Venue category recommendation is an essential application for the tourism and advertisement industries, wherein it may suggest attractive localities within close proximity to users’ current location. Considering that many adults use more than three social networks simultaneously, it is reasonable to leverage on this rapidly growing multi-source social media data to boost venue recommendation performance. Another approach to achieve higher recommendation results is to utilize group knowledge, which is able to diversify recommendation output. Taking into account these two aspects, we introduce a novel cross-network collaborative recommendation framework C 3R, which utilizes both individual and group knowledge, while being trained on data from multiple social media sources. Group knowledge is derived based on new crosssource user community detection approach, which utilizes both inter-source relationship and the ability of sources to complement each other. To fully utilize multi-source multi-view data, we process user-generated content by employing state-of-the-art text, image, and location processing techniques. Our experimental results demonstrate the superiority of our multi-source framework over state-of-the-art baselines and different data source combinations. In addition, we suggest a new approach for automatic construction of inter-network relationship graph based on the data, which eliminates the necessity of having pre-defined domain knowledge
In this technical demonstration, we showcase the first ai-driven social multimedia influencer discovery marketplace, called SoMin. The platform combines advanced data analytics and behavioral science to help marketers find, understand their audience and engage the most relevant social media micro-influencers at a large scale. SoMin harvests brand-specific life social multimedia streams in a specified market domain, followed by rich analytics and semantic-based influencer search. The Individual User Profiling models extrapolate the key personal characteristics of the brand audience, while the influencer retrieval engine reveals the semantically-matching social media influencers to the platform users. The influencers are matched in terms of both their-posted content and social media audiences, while the evaluation results demonstrate an excellent performance of the proposed recommender framework. By leveraging influencers at a large scale, marketers will be able to execute more effective marketing campaigns of higher trust and at a lower cost.
The exponential growth of online social networks has inspired us to tackle the problem of individual user attributes inference from the Big Data perspective. It is well known that various social media networks exhibit different aspects of user interactions, and thus represent users from diverse points of view. In this preliminary study, we make the first step towards solving the significant problem of personality profiling from multiple social networks. Specifically, we tackle the task of relationship prediction, which is closely related to our desired problem. Experimental results show that the incorporation of multi-source data helps to achieve better prediction performance as compared to single-source baselines.
March 9 saw the launch of the principal phase of Sber500, Sber and 500 Startups’ international accelerator program. The 25 best startups were chosen to work individually with Silicon Valley mentors over the next eight weeks and present their projects on Demo Day to Sber ecosystem executives and leading Russian and international corporations and funds.
The accelerator received entries from approximately 1,800 startups from countries all over the world – almost three times the applicants for the previous two Sber500 batches. Aside from Russia, this year’s submissions came from countries including Singapore, Israel, Germany, Kazakhstan, Latvia, Belarus, and Estonia. The vast majority of projects submitted (over 70%) have already closed their first deals and are based on B2B and B2B2C business models. Around 60% of all submissions were startups working with e-commerce, logistics, marketing, and Data Analytics.
Artificial intelligence has completely redefined the world we live in today. Now, our mornings start with personal assistants waking us up and laying our favorite playlists. Bots help customers with their questions and issues about the services and products they have purchased. And now, artificial intelligence is capable of redefining influencer marketing with the power of creating a persona.
Meet Maya — the next-generation virtual influencer set to transform the concept of brand marketing.
The SoMin.ai startup, which improves its ad targeting using artificial intelligence, has entered an emerging market. A Singapore-based startup uses big data and artificial intelligence to boost conversions.
In targeted advertising from Citibank, AI increased conversion by 53% and reduced the cost of customer contacts by 43%, SoMin.ai founder Alex Farseev told DP. The Lenta network noted a 2.5-fold improvement in the effectiveness of an advertising campaign. Somin.ai is currently working with MTS.