UX Team Of One @ IA Summit 2008

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How to Be a UX Team of One was presented by Leah Buley at the 2008 IA Summit in Miami, Florida. It shows techniques for generative design that can be used by solo user experience practitioners.

Business Formation

How to Be a UX Team of One
IA Summit, 2008

Leah Buley,

3 ideas for the team of one

Leah Buley,

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With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.

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최규민(pi.314) / kakao corp.(추천팀) --- 발표의 시작은? Music Streaming 서비스인 멜론과 카카오미니에 적용된 Toros 음악 추천 시스템의 다양한 추천 모델과 이를 활용한 추천 레시피에 대한 이야기를 합니다. 그리고 마무리는? 음악 추천을 위해 40~100차원 벡터로 모델링 된 (CB/CF) Latent Feature들을 다양한 조합으로 Visualization(t-SNE)하고 눈으로 탐색해 가면서 재미난 특징을 찾아보고자 합니다. "CB와 CF Feature 노래를 어떻게 표현했을까?”, "Popular노래와 Rare 한 노래의 Feature는?” “Feedback이 많은 유저와 적은 유저 차이는?” “팬덤이 있는 유저들은 어떤 모양일까?"

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Presentation of the Phd. thesis defense of Gabriel de Souza Pereira Moreira at Instituto Tecnológico de Aeronáutica (ITA), on Dec. 09, 2019, in São José dos Campos, Brazil. Abstract: Recommender systems have been increasingly popular in assisting users with their choices, thus enhancing their engagement and overall satisfaction with online services. Since the last decade, recommender systems became a topic of increasing interest among machine learning, human-computer interaction, and information retrieval researchers. News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated and irrelevant to most readers. News readers exhibit more unstable consumption behavior than users in other domains such as entertainment. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous, with no past behavior tracked. Since 2016, Deep Learning methods and techniques have been explored in Recommender Systems research. In general, they can be divided into methods for: Deep Collaborative Filtering, Learning Item Embeddings, Session-based Recommendations using Recurrent Neural Networks (RNN), and Feature Extraction from Items' Unstructured Data such as text, images, audio, and video. The main contribution of this research was named CHAMELEON a meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks. As information about users' past interactions is scarce in the news domain, information such as the user context (e.g., time, location, device, the sequence of clicks within the session), static and dynamic article features like the article textual content and its popularity and recency, are explicitly modeled in a hybrid session-based recommendation approach using RNNs. The recommendation task addressed in this work is the next-item prediction for user sessions, i.e., "what is the next most likely article a user might read in a session?". A temporal offline evaluation is used for a realistic offline evaluation of such task, considering factors that affect global readership interests like popularity, recency, and seasonality. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based algorithms.

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Nick will explore the best practices of user experience by reviewing some of the most popular and highly trafficked websites today such as eBay, Amazon, Toyota, Flickr, Twitter, Netflix and more. Nick will identify and explain both good an bad experiences on these sites on the merits of visual design, information architecture, interaction, and ease of use. If there is time we will open the floor for audience submissions and to provide quick feedback and areas of improvement.

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Wireframes beyond the basics, not for the weak at heart. In this panel, three experienced designers will share their tried and true tips for making wireframes really work. We'll talk about how to sketch a wireframe on the fly to demonstrate an idea and how to create a standalone wireframe deliverable; when to show a concept and when to describe nitty-gritty detail; how to make a narrative wireframe and how to make a specification wireframe. And best of all, we'll show you plenty of examples.

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The mighty user research toolkit is packed with techniques. It can do everything from blue sky innovation research, to need-finding and requirements gathering, to product validation and testing. But many teams don't exploit the full toolkit, sticking instead to one side or the other of the quant versus qual divide, or returning again and again to that tired old workhorse—usability testing. This presentation is a primer on the range of research methods available, and a guide for determining which is the best technique for what you’re trying to learn now (and for your budget).

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UX Team Of One @ IA Summit 2008