LibRec 精选：基于LSTM的序列推荐实现（PyTorch）

LibRec 精选

推荐系统进展 第12期（更新至2018.8.26），更新 9 篇论文。

我一直以为人是慢慢变老的，其实不是，人是一瞬间变老的。-- 村上春树

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社交更新

1

ACM UMAP 2018 best paper: 
Schaffer et al., Easy to Please: Separating User Experience from Choice, https://dl.acm.org/citation.cfm?id=3209222

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【Florian Wilhelm's Blog】Multiplicative LSTM for sequence-based Recommenders （by PyTorch，附有源码和评估结果）: 
https://florianwilhelm.info/2018/08/multiplicative_LSTM_for_sequence_based_recos/

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【Pavel Kordik's Blog】Machine Learning for Recommender Systems - Part 2 (Deep Recommendation, Sequence Prediction, AutoML and Reinforcement Learning in Recommendation):
https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-2-deep-recommendation-sequence-prediction-automl-f134bc79d66b

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论文更新

1. Attainment Ratings for Graph-Query Recommendation

Hal Cooper, Garud Iyengar, Ching-Yung Lin

https://arxiv.org/abs/1808.05988v1

Recommender systems for video games have received relatively scant academic attention, despite the uniqueness of the medium and its data. We show that the use of implicit data that tracks user-game interactions and levels of attainment (e.g. We demonstrate the natural suitability of graph-query based recommendation for this purpose.

2. A Simple but Hard-to-Beat Baseline for Session-based Recommendations

Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, Xiangnan He

https://arxiv.org/abs/1808.05163v2

Convolutional Neural Networks (CNNs) models have been recently introduced in the domain of top-$N$ session-based recommendations. The proposed generative model attains state-of-the-art accuracy with less training time in the session-based recommendation task. It accordingly can be used as a powerful session-based recommendation baseline to beat in future, especially when there are long sequences of user feedback.

3. Neural Collaborative Ranking

Bo Song, Xin Yang, Yi Cao, Congfu Xu

https://arxiv.org/abs/1808.04957v1

With the unprecedented success of deep learning in computer vision and speech recognition, recently it has been a hot topic to bridge the gap between recommender systems and deep neural network. We combine our classification strategy with the recently proposed neural collaborative filtering framework, and propose a general collaborative ranking framework called Neural Network based Collaborative Ranking (NCR). We resort to a neural network architecture to model a user's pairwise preference between items, with the belief that neural network will effectively capture the latent structure of latent factors.

4. AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments

Dominik Kowald, Emanuel Lacic, Dieter Theiler, Elisabeth Lex

https://arxiv.org/abs/1808.04603v1

In this paper, we present preliminary results of AFEL-REC, a recommender system for social learning environments. Furthermore, AFEL-REC can cope with any kind of data that is present in social learning environments such as resource metadata, user interactions or social tags. This paper should be valuable for both researchers and practitioners interested in providing resource recommendations in social learning environments.

5. Automatic Playlist Continuation through a Composition of Collaborative Filters

Irene Teinemaa, Niek Tax, Carlos Bentes

https://arxiv.org/abs/1808.04288v1

The RecSys Challenge 2018 focused on automatic playlist continuation, i.e., the task was to recommend additional music tracks for playlists based on the playlist's title and/or a subset of the tracks that it already contains. The challenge is based on the Spotify Million Playlist Dataset (MPD), containing the tracks and the metadata from one million real-life playlists. This paper describes the automatic playlist continuation solution of team Latte, which is based on a composition of collaborative filters that each capture different aspects of a playlist, where the optimal combination of those collaborative filters is determined using a Tree-structured Parzen Estimator (TPE).

6. IceBreaker: Solving Cold Start Problem for Video Recommendation Engines

Yaman Kumar, Agniv Sharma, Abhigyan Khaund, Akash Kumar, Ponnurangam Kumaraguru, Rajiv Ratn Shah

https://arxiv.org/abs/1808.05636v1

Thus it becomes imperative for video recommendation engines such as Hulu to look for novel and innovative ways to recommend the newly added videos to their users. However, the problem with new videos is that they lack any sort of metadata and user interaction so as to be able to rate the videos for the consumers. The obtained results are encouraging and will impel the boundaries of research for multimedia based video recommendation systems.

7. Adversarial Personalized Ranking for Recommendation

Xiangnan He, Zhankui He, Xiaoyu Du, Tat-Seng Chua

https://arxiv.org/abs/1808.03908v1

Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). To enhance the robustness of a recommender model and thus improve its generalization performance, we propose a new optimization framework, namely Adversarial Personalized Ranking (APR).

8. Outer Product-based Neural Collaborative Filtering

Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua

https://arxiv.org/abs/1808.03912v1

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions.

9. A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care

Qiwei Han, Mengxin Ji, Inigo Martinez de Rituerto de Troya, Manas Gaur, Leid Zejnilovic

https://arxiv.org/abs/1808.03265v1

We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care. We define the matchmaking process for several distinct use cases given different levels of available information about patients. Then, we adopt a hybrid recommender system to present each patient a list of family doctor recommendations.