Heterogeneous Information Networks (HINs) are labeled graphs that depict relationships among different types of entities (e.g., users, movies and directors). For HINs, meta-path-based recommenders (MPRs) utilize meta-paths (i.e., abstract paths consisting of node and link types) to predict user preference, and have attracted a lot of attention due to their explainability and performance. We observe that the performance of MPRs is highly sensitive to the meta-paths they use, but existing works manually select the meta-paths from many possible ones. Thus, to discover effective meta-paths automatically, we propose the Reinforcement learning-based Meta-path Selection (RMS) framework. Specifically, we define a vector encoding for meta-paths and design a policy network to extend meta-paths. The policy network is trained based on the results of downstream recommendation tasks and an early stopping approximation strategy is proposed to speed up training. RMS is a general model, and it can work with all existing MPRs. We also propose a new MPR called RMS-HRec, which uses an attention mechanism to aggregate information from the meta-paths. We conduct extensive experiments on real datasets. Compared with the manually selected meta-paths, the meta-paths identified by RMS consistently improve recommendation quality. Moreover, RMS-HRec outperforms state-of-the-art recommender systems by an average of 7% in hit ratio. The codes and datasets are available on https://github.com/Stevenn9981/RMS-HRec.

Conversational recommender systems (CRS) are interactive agents that support their users in recommendation-related goals through multi-turn conversations. Generally, a CRS can be evaluated in various dimensions. Today's CRS mainly rely on offline (computational) measures to assess the performance of their algorithms in comparison to different baselines. However, offline measures can have limitations, for example, when the metrics for comparing a newly generated response with a ground truth do not correlate with human perceptions, because various alternative generated responses might be suitable too in a given dialog situation. Current research on machine learning-based CRS models therefore acknowledges the importance of humans in the evaluation process, knowing that pure offline measures may not be sufficient in evaluating a highly interactive system like a CRS. In this work, we provide a user-centric evaluation approach to conversational recommendation along with the INFACT, an onlIne humaN evaluation Framework for conversAtional reCommender sysTems, which can be used to assess the suitability of system responses in a given dialog situation. The INFACT framework is prepared to enable the crowdsourcing of the evaluation task, where various CRS can be integrated for comparison. We have successfully applied the INFACT framework for conducting a number of user studies in our previous research. We believe that our study design along with the INFACT framework can be helpful in facilitating user-centric studies in domains such as dialog systems, machine translation, or Q&A.

'Swiping left' and'swiping right' have become ubiquitous with whether we find someone attractive or not, all thanks to the rise of dating apps. The likes of Tinder, Bumble and Hinge have made online dating pocket-sized, and singletons can whip out their phone wherever they are to search for a partner. But this accessibility has arguably made it more difficult than ever to stand out from the crowd, with an estimated 300 million people are currently using dating apps worldwide. Fortunately, experts are here to help the lonely hearts, and have worked tirelessly over the years to find the secret formula for success in online dating. Studies have shown that having a dog in your photos or an Apple product increase your chance of getting a match.

In this day and age of technological advancements, people are looking for solutions to automate regular and repetitive tasks as much as possible. As such, the development of artificial intelligence algorithms has come a long way to help in automation and reduce human labor. It would give us humans enough time to focus on the development of our own skills and pursue our dreams and aspirations. With the potential artificial intelligence has shown, many industries are bound to change their working strategies and rules. Industries will change how they operate and adopt newer and more efficient methods which depend on AI algorithms. The application of Artificial Intelligence to automate medical procedures and negate possible complications is not a new idea.

What is the Internet of Things(IoT)? So how does the Internet of Things disrupt digital marketing? Digital technology has blurred the line between online and offline life. From wearable technology that tracks health conditions around the clock, to watching favorite entertainment programs on the way to and from get off work, and remote access to electrical equipment at home. The shift to mobile devices has changed the way we interact with the world around us.

Many exciting things are coming up, hence no new documentation and test are going to be written until the official release of Leon. Feel free to join us on Discord to know more and to read the "A Much Better NLP and Future" blog post. Leon is an open-source personal assistant who can live on your server. He does stuff when you ask him to. You can talk to him and he can talk to you.

The recent advances of conversational recommendations provide a promising way to efficiently elicit users' preferences via conversational interactions. To achieve this, the recommender system conducts conversations with users, asking their preferences for different items or item categories. Most existing conversational recommender systems for cold-start users utilize a multi-armed bandit framework to learn users' preference in an online manner. However, they rely on a pre-defined conversation frequency for asking about item categories instead of individual items, which may incur excessive conversational interactions that hurt user experience. To enable more flexible questioning about key-terms, we formulate a new conversational bandit problem that allows the recommender system to choose either a key-term or an item to recommend at each round and explicitly models the rewards of these actions. This motivates us to handle a new exploration-exploitation (EE) trade-off between key-term asking and item recommendation, which requires us to accurately model the relationship between key-term and item rewards. We conduct a survey and analyze a real-world dataset to find that, unlike assumptions made in prior works, key-term rewards are mainly affected by rewards of representative items. We propose two bandit algorithms, Hier-UCB and Hier-LinUCB, that leverage this observed relationship and the hierarchical structure between key-terms and items to efficiently learn which items to recommend. We theoretically prove that our algorithm can reduce the regret bound's dependency on the total number of items from previous work. We validate our proposed algorithms and regret bound on both synthetic and real-world data.

Nowadays, it's a very significant way for researchers and other individuals to achieve their interests because it provides short solutions to satisfy their demands. Because there are so many pieces of information on the internet, news recommendation systems allow us to filter content and deliver it to the user in proportion to his desires and interests. RSs have three techniques: content-based filtering, collaborative filtering, and hybrid filtering. We will use the MIND dataset with our system, which was collected in 2019, the big challenge in this dataset because there is a lot of ambiguity and complex text processing. In this paper, will present our proposed recommendation system. The core of our system we have used the GloVe algorithm for word embeddings and representation. Besides, the Multi-head Attention Layer calculates the attention of words, to generate a list of recommended news. Finally, we achieve good results more than some other related works in AUC 71.211, MRR 35.72, nDCG@5 38.05, and nDCG@10 44.45.

Artificial intelligence has always been considered a revolutionary technology that has emerged to solve complex real-world problems like high-level computation, omitting manual labor, or data-driven optimization. However, with its endless possibilities, there are many applications of AI that make this technology more accessible to the average layman person or kids at home. To get people's heads around this sophisticated technology developers all around the world are continuously developing some fun AI tools that can be easily accessed online to get hands-on. Not only are these AI tools fun but also provide a good understanding of this technology to the users. So, before jumping to those exciting and crazy artificial intelligence tools; first, we must know about What AI is.

Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens of dual-graph regularization, which has significantly improved the performance of multidisciplinary machine learning tasks such as recommendation systems, genotype imputation and image inpainting. While the dual-graph regularization contributes a major part of the success, computational costly hyper-parameter tunning is usually involved. To circumvent such a drawback and improve the completion performance, we propose a novel Bayesian learning algorithm that automatically learns the hyper-parameters associated with dual-graph regularization, and at the same time, guarantees the low-rankness of matrix completion. Notably, a novel prior is devised to promote the low-rankness of the matrix and encode the dual-graph information simultaneously, which is more challenging than the single-graph counterpart. A nontrivial conditional conjugacy between the proposed priors and likelihood function is then explored such that an efficient algorithm is derived under variational inference framework. Extensive experiments using synthetic and real-world datasets demonstrate the state-of-the-art performance of the proposed learning algorithm for various data analysis tasks.