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A content recommendation system based on human brain activity has become a reality. However, the cost of collecting the information from people is problematic. This article proposes a scheme that resolves the tradeoff between the inference performance from a profile model to a brain model and the cost of collecting profile information. In the proposed scheme, a machine learning model infers the brain model from the profile model and a feature selection method is applied to reduce the cost, i.e., the number of questionnaire items, of collecting profile information. Since only the top questionnaire items with the highest importance scores are used, we can maintain the inference performance as high as possible while limiting the number of questionnaire items. We demonstrate the effectiveness of the proposed scheme with a performance evaluation using an experimentally obtained brain model and a profile model created from real profile information. The results over different experimental parameters, video lengths, and feature selection methods demonstrate that the proposed scheme successfully identifies the top questionnaire items that contribute most significantly to the inference of brain models. [ABSTRACT FROM AUTHOR]

E-commerce businesses are increasingly dependent on recommendation systems to introduce personalized services and products to targeted customers. Providing effective recommendations requires sufficient knowledge about user preferences and product (item) characteristics. Given the current abundance of available data across domains, achieving a thorough understanding of the relationship between users and items can bring in more collaborative filtering power and lead to a higher recommendation accuracy. However, how to effectively utilize different types of knowledge obtained across domains is still a challenging problem. In this paper, we propose to discover both explicit and implicit similarities from latent factors across domains based on matrix tri-factorization. In our research, common factors in a shared dimension (users or items) of two coupled matrices are discovered, while at the same time, domain-specific factors of the shared dimension are also preserved. We will show that such preservation of both common and domain-specific factors are significantly beneficial to cross-domain recommendations. Moreover, on the non-shared dimension, we propose to use the middle matrix of the tri-factorization to match the unique factors, and align the matched unique factors to transfer cross-domain implicit similarities and thus further improve the recommendation. This research is the first that proposes the transfer of knowledge across the non-shared (non-coupled) dimensions. Validated on real-world datasets, our approach outperforms existing algorithms by more than two times in terms of recommendation accuracy. These empirical results illustrate the potential of utilizing both explicit and implicit similarities for making across-domain recommendations. [ABSTRACT FROM AUTHOR]

Event recommendation is essential to help people find attractive events to attend, but it intrinsically faces cold-start problem. The previous studies exploit multiple contextual factors to overcome the cold-start problem in event recommendation. However, they do not consider the correlation among different contextual factors. Moreover, suggesting events for a group of users also has not been well studied. In this paper, we first discover the correlation between organizer and textual content, i.e., the events held by the same organizer tend to have more similar content. Based on this observation, we present a content-venue-aware topic model (CVTM) to capture group interests on an event from two perspectives: content and venue. The correlation between organizer and content is modeled in CVTM to alleviate the sparsity of textual content, and then we can further extract group interests on content of an event more accurately. Finally, a group event recommendation method using CVTM is proposed. We conduct comprehensive experiments to evaluate the recommendation performance of our model on two real-world datasets. The results demonstrate that the proposed model outperforms the state-of-the-art methods that suggest upcoming events for groups. Besides, CVTM can learn semantically coherent latent topics which are useful to explain recommendations. [ABSTRACT FROM AUTHOR]