Using Search Logs to Recommend Images to New Users

This article discusses a multiple search session model of end-users' interaction with information retrieval systems based on results from an exploratory study investigating end-users' search sessions over time with online public access catalogs (OPAC) or CD-ROM databases at different stages of their information seeking related to a current research project. Interviews were conducted with 200 academic end-users to investigate the occurrence of multiple search sessions. Results show that at the time of the interview, 57% of end-users had conducted multiple search sessions during their research project and 86% of end-users conducted their first search session at the beginning stage of their information-seeking process. Forty-nine percent of end-users had conducted between 1 and 6 search sessions and 8% more than 6 search sessions. Seventy percent of multiple search session end-users' had modified their search terms since their first search session. The implications of the findings for end-user training, information retrieval systems design, and further research are discussed. © 1996 John Wiley & Sons, Inc.

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.

The recent increase in the volume and variety of video content available online presents growing challenges for video search. Users face increased difficulty in formulating effective queries and search engines must deploy highly effective algorithms to provide relevant results. Although lately much effort has been invested in optimizing video search engine results, relatively little attention has been given to predicting for which queries results optimization is most useful, i.e., predicting which queries will fail. Being able to predict when a video search query would fail is likely to make the video search result optimization more efficient and effective, improve the search experience for the user by providing support in the query formulation process and in this way boost the development of video search engines in general. While insight about a query's performance in general could be obtained using the well-known concept of query performance prediction (QPP), we propose a novel approach for predicting a failure of a video search query in the specific context of a search session. Our 'context-aware query failure' prediction approach uses a combination of 'user indicators' and 'engine indicators' to predict whether a particular query is likely to fail in the context of a particular search session. User indicators are derived from the search log and capture the patterns of query (re)formulation behavior and the click-through data of a user during a typical video search session. Engine indicators are derived from the video search results list and capture the visual variance of search results that would be offered to the user for the given query. We validate our approach experimentally on a test set containing 1+ million video search queries and show its effectiveness compared to a set of conventional QPP baselines. Our approach achieves a 13% relative improvement over the baseline.

Googling for Health Information

The increased availability of large amounts of data about user search behaviour in search engines has triggered a lot of research in recent years. This includes developing machine learning methods to build knowledge structures that could be exploited for a number of tasks such as query recommendation. Query flow graphs are a successful example of these structures, they are generated from the sequence of queries typed in by a user in a search session. In this paper we propose to modify the query flow graph by incorporating clickthrough information from the search logs. Click information provides evidence of the success or failure of the search journey and therefore can be used to enrich the query flow graph to make it more accurate and useful for query recommendation. We propose a method of adjusting the weights on the edges of the query flow graph by incorporating the number of clicked documents after submitting a query.We explore a number of weighting functions for the graph edges using click information. Applying an automated evaluation framework to assess query recommendations allows us to perform automatic and reproducible evaluation experiments. We demonstrate how our modified query flow graph outperforms the standard query flow graph. The experiments are conducted on the search logs of an academic organisation’s search engine and validated in a second experiment on the log files of another Web site.KeywordsSearch Log AnalysisQuery SuggestionsAutomatic Evaluation

Session search is an Information Retrieval (IR) task which handles a series of queries issued for a search task. In this paper, we propose a novel reinforcement learning style information retrieval framework and develop a new feedback learning algorithm to model user feedback, including clicks and query reformulations, as reinforcement signals and to generate rewards in the RL framework. From a new perspective, we view session search as a cooperative game played between two agents, the user and the search engine. We study the communications between the two agents; they always exchange opinions on whether the current stage of search is relevant and whether we should explore now. The algorithm infers user feedback models by an EM algorithm from the query logs. We compare to several state-of-the-art session search algorithms and evaluate our algorithm on the most recent TREC 2012 to 2014 Session Tracks. The experimental results demonstrates that our approach is highly effective for improving session search accuracy.

Collaborative Web Search (CWS) is a technique used to re-rank the results of Web search engines to reflect the collective preferences of a community of online searchers. It applies a case-based reasoning perspective to Web search. In simple terms, past search sessions (queries and result selections) are stored as search cases and reused in response to similar queries; previously selected results, which have been regularly selected for similar queries in the past, are promoted in response to the new query. One of the limitations of CWS is that it only facilitates the promotion of previously selected results. In this paper we propose a solution by adopting a different type of case representation in which a search session is represented by a relevance model (e.g., a decision tree) learned from the selections made during the session. Each new target query results in the retrieval of a set of similar search cases and their component decision trees are dynamically combined to produce an ensemble classifier that is then used to re-rank the result-list to promote community-relevant results. We present the results of an evaluation based on live-user searching histories and show that this ensemble-based approach can outperform a standard CWS system.

