In this study, we evaluate the effect of model merging in ad-hoc retrieval tasks. Model merging is a technique that combines the diverse characteristics of multiple models. We hypothesized that applying model merging to domain-specific ad-hoc retrieval tasks could improve retrieval effectiveness. To verify this hypothesis, we merged the weights of a source retrieval model and a domain-specific (non-retrieval) model using a linear interpolation approach. A key advantage of our approach is that it requires no additional fine-tuning of the models. We conducted two experiments each in the medical and Japanese domains. The first compared the merged model with the source retrieval model, and the second compared it with a LoRA fine-tuned model under both full and limited data settings for model construction. The experimental results indicate that model merging has the potential to produce more effective domain-specific retrieval models than the source retrieval model, and may serve as a practical alternative to LoRA fine-tuning, particularly when only a limited amount of data is available.

In the modern era of digitization, information retrieval has become an indispensable part of daily life. In the future, with further advances in digital technology, information retrieval is expected to become even more diverse and complex. Therefore, we propose an information organization method using a whiteboard and sticky notes in combination with Mixed Reality (MR) devices. We use this method to organize virtual objects displaying information about searched products on a real whiteboard and decide on the items to purchase. With this proposed approach, users can interact with search results in a spatial manner, affixing them to the whiteboard as sticky notes and adding handwritten notes to virtual objects, offering a more intuitive and efficient way to decide on the purchase of products.

Deep hashing has been widely used to approximate nearest-neighbor search for image retrieval tasks. Most of them are trained with image-label pairs without any inter-label relationship, which may not make full use of the real-world data. This paper presents deep hashing, named HA2SH, that leverages multiple types of labels with hierarchical structures that an ethnological museum assigns to their artifacts. We experimentally prove that HA2SH can learn to generate hashes that give a better retrieval performance. Our code is available at https://github.com/wbw520/minpaku.

This paper presents findings from an empirical study of multileaved comparisons, an efficient online evaluation methodology, in a commercial Web service. The most important difference from the previous studies is the number of rankers involved in the online evaluation: we compared 30 rankers for around 90 days by multileaved comparisons. A relatively large number of rankers answered several questions that could not be addressed in the previous work due to a small number of rankers: How much ranking difference is required for rankers to be statistically distinguished? How many impressions are necessary for finding statistically significant differences for correlated rankers? How large difference in offline evaluation can predict significant differences in a multileaved comparison? We answer these questions with the results of the multileaved comparisons, and generalized some of the findings by simulation-based experiments.

To obtain accurate information through web searches, people have to search for information carefully. This study investigates how the search behaviors and decision outcomes of searchers were affected by the documents they encountered during their search process. We focus on two document factors: (1) opinion (consistent and inconsistent) with the searchers' beliefs prior to the search task, and (2) credibility (high and low). We conducted a user study in which 260 participants were asked to perform health-related search tasks while controlling a search result with different opinions and credibility levels. The results revealed that (i) the participants spent more effort searching by issuing more queries, when belief-inconsistent documents were presented; (ii) the documents' opinion and credibility affected their belief dynamics, (i.e., how their beliefs changed after the search task); and (iii) their belief dynamics and search efforts had few relationships. These findings suggest that search engines could prevent users from polarization and thus, help them to obtain accurate information, by presenting documents that are inconsistent with users' beliefs on the higher-rank of the results.

In this study, we developed an Internet of Things (IoT) monitoring device to monitor over the people inside a room. We collected sensor data at a specific location using the device. Based on the data, we tried to predict the behavior of the person at that location. Monitoring and predicting human daily behavior is trivial task. Most of the research on monitoring and predicting daily life behavior are based on the data available from smart home [7] [16] [17]. But smart home is expensive compare to normal home, as different kind of sensor are attached in the room and have more facilities. So, we developed a low cost IoT monitoring device and predict the daily life behavior of human from the sensor data taken from the device. We can extract information from the daily life behavior and share it with the family living in distant places.

We propose query priming to activate careful user information seeking in web searches. Query priming employs query auto-completion (QAC) and query suggestion (QS) to present search terms that stimulate critical thinking and encourages careful information seeking and decision making. We conducted an online user study using a crowdsourcing service. Analysis of search behavior logs and questionnaire responses confirmed the following. (1) With query priming, participants issued more queries and (re-)visited search engine result pages more frequently. (2) Query priming promoted webpage selection targeted at evidence-based decision making. (3) The query priming effect varied relative to participant educational background. This study contributes to search interaction design to enhance user engagement in critical thinking in web searches.

