LibRec 精选：基于会话的推荐算法评估

推荐进展 第六期（更新至2018.5.20），本期更新推荐系统相关论文6篇，其中部分论文的摘要部分经句子抽取算法处理过。

1. Evaluation of Session-based Recommendation Algorithms.

Malte Ludewig, Dietmar Jannach

https://arxiv.org/abs/1803.09587 

In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or Fossil, as well as more simple methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today's more complex approaches based on deep neural networks.

2. Algorithms and Architecture for Real-time Recommendations at News UK

Dion Bailey, Tom Pajak, Daoud Clarke, Carlos Rodriguez

https://arxiv.org/abs/1709.05278

At News UK, there is a requirement to be able to quickly generate recommendations for users on news items as they are published. In this paper we describe a new algorithm for updating collaborative filtering models incrementally, and demonstrate its effectiveness on clickstream data from The Times. We also describe the architecture that allows recommendations to be generated on the fly, and how we have made each component scalable. The system is currently being used in production at News UK.

3. Deep Reinforcement Learning for Page-wise Recommendations

Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang

https://arxiv.org/abs/1805.02343v1

In real-world recommendations such as e-commerce, a typical interaction between the system and its users is -- users are recommended a page of items and provide feedback; and then the system recommends a new page of items. In this paper, we study the problem of page-wise recommendations aiming to address aforementioned two challenges simultaneously. In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users.

4. Collaborative Item Embedding Model for Implicit Feedback Data

ThaiBinh Nguyen, Kenro Aihara, Atsuhiro Takasu

https://arxiv.org/abs/1805.05005v1

One way to perform collaborative filtering is matrix factorization, which characterizes user preferences and item attributes using latent vectors. In this work, we propose a method to extract the relationships between items and embed them into the latent vectors of the factorization model. This combines two worlds: matrix factorization for collaborative filtering and item embedding, a similar concept to word embedding in language processing.

5. Mobile recommender systems: Identifying the major concepts

Elias Pimenidis, Nikolaos Polatidis, Haralambos Mouratidis

https://arxiv.org/abs/1805.02276v1

However, the use of mobile devices and the rapid growth of the internet and networking infrastructure has brought the necessity of using mobile recommender systems. The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved. This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain.

6. Extendable Neural Matrix Completion

Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis

https://arxiv.org/abs/1805.04912v1

Matrix completion is one of the key problems in signal processing and machine learning, with applications ranging from image processing and data gathering to classification and recommender systems. Recently, deep neural networks have been proposed as latent factor models for matrix completion and have achieved state-of-the-art performance. In this paper, we propose a deep two-branch neural network model for matrix completion.

The figure was taken from https://medium.com/the-graph/food-for-thought-from-london-the-future-of-recommender-systems-5620787d6dac