Log analysis in Web search showed that user sessions often contain several\ndifferent topics. This means sessions need to be segmented into parts which\nhandle the same topic in order to give appropriate user support based on the\ntopic, and not on a mixture of topics. Different methods have been proposed to\nsegment a user session to different topics based on timeouts, lexical analysis,\nquery similarity or external knowledge sources. In this paper, we study the\nproblem in a digital library for the social sciences. We present a method based\non a thesaurus and a classification system which are typical knowledge\norganization systems in digital libraries. Five experts evaluated our approach\nand rated it as good for the segmentation of search sessions into parts that\ntreat the same topic.\n

Satisfaction prediction is one of the prime concerns in search performance evaluation. It is a non-trivial task for two major reasons: (1) The definition of satisfaction is rather subjective and different users may have different opinions in satisfaction judgement. (2) Most existing studies on satisfaction prediction mainly rely on users' click-through or query reformulation behaviors but there are many sessions without such kind of interactions. To shed light on these research questions, we construct an experimental search engine that could collect users' satisfaction feedback as well as mouse click-through/movement data. Different from existing studies, we compare for the first time search users' and external assessors' opinions on satisfaction. We find that search users pay more attention to the utility of results while external assessors emphasize on the efforts spent in search sessions. Inspired by recent studies in predicting result relevance based on mouse movement patterns (namely motifs), we propose to estimate the utilities of search results and the efforts in search sessions with motifs extracted from mouse movement data on search result pages (SERPs). Besides the existing frequency-based motif selection method, two novel selection strategies (distance-based and distribution-based) are also adopted to extract high quality motifs for satisfaction prediction. Experimental results on over 1,000 user sessions show that the proposed strategies outperform existing methods and also have promising generalization capability for different users and queries.

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase‐level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two‐thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self‐formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2‐second response times at the highest loads tested).

Visual data exploration assists users in investigating data by providing recommendations. These recommendations take the form of queries that retrieve these data for the next exploration step, paired with suited visualizations. This paper extends the content-based recommendation techniques adopted so far in EVLIN for query recommendations with collaborative-filtering recommendation techniques. For that, we propose a merge approach that fuses evolution provenance graphs representative of individual user’s exploration session into a global multi-user graph. This merged graph is then used by our collaborative recommendation approach that searches similar queries for a given user query in the multi-user graph to then recommend queries that were previously explored in exploration steps adjacent to these similar queries.

Understanding user intent from her sequential search behaviors, i.e. predicting the intent of each user query in a search session, is crucial for modern Web search engines. However, due to the huge number of user behavior variables and coarse level intent labels defined by human editors, it is very difficult to directly model user behavioral dynamics or user intent dynamics in user search sessions. In this paper, we propose a novel Sparse Hidden-Dynamic Conditional Random Fields (SHDCRF) model for user intent learning from their search sessions. Through incorporating the proposed hidden state variables, SHDCRF aims to learn a substructure, i.e. a set of related hidden variables, for each intent label and they are used to model the intermediate dynamics between user intent labels and user behavioral variables. In addition, SHDCRF learns a sparse relation between the hidden variables and intent labels to make the hidden state variables explainable. Extensive experiment results, on real user search sessions from a popular commercial search engine show that the proposed SHDCRF model significantly outperforms in terms of intent prediction results that those classical solutions such as Support Vector Machine (SVM), Conditional Random Field (CRF) and Latnet-Dynamic Conditional Random Field (LDCRF).

This paper proposes an automated question answering system based on improved Term Frequency- Inverse Document Frequency (TF-IDF) and cosine similarity. The main purpose of this research study is to provide an effective question answering system that retrieves precise and relevant answers to the users' queries with high confidence. The existing studies in the relevant literature show that several techniques have been proposed for automatic question answering systems. The rule-based techniques depend on inference rules and take less time to respond to the user query. However, generations of pattern and inference rules are difficult as natural languages lack to follow a fixed pattern. The content-based similarity method pre-computes similarity with all the repository questions for a given query. In this research study, firstly all repository questions are pre-processed and a matrix using the improved TF -IDF model is generated. Then, we find the similarity of each user query with the matrix after query pre-processing. For the proposed approach, we remove stop-words and apply lemmatization and POS tagging techniques for pre-processing. The proposed framework is implemented using the standard datasets used in the existing studies. The empirical analysis-based results show that the systems adopting the proposed technique takes less than five seconds to respond to user queries with maximum similarity. The proposed framework attains up to 84% accuracy.

The setting in which similarity querying of metric data is outsourced to a service provider. Users query the server for the most similar data objects and data is revealed only to trusted users and not to anyone else. The need for privacy may be due to the data being sensitive (eg. in medicine), valuable (eg. in astronomy) or otherwise confidential. In this work, image retrieval on metric data of outsourced lung images using parallelism from various sources like hospitals, scan centers and public database available in internet are handled. The proposed similarity search for content based image retrieval involves dynamic similarity querying on metric data from segmented and extracted texture features database. With real data, the technique is capable of offering privacy while enabling efficient and accurate processing of similarity queries.

Correcting queries for XML