Science Concierge: A fast content-based recommendation system for scientific publications

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.Comment: 12 pages, 5 figure

Conformative Filtering for Implicit Feedback Data

Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negative examples. Previous work tackles this problem by assuming that users are not interested or not as much interested in the unconsumed items. Those assumptions are often severely violated since non-consumption can be due to factors like unawareness or lack of resources. Therefore, non-consumption by a user does not always mean disinterest or irrelevance. In this paper, we propose a novel method called Conformative Filtering (CoF) to address the issue. The motivating observation is that if there is a large group of users who share the same taste and none of them have consumed an item before, then it is likely that the item is not of interest to the group. We perform multidimensional clustering on implicit feedback data using hierarchical latent tree analysis (HLTA) to identify user `tastes' groups and make recommendations for a user based on her memberships in the groups and on the past behavior of the groups. Experiments on two real-world datasets from different domains show that CoF has superior performance compared to several common baselines

Factor validation and Rasch analysis of the individual recovery outcomes counter

Objective: The Individual Recovery Outcomes Counter is a 12-item personal recovery self assessment tool for adults with mental health problems. Although widely used across Scotland, limited research into its psychometric properties has been conducted. We tested its' measurement properties to ascertain the suitability of the tool for continued use in its present form.Materials and methods: Anonymised data from the assessments of 1,743 adults using mental health services in Scotland were subject to tests based on principles of Rasch measurement theory, principal components analysis and confirmatory factor analysis.Results: Rasch analysis revealed that the 6-point response structure of the Individual Recovery Outcomes Counter was problematic. Re-scoring on a 4-point scale revealed well ordered items that measure a single, recovery-related construct, and has acceptable fit statistics. Confirmatory factor analysis supported this. Scale items covered around 75% of the recovery continuum; those individuals least far along the continuum were least well addressed.Conclusions: A modified tool worked well for many, but not all, service users. The study suggests specific developments are required if the Individual Recovery Outcomes Counter is to maximise its' utility for service users and provide meaningful data for service providers.*Implications for Rehabilitation*Agencies and services working with people with mental health problems aim to help them with their recovery.*The individual recovery outcomes counter has been developed and is used widely in Scotland to help service users track their progress to recovery.*Using a large sample of routinely collected data we have demonstrated that a number of modifications are needed if the tool is to adequately measure recovery.*This will involve consideration of the scoring system, item content and inclusion, and theoretical basis of the tool