Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Embedding-based News Recommendation for Millions of Users

Abstract: It is necessary to understand the content of articles and user preferences to make effective news recommendations. While ID-based methods, such as collaborative filtering and low-rank factorization, are well known for making recommendations, they are not suitable for news recommendations because candidate articles expire quickly and are replaced with new ones within short spans of time. Word-based methods, which are often used in information retrieval settings, are good candidates in terms of system performance but have issues such as their ability to cope with synonyms and orthographical variants and define "queries" from users' historical activities. This paper proposes an embedding-based method to use distributed representations in a three step end-to-end manner: (i) start with distributed representations of articles based on a variant of a denoising autoencoder, (ii) generate user representations by using a recurrent neural network (RNN) with browsing histories as input sequences, and (iii) match and list articles for users based on inner-product operations by taking system performance into consideration. The proposed method performed well in an experimental offline evaluation using past access data on Yahoo! JAPAN's homepage. We implemented it on our actual news distribution system based on these experimental results and compared its online performance with a method that was conventionally incorporated into the system. As a result, the click-through rate (CTR) improved by 23% and the total duration improved by 10%, compared with the conventionally incorporated method. Services that incorporated the method we propose are already open to all users and provide recommendations to over ten million individual users per day who make billions of accesses per month.

Tag-based social interest discovery

Abstract: The success and popularity of social network systems, such as del.icio.us, Facebook, MySpace, and YouTube, have generated many interesting and challenging problems to the research community. Among others, discovering social interests shared by groups of users is very important because it helps to connect people with common interests and encourages people to contribute and share more contents. The main challenge to solving this problem comes from the difficulty of detecting and representing the interest of the users. The existing approaches are all based on the online connections of users and so unable to identify the common interest of users who have no online connections.In this paper, we propose a novel social interest discovery approach based on user-generated tags. Our approach is motivated by the key observation that in a social network, human users tend to use descriptive tags to annotate the contents that they are interested in. Our analysis on a large amount of real-world traces reveals that in general, user-generated tags are consistent with the web content they are attached to, while more concise and closer to the understanding and judgments of human users about the content. Thus, patterns of frequent co-occurrences of user tags can be used to characterize and capture topics of user interests. We have developed an Internet Social Interest Discovery system, ISID, to discover the common user interests and cluster users and their saved URLs by different interest topics. Our evaluation shows that ISID can effectively cluster similar documents by interest topics and discover user communities with common interests no matter if they have any online connections.

Impingement with total hip replacement.

Abstract: Impingement is a cause of poor outcomes of prosthetic hip arthroplasty; it can lead to instability, accelerated wear, and unexplained pain. Impingement is influenced by prosthetic design, component position, biomechanical factors, and patient variables. Evidence linking impingement to dislocation and accelerated wear comes from implant retrieval studies. Operative principles that maximize an impingement-free range of motion include correct combined acetabular and femoral anteversion and an optimal head-neck ratio. Operative techniques for preventing impingement include medialization of the cup to avoid component impingement and restoration of hip offset and length to avoid osseous impingement.

Rhabdomyolysis: a review of the literature.

Abstract: Rhabdomyolysis is a potentially life-threatening syndrome that can develop from a variety of causes; the classic findings of muscular aches, weakness and tea-coloured urine are non-specific and may not always be present. The diagnosis therefore rests upon the presence of a high level of suspicion of any abnormal laboratory values in the mind of the treating physician. An elevated plasma creatine kinase (CK) level is the most sensitive laboratory finding pertaining to muscle injury; whereas hyperkalaemia, acute renal failure and compartment syndrome represent the major life-threatening complications. The management of the condition includes prompt and aggressive fluid resuscitation, elimination of the causative agents and treatment and prevention of any complications that may ensue. The objective of this review is to describe the aetiological spectrum and pathophysiology of rhabdomyolysis, the clinical and biological consequences of this syndrome and to provide an appraisal of the current data available in order to facilitate the prevention, early diagnosis and prompt management of this condition.

A Contextual-Bandit Approach to Personalized News Article Recommendation

Abstract: Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.