Cultural Summit II Work Book

OklahomaFeasibility study sInstitute of Museum and Library Service

Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal

The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented

How to Retrain Recommender System? A Sequential Meta-Learning Method

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community. Our first belief is that retraining the model on historical data is unnecessary, since the model has been trained on it before. Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference. To address this dilemma, we propose a new training method, aiming to abandon the historical data during retraining through learning to transfer the past training experience. Specifically, we design a neural network-based transfer component, which transforms the old model to a new model that is tailored for future recommendations. To learn the transfer component well, we optimize the "future performance" -- i.e., the recommendation accuracy evaluated in the next time period. Our Sequential Meta-Learning(SML) method offers a general training paradigm that is applicable to any differentiable model. We demonstrate SML on matrix factorization and conduct experiments on two real-world datasets. Empirical results show that SML not only achieves significant speed-up, but also outperforms the full model retraining in recommendation accuracy, validating the effectiveness of our proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

World radiocommunication conference 12 : implications for the spectrum eco-system

Spectrum allocation is once more a key issue facing the global telecommunications industry. Largely overlooked in current debates, however, is the World Radiocommunication Conference (WRC). Decisions taken by WRC shape the future roadmap of the telecommunications industry, not least because it has the ability to shape the global spectrum allocation framework. In the debates of WRC-12 it is possible to identify three main issues: enhancement of the international spectrum regulatory framework, regulatory measures required to introduce Cognitive Radio Systems (CRS) technologies; and, additional spectrum allocation to mobile service. WRC-12 eventually decided not to change the current international radio regulations with regard to the first two issues and agreed to the third issue. The main implications of WRC-12 on the spectrum ecosystem are that most of actors are not in support of the concept of spectrum flexibility associated with trading and that the concept of spectrum open access is not under consideration. This is explained by the observation that spectrum trading and spectrum commons weaken state control over spectrum and challenge the main principles and norms of the international spectrum management regime. In addition, the mobile allocation issue has shown the lack of conformity with the main rules of the regime: regional spectrum allocation in the International Telecommunication Union (ITU) three regions, and the resistance to the slow decision making procedures. In conclusion, while the rules and decision-making procedures of the international spectrum management regime were challenged in the WRC-12, the main principles and norms are still accepted by the majority of countries

Antecedents of acceptance of social networking sites in retail franchise and restaurant businesses

The paper examines the antecedents of acceptance of social networking sites in retail franchise and restaurant businesses. The success of retail franchise and restaurant business oper-ators via social networking sites depends not only on organiza-tional benefits but also on their behavioral intentions of using it. Three hundred and twenty four samples collected from South Korean retail franchise and restaurant employees are analyzed using factor analysis, structural equation model techniques and one-way analysis of variance. The results of the study identify the three constructs of organizational benefits, perceived tangible assets and perceived intangible assets as for important ante-cedents to accept social networking sites for their business use. Moreover, higher position employees tend to have more favor-able perception of tangible assets and acceptance of social net-working sites for their business use

Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research

This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing