Modeling Adoption and Usage of Competing Products

The emergence and wide-spread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, marketers and sociologists for decades, such as, e.g., product adoption, usage and competition. In this paper, we propose a continuous-time probabilistic model, based on temporal point processes, for the adoption and frequency of use of competing products, where the frequency of use of one product can be modulated by those of others. This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which we can easily recognize the effect of social influence, recency and competition. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our model over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only provides a good fit to the data and more accurate predictions than alternatives but also provides interpretable model parameters, which allow us to gain insights into some of the factors driving product adoption and frequency of use

Correlated Cascades: Compete or Cooperate

In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives

Recurrent Poisson Factorization for Temporal Recommendation

Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model and brings to the table a rich family of time-sensitive factorization models. To elaborate, we instantiate several variants of RPF who are capable of handling dynamic user preferences and item specification (DRPF), modeling the social-aspect of product adoption (SRPF), and capturing the consumption heterogeneity among users and items (HRPF). We also develop a variational algorithm for approximate posterior inference that scales up to massive data sets. Furthermore, we demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and large scale real-world datasets on music streaming logs, and user-item interactions in M-Commerce platforms.Comment: Submitted to KDD 2017 | Halifax, Nova Scotia - Canada - sigkdd, Codes are available at https://github.com/AHosseini/RP

Maximizing Welfare in Social Networks under a Utility Driven Influence Diffusion Model

Motivated by applications such as viral marketing, the problem of influence maximization (IM) has been extensively studied in the literature. The goal is to select a small number of users to adopt an item such that it results in a large cascade of adoptions by others. Existing works have three key limitations. (1) They do not account for economic considerations of a user in buying/adopting items. (2) Most studies on multiple items focus on competition, with complementary items receiving limited attention. (3) For the network owner, maximizing social welfare is important to ensure customer loyalty, which is not addressed in prior work in the IM literature. In this paper, we address all three limitations and propose a novel model called UIC that combines utility-driven item adoption with influence propagation over networks. Focusing on the mutually complementary setting, we formulate the problem of social welfare maximization in this novel setting. We show that while the objective function is neither submodular nor supermodular, surprisingly a simple greedy allocation algorithm achieves a factor of $(1-1/e-\epsilon)$ of the optimum expected social welfare. We develop \textsf{bundleGRD}, a scalable version of this approximation algorithm, and demonstrate, with comprehensive experiments on real and synthetic datasets, that it significantly outperforms all baselines.Comment: 33 page

Country knowledge and familiarity effects on consumer perceived risk and rejection of foreign-made products

Las preocupaciones de los consumidores por los productos fabricados en el extranjero plantean interrogantes sobre lo que éstos conocen acerca del país de origen y la medida en que ese conocimiento se traduce en su rechazo de los productos extranjeros. El presente estudio examina tres tipos de variables de conocimiento acerca del país de origen que, junto con la experiencia de uso/familiaridad, actúan como antecedentes potenciales del riesgo percibido y, a su vez, de la reticencia de los consumidores españoles a comprar y de la (no) posesión de productos chinos. Los resultados revelan que el conocimiento medioambiental del país genera riesgo de auto-imagen/social, mientras que la experiencia de uso/familiaridad reduce tanto el riesgo de auto-imagen/social como el riesgo de rendimiento. Ambos tipos de riesgo afectan significativamente (positivamente) a la reticencia de los consumidores a comprar y (negativamente) a la posesión de productos extranjeros. Se discuten las implicaciones teóricas.Consumer concerns about foreign-made products raise questions about what consumers know about the COO and the extent to which such knowledge translates into their rejection of foreign products. The present examines three types country knowledge variables, along with familitarity/usage experience, as potential antecedents of perceived risk and, in turn, of Spanish consumers’ reluctance to buy and (non-)ownership of Chinese apparel products. The findings reveal that environmental country knowledge can engender self-image/social risk, whereas familiarity/usage experience reduces both self-image/social and performance risks. As expected, the two distinct risk types considered here significantly contributed to consumers’ reluctance to buy (positively) and product ownership (negatively). Theoretical implications are discussed

Mediating effects of broadband consumers’ behavior in India

Internet usage is rapidly growing in areas like cosmopolitan cities, semi-urban cities in India. I-enabled services offered by various government agencies, educational institutions and commercial activities force users of these services to seek superior internet access like broadband, WiMax is likely to replace traditional broadband and dial-up access soon. Interestingly, reforms in telecom sector are taking place at a rapid pace in India. Many private players started internet services affecting monopolistic public sector telecoms. The advent of private ISPs, the consumer behavior and brand choice of broadband consumers are witnessing dynamic shift in favor of private players. Cost competitiveness, transparency, paradigm shift in consumer responsiveness etc weigh in favor of Public Sector telecoms. This paper attempts to identify the factors affecting broadband consumer behavior. Further, paper studies the causes and effects, mediating effects of consumer behavior and conceptualizes a model to capture these effects. The results suggest that adoption of broadband service is playing a mediatory role in consumer satisfaction.Broadband, Adoption, Normative constructs, mediating

Abstracts of Selected Papers, NAREA Annual Meetings, Burlington, Vermont, June 7-10, 2009

Agribusiness, Agricultural and Food Policy, Agricultural Finance, Community/Rural/Urban Development, Consumer/Household Economics, Crop Production/Industries, Demand and Price Analysis, Environmental Economics and Policy, Farm Management, Financial Economics, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Health Economics and Policy, Industrial Organization, Institutional and Behavioral Economics, International Development, International Relations/Trade, Labor and Human Capital, Land Economics/Use, Livestock Production/Industries, Marketing, Political Economy, Production Economics, Productivity Analysis, Public Economics, Research and Development/Tech Change/Emerging Technologies, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy, Risk and Uncertainty, Teaching/Communication/Extension/Profession,