http://www3.eng.cam.ac.uk/~hj231/Papers/rostering.pdf

Staff scheduling and rostering: A review of applications, methods and models

With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.

Deep Learning Based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.

Deep Learning based Recommender System: A Survey and New Perspectives.

Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. To solve the above problem, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users» diverse interests, we also design an attention module in DKN to dynamically aggregate a user»s history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.

DKN: Deep Knowledge-Aware Network for News Recommendation

The Monte Carlo method.- Sampling from known distributions.- Pseudorandom number generators.- Variance reduction techniques.- Quasi-Monte Carlo constructions.- Using quasi-Monte Carlo constructions.- Using quasi-Monte Carlo in practice.- Financial applications.- Beyond numerical integration.- Review of algebra.- Error and variance analysis for Halton sequences.- References.- Index.

Monte Carlo and Quasi-Monte Carlo Sampling

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.

Embedding-based News Recommendation for Millions of Users

Contextual advertising is a textual advertising displayed within the content of a generic web page. Predicting the probability that users will click on ads plays a crucial role in contextual advertising because it influences ranking, filtering, placement, and pricing of ads. In this paper, we introduce a click-through rate prediction algorithm based on the learning-to-rank approach. Focusing on the fact that some of the past click data are noisy and ads are ranked as lists, we build a ranking model by using partial click logs and then a regression model on it. We evaluated this approach offline on a data set based on logs from an ad network. Our method is observed to achieve better results than other baselines in our three metrics.

CTR prediction for contextual advertising: learning-to-rank approach

An image stabilized system comprising an afocal telescopic system section, deflecting means and a relay lens system section arranged in this order from the front, said deflecting means including a plano-convex and a plano-concave lens mounted in mating rotatable relation to each other, one of which is fixedly secured to the instrument housing containing the optical system in coaxial alignment with said telescopic system and relay lens system, and the other is held in fixed spatial alignment with the original line of a sight axis by means of a gyro stabilizer coupled thereto, thereby provide for stabilization of an image at the focal plane against accidental angular displacement or motions of the instrument relative to the original line of sight axis.

Image stabilizing optical system having a variable prism

A network system includes an application service provider (ASP) which is connected to the Internet and executes an application, and a CPU resource provider which is connected to the Internet and provides a processing service to a particular computational part (e.g., computation intensive part) of the application, wherein: when requesting a job from the CPU resource provider, the application service provider (ASP) sends information about estimated computation time of the job to the CPU resource provider via the Internet; and the CPU resource provider assigns the job by correcting this estimated computation time based on the estimated computation time sent from the application service provider (ASP).

Network system, CPU resource provider, client apparatus, processing service providing method, and program

A method for demand planning of products. The method comprises the steps of constructing a configure-to-order operation/multiple building block environment; and, forecasting the demand of the building blocks within this environment for establishing an efficient supply chain management.

Akira Tajima

Papers

Embedding-based News Recommendation for Millions of Users

CTR prediction for contextual advertising: learning-to-rank approach

Image stabilizing optical system having a variable prism

Network system, CPU resource provider, client apparatus, processing service providing method, and program

Demand planning for configure-to-order and building blocks-based market environment