Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history. The lack of historical ratings results in the user and the item cold-start problems. The latter is the main focus of this work. Most of the current literature addresses this problem by integrating content-based recommendation techniques to model the new item. However, in many cases such content is not available, and the question arises is whether this problem can be mitigated using CF techniques only. We formalize this problem as an optimization problem: given a new item, a pool of available users, and a budget constraint, select which users to assign with the task of rating the new item in order to minimize the prediction error of our model. We show that the objective function is monotone-supermodular, and propose efficient optimal design based algorithms that attain an approximation to its optimum. Our findings are verified by an empirical study using the Netflix dataset, where the proposed algorithms outperform several baselines for the problem at hand.Comment: 11 pages, 2 figure

A Distributed Method for Trust-Aware Recommendation in Social Networks

This paper contains the details of a distributed trust-aware recommendation system. Trust-base recommenders have received a lot of attention recently. The main aim of trust-based recommendation is to deal the problems in traditional Collaborative Filtering recommenders. These problems include cold start users, vulnerability to attacks, etc.. Our proposed method is a distributed approach and can be easily deployed on social networks or real life networks such as sensor networks or peer to peer networks

RACOFI: A Rule-Applying Collaborative Filtering System

In this paper we give an overview of the RACOFI (Rule-Applying Collaborative Filtering) multidimensional rating system and its related technologies. This will be exemplified with RACOFI Music, an implemented collaboration agent that assists on-line users in the rating and recommendation of audio (Learning) Objects. It lets users rate contemporary Canadian music in the five dimensions of impression, lyrics, music, originality, and production. The collaborative filtering algorithms STI Pearson, STIN2, and the Per Item Average algorithms are then employed together with RuleML-based rules to recommend music objects that best match user queries. RACOFI has been on-line since August 2003 at http://racofi.elg.ca.

A comparison of homonym meaning frequency estimates derived from movie and television subtitles, free association, and explicit ratings

First Online: 10 September 2018Most words are ambiguous, with interpretation dependent on context. Advancing theories of ambiguity resolution is important for any general theory of language processing, and for resolving inconsistencies in observed ambiguity effects across experimental tasks. Focusing on homonyms (words such as bank with unrelated meanings EDGE OF A RIVER vs. FINANCIAL INSTITUTION), the present work advances theories and methods for estimating the relative frequency of their meanings, a factor that shapes observed ambiguity effects. We develop a new method for estimating meaning frequency based on the meaning of a homonym evoked in lines of movie and television subtitles according to human raters. We also replicate and extend a measure of meaning frequency derived from the classification of free associates. We evaluate the internal consistency of these measures, compare them to published estimates based on explicit ratings of each meaning’s frequency, and compare each set of norms in predicting performance in lexical and semantic decision mega-studies. All measures have high internal consistency and show agreement, but each is also associated with unique variance, which may be explained by integrating cognitive theories of memory with the demands of different experimental methodologies. To derive frequency estimates, we collected manual classifications of 533 homonyms over 50,000 lines of subtitles, and of 357 homonyms across over 5000 homonym–associate pairs. This database—publicly available at: www.blairarmstrong.net/homonymnorms/—constitutes a novel resource for computational cognitive modeling and computational linguistics, and we offer suggestions around good practices for its use in training and testing models on labeled data

Performance Evaluation Trait Validation

NPS NRP Executive SummaryA valid and credible performance evaluation system is critical for identifying and managing talent in the US Navy. Yet, while multiple performance trait item banks have been developed to rate promotion candidates based on the quality of their candidacy, a systematic study of the validity of such trait items has not been conducted. The objective of this research is to assess the construct validity of draft performance trait/value statements recently developed to assist with promotion board decisions. This study aims to provide N1 with a set of unbiased, validated performance trait/value statements for performance evaluation efforts across the Navy, offering leadership an informed perspective on the potential benefits and risks associated these new metrics.N1 - Manpower, Personnel, Training & EducationThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Individual and Domain Adaptation in Sentence Planning for Dialogue

One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations

Framing Appropriate Accommodations in Terms of Individual Need: Examining the Fit of Four Approaches to Selecting Test Accommodations of English Language Learners

Providing appropriate test accommodations to most English language learners (ELLs) is important to facilitate meaningful inferences about learning. This study compared teacher large-scale test accommodation recommendations to those from a literature- and practitioner-grounded accommodation selection taxonomy. The taxonomy links student-specific needs, strengths and schooling experiences to large-scale test accommodation recommendations that differentially minimize barriers of access for students with different profiles. A blind panel of experts rated four sets of recommendations for each of 114 ELLs. Results found the taxonomy was a significantly better fit for distinguishing accommodations by student need than teacher recommendations. Further, the fit of teacher recommendations showed no difference when the teacher used a structured data collection procedure to gather profile information about each of their ELLs and when they did not, and teachers’ recommendations were not found to differ significantly from a random set of accommodations. Findings are consistent with previous literature that suggests the task of matching specific accommodations to individual needs, rather than the task of identifying individual needs, is where teachers struggle in recommending appropriate test accommodations

Performance Evaluation Trait Validation

NPS NRP Technical ReportA valid and credible performance evaluation system is critical for identifying and managing talent in the US Navy. Yet, while multiple performance trait item banks have been developed to rate promotion candidates based on the quality of their candidacy, a systematic study of the validity of such trait items has not been conducted. The objective of this research is to assess the construct validity of draft performance trait/value statements recently developed to assist with promotion board decisions. This study aims to provide N1 with a set of unbiased, validated performance trait/value statements for performance evaluation efforts across the Navy, offering leadership an informed perspective on the potential benefits and risks associated these new metrics.N1 - Manpower, Personnel, Training & EducationThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.