Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment

Recommender systems often struggle to strike a balance between matching users' tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cover the full range of users' preferences, the system is perceived as useless. Conversely, when the system suggests too many items that users don't like, it is considered impersonal or ineffective. To better understand user sentiment about the breadth of recommendations given by a movie recommender, we conducted interviews and surveys and found out that many users considered narrow recommendations to be useful, while a smaller number explicitly wanted greater breadth. Additionally, we designed and ran an online field experiment with a larger user group, evaluating two new interfaces designed to provide users with greater access to broader recommendations. We looked at user preferences and behavior for two groups of users: those with higher initial movie diversity and those with lower diversity. Among our findings, we discovered that different level of exploration control and users' subjective preferences on interfaces are more predictive of their satisfaction with the recommender.Comment: International Journal of Human Computer Interactio

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this paper, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they expect from the system. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. Besides, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists.We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing the users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” data sets to compare our recommendation results with some other standard baseline methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage and aggregate diversity, while avoiding any accuracy loss

A reinforcement learning recommender system using bi-clustering and Markov Decision Process

Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning

CWU Faculty Senate Minutes - 03/03/2004

These are the official Central Washington University Faculty Senate Minutes for the 03/03/2004 regular meeting

Faculty Search Committee Procedures Handbook

A handbook from 2009 to provide search committees with the information they need to conduct a fair and equitable search (3). The handbook goes over everything from the roles of various members of a given committee (7-13), to detailed information on interview conduct and procedures (24-30)

Globe: All Ireland Programme for Immigrant Parents: Final Evaluation Report

In 2007, the Child and Family Research Centre, NUI Galway, was commissioned by the PMC to evaluate Globe: All Ireland Programme for Immigrant Parents. From 2007 - 2009 a number of interim evaluation reports were submitted to the PMC on the development phase of the project and its resources. In 2009, following the extension of the project, the objectives of the evaluation were revised. These objectives, which underpin this final evaluation report, are as follows:Examine and assess the pilot phase;Examine and assess the uptake and use of the Information Packs by parents and practitioners;Examine and assess the partnership working and development on a multi-sectoral and crossborder basis of the PMC, and more generally in meeting the needs of immigrant parents;Examine and assess the mainstreaming of learning and good practice; andExamine and assess the training/awareness raising and support of practitioners in delivering the programme

A report on e-portfolios : design features, uses, benefits, examples & emerging trends

This paper gives a more sophisticated response to the rapid (re)emergence of the e­portfolio buzz­word. Starting from a basic e­portfolio design pattern, a wide range of variations are explored. The aim is to establish a body of knowledge for guiding users and technology providers, so as to achieve an ever­more appropriate and fruitful alignment of needs, designs, platforms and informed choices. The key benefits of e­portfolio approaches are discussed, with some coverage of the variations, and suggested research and development directions. Deep and persistent diversity­creating factors are highlighted. A range of mini case studies from Warwick are then examined to throw further light upon the combinations of real and perceived needs, platform affordances and design choices. Finally, this is a fast evolving field, especially given the near ­ubiquitous adoption of platforms with e­portfolio­like elements (Facebook, LinkedIn etc). Technology and academic support services must look further forwards to emerging practices and requirements just at the edge of the institutional­perceptual horizon. We must be prepared to shape these potentially disruptive developments for the benefit of students, teachers, the institution and society