An Optimization Analysis of the Subject Directory System on the Medlineplus Portal - An Investigation of Mental Health, Children, Teenagers, and Older Adults Related Health Topics

The Internet is a common means for people to search for health information. The subject directory of MedlinePlus offers Internet searchers a browsing environment so that those seekers could start from a broad term and refine their search terms to meet their real information needs, thus resulting in a better information search. For those novice users who are not familiar with relevant domain knowledge, MedlinePlus’s directory can be of great assistance and enable the portal to adopt to a more general population. Such a subject directory system and its involved health topics in the MedlinePlus portal formed a network where a specific research methodology, social network analysis, is applicable. In this study, four health topic groups – mental health, children, teenagers, and older adults - were selected as the focus for the investigation toward the subject directory on the MedlinePlus portal. This study applied social network analysis to explore the health topic directories and connection patterns among the health topics that comprised the subject directory of the MedlinePlus portal, and identified the influential topics (i.e., those health topics which play more important roles than others in connecting different topics) among the topic networks. As a result, different recommendations were made toward mental health, children, teenagers, and older adults related health topics, respectively. New optimized structural networks were suggested to be built for each of the four health topic subcategories according to the similarity values calculated through the cosine similarity measure in terms of the textual information contained in health topics’ Web pages, as well as the key nodes identified in the networks of health topics. Evaluations were later conducted to compare the original and optimized structural networks of the four health topic groups regarding their topics’ new similarity values. Newly identified influential health topics were verified to have improved the overall semantic connections among the whole networks. Last but not least, the recommendation results were evaluated by two health field experts and the evaluation outcomes proved that the recommendations suggested in this study were consistent with the opinions generated by health professionals. The findings of this research will provide suggestions to optimize and enhance the current navigation guidance system in MedlinePlus, improve the information searching effectiveness among the portal users, offer insights to public health portal creators, and support other researchers focusing on subject directory systems

A Physiologically Based System Theory of Consciousness

A system which uses large numbers of devices to perform a complex functionality is forced to adopt a simple functional architecture by the needs to construct copies of, repair, and modify the system. A simple functional architecture means that functionality is partitioned into relatively equal sized components on many levels of detail down to device level, a mapping exists between the different levels, and exchange of information between components is minimized. In the instruction architecture functionality is partitioned on every level into instructions, which exchange unambiguous system information and therefore output system commands. The von Neumann architecture is a special case of the instruction architecture in which instructions are coded as unambiguous system information. In the recommendation (or pattern extraction) architecture functionality is partitioned on every level into repetition elements, which can freely exchange ambiguous information and therefore output only system action recommendations which must compete for control of system behavior. Partitioning is optimized to the best tradeoff between even partitioning and minimum cost of distributing data. Natural pressures deriving from the need to construct copies under DNA control, recover from errors, failures and damage, and add new functionality derived from random mutations has resulted in biological brains being constrained to adopt the recommendation architecture. The resultant hierarchy of functional separations can be the basis for understanding psychological phenomena in terms of physiology. A theory of consciousness is described based on the recommendation architecture model for biological brains. Consciousness is defined at a high level in terms of sensory independent image sequences including self images with the role of extending the search of records of individual experience for behavioral guidance in complex social situations. Functional components of this definition of consciousness are developed, and it is demonstrated that these components can be translated through subcomponents to descriptions in terms of known and postulated physiological mechanisms

A Functional Architecture Approach to Neural Systems

The technology for the design of systems to perform extremely complex combinations of real-time functionality has developed over a long period. This technology is based on the use of a hardware architecture with a physical separation into memory and processing, and a software architecture which divides functionality into a disciplined hierarchy of software components which exchange unambiguous information. This technology experiences difficulty in design of systems to perform parallel processing, and extreme difficulty in design of systems which can heuristically change their own functionality. These limitations derive from the approach to information exchange between functional components. A design approach in which functional components can exchange ambiguous information leads to systems with the recommendation architecture which are less subject to these limitations. Biological brains have been constrained by natural pressures to adopt functional architectures with this different information exchange approach. Neural networks have not made a complete shift to use of ambiguous information, and do not address adequate management of context for ambiguous information exchange between modules. As a result such networks cannot be scaled to complex functionality. Simulations of systems with the recommendation architecture demonstrate the capability to heuristically organize to perform complex functionality

Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge Managemen

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information