This paper discusses challenges of an online evaluation technique, multileaved comparison, based on the analysis of evaluation results in a community question-answering (cQA) search service. NTCIR-13 OpenLiveQ task offered a shared task in which participants addressed an ad-hoc retrieval task in a cQA service, and evaluated their rankers by multileaved comparison, which combines multiple rankings to generate a single search result page, and simultaneously evaluates the different rankings based on users' clicks on the search result page. Since the number of search result impressions during the evaluation period might not suffice to evaluate a hundred of rankers, we conducted the online evaluation only for rankers that achieved high performance in offline evaluation. The analysis of evaluation results showed that offline and online evaluation results did not fully agree, and a large number of users' clicks were necessary to find a statistically significant difference for every ranker pair. To cope with these problems in large-scale multileaved comparison, we propose a new experimental design that evaluates all the rankers online but intensively tests only the top-k rankers. Simulation-based experiments demonstrated that Copeland counting algorithm could achieve high top-k recall in the top-k identification problem for multileaved comparison.

This study investigates how people carefully search for the Web to obtain credible and accurate information. The goal of this study is to better understand people's attitudes toward careful information seeking via Web search, and the relationship between such attitudes and their daily search behaviors. To this end, we conducted two experiments. We first administrated an online questionnaire to investigate how people's attitudes toward using the strategies for verifying information in the Web search process differ based on various factors such as their credulity toward Web information, individual thinking styles, educational background, and search expertise. We then analyzed their one-year and one-month query logs of a commercial Web search engine to explore how their daily search behaviors are different according to their attitudes. The analysis of the questionnaire and the query logs obtained from ¥subjects participants revealed that (i) the people's attitudes toward using the verification strategies in Web search are positively correlated to their Need for Cognition (NFC), educational background, and search expertise; (ii) people with strong attitudes are likely to click lower-ranked search results than those with intermediate levels of attitude; (iii) people with strong attitudes are more likely to use the terms such as "evidence'' or "truth'' in their queries, possibly to scrutinize the uncertain or incredible information; and (iv) the behavioral differences found in (ii) and (iii) are not identified from the differences in the participants' educational backgrounds. These findings help us explore future directions for a new Web search system that encourages people to be more careful in Web search, and suggest the need for an educational program or training to facilitate the attitudes and skills for using Web search engines to obtain accurate information.

We present one of the world's first attempts to examine the feasibility of multileaving evaluation of document rankings on a large scale commercial community Question Answering (cQA) service. As a natural enhancement of interleaving evaluation, multileaving merges more than two input rankings into one and measures the search user satisfaction of each input ranking on the basis of user clicks on the multileaved ranking. We evaluated the adequateness of two major multileaving methods, team draft multileaving (TDM) and optimized multileaving (OM), proposing their practical implementation for live services. Our experimental results demonstrated that multileaving methods could precisely evaluate the effectiveness of five rankings with different quality by using clicks from real users. Moreover, we concluded that OM is more efficient than TDM by observing that most of the evaluation results with OM converged after showing multileaved rankings around 40,000 times and an in-depth analysis of their characteristics.

This study proposes a method for retrieving and ranking posts from social network services(SNSs) by specifying and providing feedback on the context of posts. Current search systems for SNS posts cannot handle user intent with regard to the context of posts to be retrieved, mainly owing to the incompleteness of SNS posts, i.e., they do not contain the users' contexts (e.g., situations or preferences) of users posting messages. Hence, we propose a search method that accepts two kinds of queries, namely, content queries and context queries, and that updates these queries based on the user feedback with special attention to the contexts of posts. Our search method considers the whole SNS dataset as a graph and the nodes surrounding each post as its context; to find relevant posts in terms of content and context, our method propagates user feedback via this graph. Our experimental results based on a Twitter test collection revealed that our proposed method showed improved retrieval performance as compared with conventional SNS retrieval and relevance feedback. In addition, we could detect the optimal parameters for feedback propagating.

Web searchers often use a Web search engine to find a way or means to achieve his/her goal. For example, a user intending to solve his/her sleeping problem, the query "sleeping pills" may be used. However, there may be another solution to achieve the same goal, such as "have a cup of hot milk" or "stroll before bedtime." The problem is that the user may not be aware that these solutions exist. Thus, he/she will probably choose to take a sleeping pill without considering these solutions. In this study, we define and tackle the alternative action mining problem. In particular, we attempt to develop a method for mining alternative actions for a given query. We define alternative actions as actions which share the same goal and define the alternative action mining problem as similar in the search result diversification. To tackle the problem, we propose leveraging a community Q&A (cQA) corpus for mining alternative actions. We propose a method to compute how well two actions can be alternative actions by using a question-answer structure in a cQA corpus. Our method builds a question-action bipartite graph and recursively computes how well two actions can be alternative actions. We conducted experiments to investigate the effectiveness of our method using two newly built test collections, each containing 50 queries. The experimental results indicated that our proposed method outperformed the query suggestion methods provided by the commercial search engines in terms of D#-nDCG.

