used as a medium to meet this information need by the @tabu.id account. The purpose of this study was to determine the effect of intensity, media content and attractiveness of using social media on the TikTok @tabu.id account on the fulfillment of sex education information needs. This study uses a quantitative approach with an explanatory survey. The theory used is the Uses Effect Theory. The data collection technique used is a questionnaire or questionnaire and literature study. While the data analysis technique will be collected using a Likert scale. The results of this study are the intensity, message content and attractiveness affect the need for information about sex education. While the magnitude of the influence of sex education information is 6.75%, the magnitude of the influence of infographic messages on sex education information is 33.26% and the magnitude of the influence of sex education information is 15.02%.

Education and Information Need for Emergency Physicians About Rare Diseases in China

Background: Rare diseases are serious and chronic disease that affect no more than 1 person in 2000. The patients suffering from RD may come to emergency department for life-threatening symptoms, such as acute aortic dissection, intracranial hemorrhage, and severe respiratory distress. Diagnostic delay of rare disease patients is common and often caused by low rare disease awareness among physicians. The main aim of this study was to investigate the Chinese emergency physicians’ basic knowledge, information access and educational needs regarding rare diseases. An online questionnaire was completed by Chinese emergency physicians during January and March 2021. Methods and Results: A total of 539 emergency physicians responded to the questionnaire-based study, including 200 females and 339 males. More than half of respondents were from Tertiary A hospital and had engaged in medical clinical work more than 10 years. Only 4.27% of respondents correctly estimated the prevalence of rare diseases. A few respondents knew the exact number of RD in the first official list of rare diseases in 2018. 98.5% of respondents rated their knowledge about rare diseases as rare or insufficient. Most of emergency physicians preferred to getting information by search engine instead of specialized websites of rare diseases. Lack of practice guidelines or consensus and were considered as the most important reason for diagnostic delay of RD. Practice guidelines or consensus and professional websites on rare diseases were urgently needed for emergency physicians.Conclusion: The investigation shows poor knowledge of emergency physicians regarding rare diseases. Practice guidelines and professional websites on rare diseases were the prominently urgent needs for emergency physicians. Specialized RD courses should also be added in medical education.

A large scale study of reader interactions with images on Wikipedia

Wikipedia is the largest source of free encyclopedic knowledge and one of the most visited sites on the Web. To increase reader understanding of the article, Wikipedia editors add images within the text of the article’s body. However, despite their widespread usage on web platforms and the huge volume of visual content on Wikipedia, little is known about the importance of images in the context of free knowledge environments. To bridge this gap, we collect data about English Wikipedia reader interactions with images during one month and perform the first large-scale analysis of how interactions with images happen on Wikipedia. First, we quantify the overall engagement with images, finding that one in 29 pageviews results in a click on at least one image, one order of magnitude higher than interactions with other types of article content. Second, we study what factors associate with image engagement and observe that clicks on images occur more often in shorter articles and articles about visual arts or transports and biographies of less well-known people. Third, we look at interactions with Wikipedia article previews and find that images help support reader information need when navigating through the site, especially for more popular pages. The findings in this study deepen our understanding of the role of images for free knowledge and provide a guide for Wikipedia editors and web user communities to enrich the world’s largest source of encyclopedic knowledge.

Hybrid Query Expansion Model Based on Pseudo Relevance Feedback and Semantic Tree for Arabic IR

In this paper, the authors propose and readapt a new concept-based approach of query expansion in the context of Arabic information retrieval. The purpose is to represent the query by a set of weighted concepts in order to identify better the user's information need. Firstly, concepts are extracted from the initially retrieved documents by the Pseudo-Relevance Feedback method, and then they are integrated into a semantic weighted tree in order to detect more information contained in the related concepts connected by semantic relations to the primary concepts. The authors use the “Arabic WordNet” as a resource to extract, disambiguate concepts and build the semantic tree. Experimental results demonstrate that measure of MAP (Mean Average Precision) is about 10% of improvement using the open source Lucene as IR System on a collection formed from the Arabic BBC news.

PERILAKU PENCARIAN INFORMASI SISWA DALAM MODEL PEMBELAJARAN JARAK JAUH DI MASA PANDEMI COVID-19

The pandemic Covid-19 has impact most of human activities, one of them is education. The implementation of distance learning has influence for the parents, teachers, and students who are the main subjects in education, as though increased stress, less of competency development, or technical learning problems related to fulfill information needs. In case the problems abandoned, it will hinder the students-development, the development of sustainable education, and the progress of the nation. This research aims to determine the information behavior of students of SMAN 1 Cicalengka in overcome learning constraints during the pandemic Covid-19. This research uses a qualitative approach with descriptive methods. Data collection techniques is using triangulation data, through observation/surveys, interviews, and literature study. The population were all students of SMAN 1 Cicalengka with 5 informants. It uses the Ohotshi-Gottschalg-Duque information-behavior model as conceptual framework. The results showed that: First, students information needs were at the concious, visceral, and adapted levels; Second, information needs are mostly related with learning materials, motivation, health, hobbies, college even job vacancies; Third, students information behavior based on habits and intuitions, most of students do not understand the process of extracting because they do not recognize the domain of the information need. The implication that learning based on library is necessity at the level of educational unit that collaborates between teachers and school librarians.