In today's world of marketing, it is necessary to have visually appealing
content. Visual material has become an essential area of focus for every
company as a result of the widespread availability of gadgets for mass
communication and extended visual advancements. Similarly, artificial
intelligence is also gaining ground and it is proving to be the most
revolutionary technological advancement thus far. The integration of visual
content with artificial intelligence is the key to acquiring and retaining
loyal customers; its absence from the overarching marketing strategy of any
production raises a red flag that could ultimately result in a smaller market
share for that company.

将视觉内容与人工智能相结合是获取和保留忠实客户的关键，没有它将在产品的总体营销策略中引起警惕，并最终导致公司的市场份额减小。

生成式人工智能对视觉内容营销未来的影响

The Impact of Generative AI on the Future of Visual Content Marketing

As one of major challenges, cold-start problem plagues nearly all recommender
systems. In particular, new items will be overlooked, impeding the development
of new products online. Given limited resources, how to utilize the knowledge
of recommender systems and design efficient marketing strategy for new items is
extremely important. In this paper, we convert this ticklish issue into a clear
mathematical problem based on a bipartite network representation. Under the
most widely used algorithm in real e-commerce recommender systems, so-called
the item-based collaborative filtering, we show that to simply push new items
to active users is not a good strategy. To our surprise, experiments on real
recommender systems indicate that to connect new items with some less active
users will statistically yield better performance, namely these new items will
have more chance to appear in other users' recommendation lists. Further
analysis suggests that the disassortative nature of recommender systems
contributes to such observation. In a word, getting in-depth understanding on
recommender systems could pave the way for the owners to popularize their
cold-start products with low costs.

通过使用二分网络表示法，本文针对冷启动问题，旨在探讨在推荐系统中如何设计高效的营销策略，并通过实验发现，将新商品与一些不太活跃的用户联系起来比简单地将新商品推送给活跃用户更具效果。