The insurance industry is shifting their sales mode from offline to online, in expectation to reach massive potential customers in the digitization era. Due to the complexity and the nature of insurance products, a cost-effective online sales solution is to exploit chatbot AI to raise customers' attention and pass those with interests to human agents for further sales. For high response and conversion rates of customers, it is crucial for the chatbot to initiate a conversation with personalized opening sentences, which are generated with user-specific topic selection and ordering. Such personalized opening sentence generation is challenging because (i) there are limited historical samples for conversation topic recommendation in online insurance sales and (ii) existing text generation schemes often fail to support customized topic ordering based on user preferences. We design POSGen, a personalized opening sentence generation scheme dedicated for online insurance sales. It transfers user embeddings learned from auxiliary online user behaviours to enhance conversation topic recommendation, and exploits a context management unit to arrange the recommended topics in user-specific ordering for opening sentence generation. POSGen is deployed on a real-world online insurance platform. It achieves 2.33x total insurance premium improvement through a two-month global test.

Pretrained Language Models (PLM) have been greatly successful on a board range of natural language processing (NLP) tasks. However, it has just started being applied to the domain of recommendation systems. Traditional recommendation algorithms failed to incorporate the rich textual information in e-commerce datasets, which hinderss the performance of those models. We present a thorough investigation on the effect of various strategy of incorporating PLMs into traditional recommender algorithms on one of the e-commerce datasets, and we compare the results with vanilla recommender baseline models. We show that the application of PLMs and domain specific fine-tuning lead to an increase on the predictive capability of combined models. These results accentuate the importance of utilizing textual information in the context of e-commerce, and provides insight on how to better apply PLMs alongside traditional recommender system algorithms. The code used in this paper is available on Github: https://github.com/NuofanXu/bert_retail_recommender.

In recent years, thanks to the rapid development of deep learning (DL), DL-based multi-task learning (MTL) has made significant progress, and it has been successfully applied to recommendation systems (RS). However, in a recommender system, the correlations among the involved tasks are complex. Therefore, the existing MTL models designed for RS suffer from negative transfer to different degrees, which will injure optimization in MTL. We find that the root cause of negative transfer is feature redundancy that features learned for different tasks interfere with each other. To alleviate the issue of negative transfer, we propose a novel multi-task learning method termed Feature Decomposition Network (FDN). The key idea of the proposed FDN is reducing the phenomenon of feature redundancy by explicitly decomposing features into task-specific features and task-shared features with carefully designed constraints. We demonstrate the effectiveness of the proposed method on two datasets, a synthetic dataset and a public datasets (i.e., Ali-CCP). Experimental results show that our proposed FDN can outperform the state-of-the-art (SOTA) methods by a noticeable margin.

With Valentine's Day on the horizon, many singletons might be swiping on their dating apps with a little more urgency than normal. Unfortunately, a new study from the University of Vienna has found that this excessive searching could be doing more harm than good in the quest for love. Psychologists surveyed 464 young people on their dating app use, including how much they swipe and how they decide whether to go left or right on a profile. They were also asked if they compare themselves to others or become overwhelmed when browsing profiles, as well as about their feelings towards being single. A correlation was found between excessive swiping and a fear of being alone forever, feeling bad about one's life and so-called'partner choice overload'.

The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, such as short video, news and etc., understanding these contents while recommending becomes critical. Besides, multimodal features are also helpful in alleviating the problem of data sparsity in RS. Thus, Multimodal Recommender System (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views. First, we conclude the general procedures and major challenges for MRS. Then, we introduce the existing MRS models according to three categories, i.e., Feature Interaction, Feature Enhancement and Model Optimization. To make it convenient for those who want to research this field, we also summarize the dataset and code resources. Finally, we discuss some promising future directions of MRS and conclude this paper.

Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros. We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing. The key idea is to transform each user's ratings on the items to a function (signal) on the vertices of an item-item graph, then learn structural graph properties to recover the function from its values on certain vertices -- the problem of graph signal sampling. We propose a class of regularization functionals that takes into account discrete random label noise in the graph vertex domain, then develop the GS-IMC approach which biases the reconstruction towards functions that vary little between adjacent vertices for noise reduction. Theoretical result shows that accurate reconstructions can be achieved under mild conditions. For the online setting, we develop a Bayesian extension, i.e., BGS-IMC which considers continuous random Gaussian noise in the graph Fourier domain and builds upon a prediction-correction update algorithm to obtain the unbiased and minimum-variance reconstruction. Both GS-IMC and BGS-IMC have closed-form solutions and thus are highly scalable in large data. Experiments show that our methods achieve state-of-the-art performance on public benchmarks.

Secret Siri shortcuts you never knew to ask until now. Many times, I think Siri just doesn't understand me. That may be true and not her fault. It turns out I may not have been asking Siri the right questions to make my life easier – until I learned these shortcuts. Putting these five Siri tricks into play will have you wishing you had known these secret Siri commands a long time ago.

Spread the loveIt refers to the capacity of computer programs to carry out tasks that were normally attributed to humans. Such tasks include translation of languages, speech recognition, visual awareness & perception, as well as the making of decisions. Artificial intelligence can be broadly grouped into two classes – weak AI and strong AI. Weak AI, often called Artificial Narrow Intelligence (ANI) or Narrow AI, refers to artificial intelligence that’s trained and focused on carrying out particular tasks. Most of the AI that’s in operation today is driven by weak AI. It powers some extremely robust applications, like Amazon’s Alexa, […]

The use of packaged libraries can significantly shorten the software development cycle by improving the quality and readability of code. In this paper, we present a recommendation engine called Librarian for open source libraries. A candidate library package is recommended for a given context if: 1) it has been frequently used with the imported libraries in the program; 2) it has similar functionality to the imported libraries in the program; 3) it has similar functionality to the developer's implementation, and 4) it can be used efficiently in the context of the provided code. We apply the state-of-the-art CodeBERT-based model for analysing the context of the source code to deliver relevant library recommendations to users.

I am a mostly visual thinker, and thoughts pose as scenes in the theater of my mind. When my many supportive family members, friends, and colleagues asked how I was doing, I'd see myself on a cliff, transfixed by an omniscient fog just past its edge. In the scene, there is no sound or urgency and I am waiting for it to swallow me. I'm searching for shapes and navigational clues, but it's so huge and gray and boundless. I wanted to take that fog and put it under a microscope.