Adaptive image retrieval using a graph model for semantic feature integration

The variety of features available to represent multimedia data constitutes a rich pool of information. However, the plethora of data poses a challenge in terms of feature selection and integration for effective retrieval. Moreover, to further improve effectiveness, the retrieval model should ideally incorporate context-dependent feature representations to allow for retrieval on a higher semantic level. In this paper we present a retrieval model and learning framework for the purpose of interactive information retrieval. We describe how semantic relations between multimedia objects based on user interaction can be learnt and then integrated with visual and textual features into a unified framework. The framework models both feature similarities and semantic relations in a single graph. Querying in this model is implemented using the theory of random walks. In addition, we present ideas to implement short-term learning from relevance feedback. Systematic experimental results validate the effectiveness of the proposed approach for image retrieval. However, the model is not restricted to the image domain and could easily be employed for retrieving multimedia data (and even a combination of different domains, eg images, audio and text documents)

The application of user log for online business environment using content-based Image retrieval system

Over the past few years, inter-query learning has gained much attention in the research and development of content-based image retrieval (CBIR) systems. This is largely due to the capability of inter-query approach to enable learning from the retrieval patterns of previous query sessions. However, much of the research works in this field have been focusing on analyzing image retrieval patterns stored in the database. This is not suitable for a dynamic environment such as the World Wide Web (WWW) where images are constantly added or removed. A better alternative is to use an image's visual features to capture the knowledge gained from the previous query sessions. Based on the previous work (Chung et al., 2006), the aim of this paper is to propose a framework of inter-query learning for the WWW-CBIR systems. Such framework can be extremely useful for those online companies whose core business involves providing multimedia content-based services and products to their customers

Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)

What if Information Retrieval (IR) systems did not just retrieve relevant information that is stored in their indices, but could also "understand" it and synthesise it into a single document? We present a preliminary study that makes a first step towards answering this question. Given a query, we train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, July 21, 2016, Pisa, Ital

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

The effect of dyslexia on information retrieval: A pilot study

Purpose – The purpose of the paper is to resolve a gap in our knowledge of how people with dyslexia interact with Information Retrieval (IR) systems, specifically an understanding of their information searching behaviour. Very little research has been undertaken with this particular user group, and given the size of the group (an estimated 10% of the population) this lack of knowledge needs to be addressed. Design/Methodology/Approach - We use elements of the dyslexia cognitive profile to design a logging system recording the difference between two sets of participants: dyslexic and control users. We use a standard Okapi interface together with two standard TREC topics in order to record the information searching behaviour of these users. We gather evidence from various sources, including quantitative information on search logs, together with qualitative information from interviews and questionnaires. We record variables on queries, documents, relevance assessments and sessions in the search logs. We use this evidence to examine the difference in searching between the two sets of users, in order to understand the effect of dyslexia on the information searching behaviour. A topic analysis is also conducted on the quantitative data to show any effect on the results from the information need. Research limitations/implications – As this is a pilot study, only 10 participants were recruited for the study, 5 for each user group. Due to ethical issues, the number of topics per search was restricted to one topic only. The study shows that the methodology applied is useful for distinguishing between the two user groups, taking into account differences between topic. We outline further research on the back of this pilot study in four main areas. A different approach from the proposed methodology is needed to measure the effect on query variables, which takes account of topic variation. More details on users are needed such as reading abilities, speed of language processing and working memory to distinguish the user groups. Effect of topic on search interaction must be measured in order to record the potential impact on the dyslexic user group. Work is needed on relevance assessment and effect on precision and recall for users who may not read many documents. Findings – Using the log data, we establish the differences in information searching behaviour of control and dyslexic users i.e. in the way the two groups interact with Okapi, and that qualitative information collected (such as experience etc) may not be able to account for these differences. Evidence from query variables was unable to distinguish between groups, but differences on topic for the same variables were recorded. Users who view more documents tended to judge more documents as being relevant, either in terms of the user group or topic. Session data indicated that there may be an important difference between the number of iterations used in a search between the user groups, as there may be little effect from the topic on this variable. Originality/Value – This is the first study of the effect of dyslexia on information search behaviour, and provides some evidence to take the field forward