Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Leadership in Action: How Top Hackers Behave A Big-Data Approach with Text-Mining and Sentiment Analysis

This paper examines hacker behavior in dark forums and identifies its significant predictors in the light of leadership theory for communities of practice. We combine techniques from online forum features as well as text-mining and sentiment-analysis of messages. We create a multinomial logistic regression model to achieve role-based hacker classification and validate our model with actual hacker forum data. We identify total number of messages, number of threads, hacker keyword frequency, and sentiments as the most significant predictors of expert hacker behavior. We also demonstrate that while disseminating technical knowledge, the hacker community follows Pareto principle. As a recommendation for future research, we build a unique keyword lexicon of the most significant terms derived by tf-idf measure. Such investigation of hacker behavior is particularly relevant for organizations in proactive prevention of cyber-attacks. Foresight on online hacker behavior can help businesses save losses from breaches and additional costs of attack-preventive measures

Multinationals or Cooperatives: Does it Matter to Farmers? - A Study of the Dairy Sector in Punjab (India)

Agribusiness, Livestock Production/Industries,

Modes of Cooperative R&D Commercialization by Start-Ups

This study empirically examines the determinants of heterogeneous firm-level cooperative R&D commercialization strategies. While the volume of interfirm collaboration has increased dramatically in recent decades, the determinants of firm-level choices among alternate modes of such cooperative activity remain relatively understudied. We develop a conceptual model of factors determining collaborative mode choice at the organizational portfolio level. These factors include the firm-level appropriation environment, in which deal-level choices have portfolio-level spillover implications, as well as governance capabilities developed by the firm over time. Using a random sample of innovating biotechnology start-ups, we assemble a firm-year panel dataset that aggregates transaction-level collaboration data to the firm-year level, allowing us to characterize firms\u27 portfolios of collaborative deals. We find broad empirical support for our model, suggesting that a firm\u27s appropriation environment and governance capabilities strongly influence portfolio-level collaboration mode choices. In addition, we explore the implications of governance capability development, finding that experience with particular modes, as well as deviations from existing capabilities, impact firm valuation