The reduction enables the use of all recent algorithmic advances (e.g.

Explaining the output of a complex system, such as a Recommender System (RS), is becoming of utmost importance for both users and companies. In this paper we explore the idea that personalized explanations can be learned as recommendation themselves. There are plenty of online services where users can upload some photos, in addition to rating items. We assume that users take these photos to reinforce or justify their opinions about the items. For this reason we try to predict what photo a user would take of an item, because that image is the argument that can best convince her of the qualities of the item. In this sense, an RS can explain its results and, therefore, increase its reliability. Furthermore, once we have a model to predict attractive images for users, we can estimate their distribution. The paper includes a formal framework that estimates the authorship probability for a given pair (user, photo). To illustrate the proposal, we use data gathered from TripAdvisor containing the reviews (with photos) of restaurants in six cities of different sizes. Keywords: Recommender Systems, Personalization, Explainability, Photo, Collaborative 1. Introduction Explainable Artificial Intelligence (XAI) is becoming an important area of interest since explainability is increasingly necessary to meet stakeholder demands. In particular, the General Data Protection Regulation (GDPR) [29] of the European Union demands transparency in systems that take decisions affecting people, making explanations more needed than ever. Additionally, explanations may help increase the trust of users in AI algorithms, since people rely not only on their efficacy but also on the degree of understanding of the process they follow. Since they provide suggestions to users, explainability plays an important role on them.

Neural Information Processing Systems http://nips.cc/

TripAdvisor has entered into a partnership with Perplexity to offer a human touch to the AI company's hotel information. Through this deal, listings for hotel searches on Perplexity will now include summaries of information from TripAdvisor explaining why they were included in the results. Ratings, perks and images from TripAdvisor will also appear on Perplexity. "From the Tripadvisor side, they provide an up-to-date trustworthy source of information that we sync regularly," Perplexity cofounder Johnny Ho told The Verge. "On the fly, we'll index and retrieve the right results depending on the user intent of the query."

Online rating systems are often used in numerous web or mobile applications, e.g., Amazon and TripAdvisor, to assess the ground-truth quality of products. Due to herding effects, the aggregation of historical ratings (or historical collective opinion) can significantly influence subsequent ratings, leading to misleading and erroneous assessments. We study how to manage product ratings via rating aggregation rules and shortlisted representative reviews, for the purpose of correcting the assessment error. We first develop a mathematical model to characterize important factors of herding effects in product ratings. We then identify sufficient conditions (via the stochastic approximation theory), under which the historical collective opinion converges to the ground-truth collective opinion of the whole user population. These conditions identify a class of rating aggregation rules and review selection mechanisms that can reveal the ground-truth product quality. We also quantify the speed of convergence (via the martingale theory), which reflects the efficiency of rating aggregation rules and review selection mechanisms. We prove that the herding effects slow down the speed of convergence while an accurate review selection mechanism can speed it up. We also study the speed of convergence numerically and reveal trade-offs in selecting rating aggregation rules and review selection mechanisms. To show the utility of our framework, we design a maximum likelihood algorithm to infer model parameters from ratings, and conduct experiments on rating datasets from Amazon and TripAdvisor. We show that proper recency aware rating aggregation rules can improve the speed of convergence in Amazon and TripAdvisor by 41% and 62% respectively.

Compliance with the European Union's Platform-to-Business (P2B) Regulation is challenging for online platforms, and assessing their compliance can be difficult for public authorities. This is partly due to the lack of automated tools for assessing the information (e.g., software documentation) platforms provide concerning ranking transparency. Our study tackles this issue in two ways. First, we empirically evaluate the compliance of six major platforms (Amazon, Bing, Booking, Google, Tripadvisor, and Yahoo), revealing substantial differences in their documentation. Second, we introduce and test automated compliance assessment tools based on ChatGPT and information retrieval technology. These tools are evaluated against human judgments, showing promising results as reliable proxies for compliance assessments. Our findings could help enhance regulatory compliance and align with the United Nations Sustainable Development Goal 10.3, which seeks to reduce inequality, including business disparities, on these platforms.

The four-star hotel in Kraków in Poland, the review says, is "excellent", a "short walk from the main square" and boasts a "first-rate" spa and fitness centre. A less positive review describes it as "small, cramped and outdated" with "lumpy" pillows. But then a family who stayed said they were made to feel "instantly welcome". The truth is that none of those reviews are real. They were generated in seconds by the free-to-use artificial intelligence tool ChatGPT.

We believe that we are better together, and at Tripadvisor we welcome you for who you are. Our workplace is for everyone, as is our people powered platform. At Tripadvisor, we want you to bring your unique identities, abilities, and experiences, so we can collectively revolutionize travel and together find the good out there. The Tripadvisor Data Science Team is looking for an exceptional experienced individual to help lead the design and building next generation NLP based systems that model how travelers interact with the various products on the site. Such a model will allow a significant improvement in our ability to offer to travelers the broadest, most relevant travel products (e.g., tell me the most family-friendly summertime activities in Paris). This individual will also have the opportunity to lead other projects on the team, including those related to text and image processing.

We believe that we are better together, and at Tripadvisor we welcome you for who you are. Our workplace is for everyone, as is our people powered platform. At Tripadvisor, we want you to bring your unique perspective and experiences, so we can collectively revolutionize travel and together find the good out there. Our team is building the Machine Learning Platform for all Data and ML scientists across Tripadvisor. Our mission is to make data scientists more productive and to enable broader and deeper utilization of machine learning techniques to help improving the business performance.

We believe that we are better together, and at Tripadvisor we welcome you for who you are. Our workplace is for everyone, as is our people powered platform. At Tripadvisor, we want you to bring your unique identities, abilities, and experiences, so we can collectively revolutionize travel and together find the good out there. Tripadvisor is looking for an experienced ML II to join our dynamic and fast paced Machine Learning Team on our Viator brand, home to over 300K bookable experiences worldwide. As a member of this team, you will have the opportunity to work on a wide range of complex and interesting business problems including: recommender systems, NLP/NLU, computer vision, online advertising and targeting, ranking algorithms, and more.