LibRec 精选：从单词Embeddings生成单词

LibRec 精选

推荐系统进展 第 15 期（更新至2018.10.10），更新 7 篇论文。

世界上唯一不用努力，就能得到的只有年龄！

1

社交更新

1

RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising，链接：https://github.com/criteo-research/reco-gym

2

Machine Learning with Kaggle Kernels – Part 1: https://blog.shahinrostami.com/2018/10/machine-learning-with-kaggle-kernels-part-1/

3

Generating Words from Embeddings，链接：https://rajatvd.github.io/Generating-Words-From-Embeddings/，源码：https://github.com/rajatvd/WordGenerator

2

论文更新

1. Neural Educational Recommendation Engine (NERE)

Moin Nadeem, Dustin Stansbury, Shane Mooney

https://arxiv.org/abs/1809.08922v1

Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space.

2. Learning Recommender Systems from Multi-Behavior Data

Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Depeng jin

https://arxiv.org/abs/1809.08161v1

Most existing recommender systems leverage the data of one type of user behaviors only, such as the purchase behavior in E-commerce that is directly related to the business KPI (Key Performance Indicator) of conversion rate. In this work, we contribute a novel solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from multiple types of user behaviors. Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data.

3. Adversarial Training Towards Robust Multimedia Recommender System

Jinhui Tang, Xiangnan He, Xiaoyu Du, Fajie Yuan, Qi Tian, Tat-Seng Chua

https://arxiv.org/abs/1809.07062v1

To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning.

4. NAIS: Neural Attentive Item Similarity Model for Recommendation

Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, Tat-Seng Chua

https://arxiv.org/abs/1809.07053v1

In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF. Compared to the state-of-the-art item-based CF method Factored Item Similarity Model (FISM), our NAIS has stronger representation power with only a few additional parameters brought by the attention network.

5. Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System

Jiaxi Tang, Ke Wang

https://arxiv.org/abs/1809.07428v1

We propose a novel way to train ranking models, such as recommender systems, that are both effective and efficient. The student model achieves a similar ranking performance to that of the large teacher model, but its smaller model size makes the online inference more efficient. RD is flexible because it is orthogonal to the choices of ranking models for the teacher and student.

6. Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks

Farzad Eskandanian, Bamshad Mobasher

https://arxiv.org/abs/1810.00272v1

In many domains, however, the tastes and preferences of users change over time due to a variety of factors and recommender systems should capture these dynamics in user preferences in order to remain tuned to the most current interests of users. In this work we present a recommendation framework based on Hidden Markov Models (HMM) which takes into account the dynamics of user preferences. In the second approach the HMM is used directly to generate recommendations taking into account the identified change points.

7. Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence

Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu

https://arxiv.org/abs/1809.10770v1

The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation services. To cope with these challenges, we propose a novel autoencoder-based model to learn the non-linear user-POI relations, namely \textit{SAE-NAD}, which consists of a self-attentive encoder (SAE) and a neighbor-aware decoder (NAD). In particular, unlike previous works equally treat users' checked-in POIs, our self-attentive encoder adaptively differentiates the user preference degrees in multiple aspects, by adopting a multi-dimensional attention mechanism.