Finding online research papers relevant to one's interests is very challenging due to the increasing number of publications. Therefore, personalized research paper recommendation has become a significant and timely research topic. Collaborative filtering is a successful recommendation approach, which exploits the ratings given to items by users as a source of information for learning to make accurate recommendations. However, the ratings are often very sparse as in the research paper domain, due to the huge number of publications growing every year. Therefore, more attention has been drawn to hybrid methods that consider both ratings and content information. Nevertheless, most of the hybrid recommendation approaches that are based on text embedding have utilized bag-of-words techniques, which ignore word order and semantic meaning. In this paper, we propose a hybrid approach that leverages deep semantic representation of research papers based on social tags assigned by users. The experimental evaluation is performed on CiteULike, a real and publicly available dataset. The obtained findings show that the proposed model is effective in recommending research papers even when the rating data is very sparse.

Voice AI is expected to reduce global call centre labour costs by as much as US$80 billion by 2026, according to Gartner. However, Australia's adoption of conversational AI for customer service remains relatively low compared to other countries. "Nine-in-ten Australians are now smartphone users, with Siri, Google or Alexa at their fingertips," said Kun Wu, Founder and Managing Director of AI Rudder, a leading voice AI provider in the Asian Pacific (APAC). Voice AI technologies have enhanced consumers' lives in terms of communication, media use, entertainment and information searches. Given this high consumer penetration, Wu says the potential exists for the Australian customer service industry to follow APAC markets in using voice AI to deliver high-quality service.

Healthcare leaders don't need reminding that they face serious nurse shortages. It's a challenge they have faced for years – and it's become a critical concern as record numbers leave their practices due to burnout and exhaustion. The November 2021 Hospital IQ Survey found that a full 90% of nurse respondents were considering resigning to seek new careers. Another disturbing result: 71% of nurses with 15 or more years of experience – an invaluable resource to health systems – said they were on the verge of leaving. The stakes to relieve the stress on nurses have never been higher.

Modernizing industries depend heavily on emerging technologies. These technologies, like artificial intelligence, are primarily impactful for the manufacturing, energy, and transportation sectors. Enterprises are being transformed into a digital environment with emerging technologies. Every time the phrase "technology" is used, something new is always being developed or put into use that could benefit organizations. A few years ago, no one thought emerging technologies would soon take over our lives. The users' quick development has impacted the business ecosystem in wants and expectations for real-time interaction on these applications.

Converting written texts into their spoken forms is an essential problem in any text-to-speech (TTS) systems. However, building an effective text normalization solution for a real-world TTS system face two main challenges: (1) the semantic ambiguity of non-standard words (NSWs), e.g., numbers, dates, ranges, scores, abbreviations, and (2) transforming NSWs into pronounceable syllables, such as URL, email address, hashtag, and contact name. In this paper, we propose a new two-phase normalization approach to deal with these challenges. First, a model-based tagger is designed to detect NSWs. Then, depending on NSW types, a rule-based normalizer expands those NSWs into their final verbal forms. We conducted three empirical experiments for NSW detection using Conditional Random Fields (CRFs), BiLSTM-CNN-CRF, and BERT-BiGRU-CRF models on a manually annotated dataset including 5819 sentences extracted from Vietnamese news articles. In the second phase, we propose a forward lexicon-based maximum matching algorithm to split down the hashtag, email, URL, and contact name. The experimental results of the tagging phase show that the average F1 scores of the BiLSTM-CNN-CRF and CRF models are above 90.00%, reaching the highest F1 of 95.00% with the BERT-BiGRU-CRF model. Overall, our approach has low sentence error rates, at 8.15% with CRF and 7.11% with BiLSTM-CNN-CRF taggers, and only 6.67% with BERT-BiGRU-CRF tagger.

Conversational recommender systems (CRS) that are able to interact with users in natural language often utilize recommendation dialogs which were previously collected with the help of paired humans, where one plays the role of a seeker and the other as a recommender. These recommendation dialogs include items and entities that indicate the users' preferences. In order to precisely model the seekers' preferences and respond consistently, CRS typically rely on item and entity annotations. A recent example of such a dataset is INSPIRED, which consists of recommendation dialogs for sociable conversational recommendation, where items and entities were annotated using automatic keyword or pattern matching techniques. An analysis of this dataset unfortunately revealed that there is a substantial number of cases where items and entities were either wrongly annotated or annotations were missing at all. This leads to the question to what extent automatic techniques for annotations are effective. Moreover, it is important to study impact of annotation quality on the overall effectiveness of a CRS in terms of the quality of the system's responses. To study these aspects, we manually fixed the annotations in INSPIRED. We then evaluated the performance of several benchmark CRS using both versions of the dataset. Our analyses suggest that the improved version of the dataset, i.e., INSPIRED2, helped increase the performance of several benchmark CRS, emphasizing the importance of data quality both for end-to-end learning and retrieval-based approaches to conversational recommendation. We release our improved dataset (INSPIRED2) publicly at https://github.com/ahtsham58/INSPIRED2.

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.