For intrinsically diverse tasks, in which collecting extensive information from different aspects of a topic is required, searchers often have difficulty formulating queries to explore diverse aspects and deciding when to stop searching. With the goal of helping searchers discover unexplored aspects and find the appropriate timing for search stopping in intrinsically diverse tasks, we propose ScentBar, a query suggestion interface visualizing the amount of important information that a user potentially misses collecting from the search results of individual queries. We define the amount of missed information for a query as the additional gain that can be obtained from unclicked search results of the query, where gain is formalized as a set-wise metric based on aspect importance, aspect novelty, and per-aspect document relevance and is estimated by using a state-of-the-art algorithm for subtopic mining and search result diversification. Results of a user study involving 24 participants showed that the proposed interface had the following advantages when the gain estimation algorithm worked reasonably: (1) ScentBar users stopped examining search results after collecting a greater amount of relevant information; (2) they issued queries whose search results contained more missed information; (3) they obtained higher gain, particularly at the late stage of their sessions; and (4) they obtained higher gain per unit time. These results suggest that the simple query visualization helps make the search process of intrinsically diverse tasks more efficient, unless inaccurate estimates of missed information are visualized.

We propose a method to generate effective query suggestions aiming to help struggling search, where users experience difficulty in locating information that is relevant to their information need in the search session. The core is identifying struggling component of an on-going struggling session and mining the effective representations of it. The struggling component is the semantic component of information need for which the user struggled to find an effective representation during the struggling session. The proposed method identifies the struggling component of given on-going struggling session and mines the sessions containing the identified struggling component from a query log to build a struggling flow graph. The struggling flow graph records users' reformulation behaviors for the terms of the struggling component, through struggling flow graph we can mine effective representations of the struggling component. The experimental results demonstrate that the proposed method outperforms the baseline methods when it can use two or more queries in a struggling session.

This study investigated query formulations by users with {\it Cognitive Search Intents} (CSIs), which are users' needs for the cognitive characteristics of documents to be retrieved, {\em e.g. comprehensibility, subjectivity, and concreteness. Our four main contributions are summarized as follows (i) we proposed an example-based method of specifying search intents to observe query formulations by users without biasing them by presenting a verbalized task description;(ii) we conducted a questionnaire-based user study and found that about half our subjects did not input any keywords representing CSIs, even though they were conscious of CSIs;(iii) our user study also revealed that over 50\% of subjects occasionally had experiences with searches with CSIs while our evaluations demonstrated that the performance of a current Web search engine was much lower when we not only considered users' topical search intents but also CSIs; and (iv) we demonstrated that a machine-learning-based query expansion could improve the performances for some types of CSIs.Our findings suggest users over-adapt to current Web search engines,and create opportunities to estimate CSIs with non-verbal user input.

Building test collections based on nuggets is useful evaluating systems that return documents, answers, or summaries. However, nugget construction requires a lot of manual work and is not feasible for large query sets. Towards an efficient and scalable nugget-based evaluation, we study the applicability of semi-automatic nugget extraction in the context of the ongoing NTCIR One Click Access (1CLICK) task. We compare manually-extracted and semi-automatically-extracted Japanese nuggets to demonstrate the coverage and efficiency of the semi-automatic nugget extraction. Our findings suggest that the manual nugget extraction can be replaced with a direct adaptation of the English semi-automatic nugget extraction system, especially for queries for which the user desires broad answers from free-form text.

The One Click Access Task (1CLICK) of NTCIR requires systems to return a concise multi-document summary of web pages in response to a query which is assumed to have been submitted in a mobile context. Systems are evaluated based on information units (or iUnits), and are required to present important pieces of information first and to minimise the amount of text the user has to read. Using the official Japanese results of the second round of the 1CLICK task from NTCIR-10, we discuss our task setting and evaluation framework. Our analyses show that: (1) Simple baseline methods that leverage search engine snippets or Wikipedia are effective for 'lookup' type queries but not necessarily for other query types; (2) There is still a substantial gap between manual and automatic runs; and (3) Our evaluation metrics are relatively robust to the incompleteness of iUnits.

