A contextual modeling approach for model-based recommender systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40643-0_5Proceedings of 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013.In this paper we present a contextual modeling approach for model-based recommender systems that integrates and exploits both user preferences and contextual signals in a common vector space. Differently to previous work, we conduct a user study acquiring and analyzing a variety of realistic contextual signals associated to user preferences in several domains. Moreover, we report empirical results evaluating our approach in the movie and music domains, which show that enhancing model-based recommender systems with time, location and social companion information improves the accuracy of generated recommendations

Understanding the purchasing behavior of consumers in response to sustainable marketing practices: An empirical analysis in the food domain

Sustainability has become an important driver in defining business strategies, affecting most critical corporate functions and changing the way in which value is created, communicated, and distributed. This is increasingly impacting marketing practices, in particular, through promoting the development of sustainable marketing in the food sector. In line with this, our study aimed to investigate if and how sustainable marketing practices affect consumer loyalty to a specific brand. To answer our research questions, we relied on the results of a survey submitted to a sample of 907 Italian consumers of biscuits. Results showed that the consumers’ attention to sustainable issues (in the absence of adequate information that can guide them in choosing a brand) did not result in brand loyalty. The same outcome was found when consumers were overloaded by marketing campaigns, which had the effect of confusing users and making them unfaithful. Ultimately, when consumers showed both engagement with sustainable concerns and sensitivity to marketing initiatives (i.e., they are sensitive to sustainable marketing practices), a positive effect on brand loyalty was observed. Our results contribute to the emerging stream of literature discussing the relevance and potential impact of sustainable marketing

Sharing economy and dynamic pricing: Is the impact of Airbnb on the hotel industry time-dependent?

Prior literature has reported significant price and revenue reductions in the hotel industry due to the emergence of Airbnb. Other studies have documented that hotels' price reactions to the penetration of Airbnb depend on their service level, e.g., low/medium-end versus high end. Relying on a large sample from the Italian market, we contribute by showing that the effect of Airbnb on hotels' price decisions does not only depend on incumbents’ quality level, but also on the difference between booking and check-in time. That is, the effect of the penetration of Airbnb on hotels' dynamic price decisions varies over time depending on the core segment hotels target

Development of a decision support system framework for cultural heritage management

Decision support systems (DSSs) have been traditionally identified as useful information technology tools in a variety of fields, including the context of cultural heritage. However, to the best of our knowledge, no prior study has developed a DSS framework that incorporates all the main decision areas simultaneously in the context of cultural heritage. We fill this gap by focusing on design-science research and specifically by developing a DSS framework whose features support all the main decision areas for the sustainable management of cultural assets in a comprehensive manner. The main decision-making areas considered in our study encompass demand manage-ment, segmentation and communication, pricing, space management, and services management. For these areas, we select appropriate decision-making supporting techniques and data management solutions. The development of our framework, in the form of a web-based system, results in an architectural solution that is able to satisfy critical requirements such as ease of use and response time. We present an application of the innovative DSS framework to a museum and discuss the main managerial implications and future improvements

Crowdfunding performance, market performance, and the moderating roles of product innovativeness and experts' judgment: Evidence from the movie industry

Reward-based crowdfunding (CF) has emerged as a method to solicit funds for innovative projects. Yet, little is still known about the ability of reward-based CF to act as a signal in the eyes of future consumers, and thus boost the future market performance of new products that innovators intend to commercialize using the campaign funds. In addition, scant research has clarified the boundary conditions that can magnify or weaken the efficacy of this CF signal. Given the relevance of reward-based CF for supporting innovation, understanding when the CF campaign performance works as an effective signal is of great interest, especially in business settings characterized by high product quality uncertainty. By using the movie industry as a setting, we contribute to fill this gap. Specifically, we argue that the positive effect of the reward-based CF performance is moderated by two important factors influencing consumers' purchase decisions: the degree of product innovativeness and the expert judgment about the product. Elaborating on the effects of product innovativeness, we posit that this product feature should moderate the positive relationship between CF and subsequent market performances in an inverted U-shaped fashion. Favorable expert recommendations, on the other hand, should weaken the efficacy of the CF performance as a signal. Results from a sample of 1059 new movies (of which 152 released in theaters) confirm these predictions and offer several remarkable implications for innovators

Context-aware movie recommendations: An empirical comparison of pre-filtering, post-filtering and contextual modeling approaches

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39878-0_13Proceedings of 14th International Conference, EC-Web 2013, Prague, Czech Republic, August 27-28, 2013.Context-aware recommender systems have been proven to improve the performance of recommendations in a wide array of domains and applications. Despite individual improvements, little work has been done on comparing different approaches, in order to determine which of them outperform the others, and under what circumstances. In this paper we address this issue by conducting an empirical comparison of several pre-filtering, post-filtering and contextual modeling approaches on the movie recommendation domain. To acquire confident contextual information, we performed a user study where participants were asked to rate movies, stating the time and social companion with which they preferred to watch the rated movies. The results of our evaluation show that there is neither a clear superior contextualization approach nor an always best contextual signal, and that achieved improvements depend on the recommendation algorithm used together with each contextualization approach. Nonetheless, we conclude with a number of cues and advices about which particular combinations of contextualization approaches and recommendation algorithms could be better suited for the movie recommendation domain.This work was supported by the Spanish Government (TIN2011-28538-C02) and the Regional Government of Madrid (S2009TIC-1542

Context-aware user modeling strategies for journey plan recommendation

Popular journey planning systems, like Google Maps or Yahoo! Maps, usually ignore user’s preferences and context. This paper shows how we applied context-aware recommendation technologies in an existing journey planning mobile application to provide personalized and context-dependent recommendations to users. We describe two different strategies for context-aware user modeling in the journey planning domain. We present an extensive performance comparison of the proposed strategies by conducting a user-centric study in addition to a traditional offline evaluation methodPeer ReviewedPostprint (published version

Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

The choice of academic itineraries and/or optional subjects to attend is not usually an easy decision since, in most cases, students lack the information, maturity, and knowledge required to make right decisions. This paper evaluates the support of Collaborative Systems for helping and guiding students in this decision-making process, considering the behavior and impact of these systems on the use of data different from the formal information the students usually use. For this purpose, the research applied the clustering based Multi-dimension Tensor Factorization approach to build a recommendation system and confirm that the increment in tensors improves the recommendation accuracy. As a result, this approach permits the user to take advantage of the contextual information to reduce the sparsity issue and increase the recommendation accuracy

From mechatronics to the Cloud

At its conception mechatronics was viewed purely in terms of the ability to integrate the technologies of mechanical and electrical engineering with computer science to transfer functionality, and hence complexity, from the mechanical domain to the software domain. However, as technologies, and in particular computing technologies, have evolved so the nature of mechatronics has changed from being purely associated with essentially stand-alone systems such as robots to providing the smart objects and systems which are the building blocks for Cyber-Physical Systems, and hence for Internet of Things and Cloud-based systems. With the possible advent of a 4th Industrial Revolution structured around these systems level concepts, mechatronics must again adapt its world view, if not its underlying technologies, to meet this new challenge