The NTCIR INTENT task comprises two subtasks: {\em Subtopic Mining}, where systems are required to return a ranked list of {\em subtopic strings} for each given query; and {\em Document Ranking}, where systems are required to return a diversified web search result for each given query. This paper summarises the novel features of the Second INTENT task at NTCIR-10 and its main findings, and poses some questions for future diversified search evaluation.

This short paper proposes a method to classify music video clips uploaded to a video sharing service into music mood categories such as 'cheerful,' 'wistful,' and 'aggressive.' The method leverages viewer comments posted to the music video clips for the music mood classification. It extracts specific features from the comments: (1) adjectives in comments, (2) lengthened words in comments, and (3) comments in chorus sections. Our experimental results classifying 695 video clips into six mood categories showed that our method outperformed the baseline in terms of macro and micro averaged F-measures. In addition, our method outperformed the existing approaches that utilize lyrics and audio signals of songs.

This paper tackles the problem of mining subgoals of a given search goal from data. For example, when a searcher wants to travel to London, she may need to accomplish several subtasks such as "book flights," "book a hotel," "find good restaurants" and "decide which sightseeing spots to visit." As another example, if a searcher wants to lose weight, there may exist several alternative solutions such as "do physical exercise," "take diet pills," and "control calorie intake." In this paper, we refer to such subtasks or solutions as subgoals, and propose to utilize sponsored search data for finding subgoals of a given query by means of query clustering. Advertisements (ads) reflect advertisers' tremendous efforts in trying to match a given query with implicit user needs. Moreover, ads are usually associated with a particular action or transaction. We therefore hypothesized that they are useful for subgoal mining. To our knowledge, our work is the first to use sponsored search data for this purpose. Our experimental results show that sponsored search data is a good resource for obtaining related queries and for identifying subgoals via query clustering. In particular, our method that combines ad impressions from sponsored search data and query co-occurrences from session data outperforms a state-of-the-art query clustering method that relies on document clicks rather than ad impressions in terms of purity, NMI, Rand Index, F1-measure and subgoal recall.

A diversified search result for an underspecified query generally contains web pages in which there are answers that are relevant to different aspects of the query. In order to help the user locate such relevant answers, we propose a simple extension to the standard Search Engine Result Page (SERP) interface, called AspecTiles. In addition to presenting a ranked list of URLs with their titles and snippets, AspecTiles visualizes the relevance degree of a document to each aspect by means of colored squares ("tiles"). To compare AspecTiles with the standard SERP interface in terms of usefulness, we conducted a user study involving 30 search tasks designed based on the TREC web diversity task topics as well as 32 participants. Our results show that AspecTiles has some advantages in terms of search performance, user behavior, and user satisfaction. First, AspecTiles enables the user to gather relevant information significantly more efficiently than the standard SERP interface for tasks where the user considers several different aspects of the query to be important at the same time (multi-aspect tasks). Second, AspecTiles affects the user's information seeking behavior: with this interface, we observed significantly fewer query reformulations, shorter queries and deeper examinations of ranked lists in multi-aspect tasks. Third, participants of our user study found AspecTiles significantly more useful for finding relevant information and easy to use than the standard SERP interface. These results suggest that simple interfaces like AspecTiles can enhance the search performance and search experience of the user when their queries are underspecified.

This paper proposes a system called "RerankEverything", which enables users to rerank search results in any search service, such as a Web search engine, an e-commerce site, a hotel reservation site, and so on. This system helps users explore diverse search results. In conventional search services, interactions between users and systems are quite limited and complicated. By using RerankEverything, users can interactively explore search results in accordance with their interests by reranking search results from various viewpoints. Experimental results show that our system potentially help users search more proactively. When using our system, users were more likely to click search results that were initially low ranked. Users also browsed through more diverse search results by reranking search results after giving various types of feedback with our system.

In this paper, we propose a method for helping users explore information via Web searches by using a question and answer (Q&A) corpus archived in a community Q&A site. When users do not have clear information needs and have little knowledge about the task domain, it is difficult for them to create queries that adequately reflect their information needs. We focused on terms like "famous temples," "historical townscapes," and "delicious sweets," which we call "adjective facets", and developed a method of extracting these facets from question and answer archives at a community Q&A site. We evaluated the effectiveness of our adjective facets by comparing them with several baselines.

This paper proposes a system called RerankEverything, which enables users to rerank search results in any search service, such as a Web search engine, an e-commerce site, a hotel reservation site and so on. In conventional search services, interactions between users and services are quite limited and complicated. In addition, search functions and interactions to refine search results differ depending on the services. By using RerankEverything, users can interactively explore search results in accordance with their interests by reranking search results from various viewpoints.