LibRec 精选:CCF TPCI 的推荐系统专刊征稿

2019 年 1 月 12 日 LibRec智能推荐
LibRec 精选:CCF TPCI 的推荐系统专刊征稿

LibRec 精选

LibRec智能推荐 第 23 期(至2019.1.12),更新 13 篇精彩讨论内容。



【征稿】CCF Transactions on Pervasive Computing and Interaction Special Issue on Recommender Systems


【会议】UMAP 2019论文截稿时间:

Abstract: 2019年1月25日

Paper: 2019年2月1日



+ 个性化商品推荐

+ 打击虚假评论

+ 库存管理和销售预测

+ 自动客服和购物机器人

+ AI驱动的客户关系管理(CRM)系统

+ 视觉搜索:以图搜商品

+ 语音搜索


【招聘】TU Delf 在推荐系统领域的教职职位:


【总结】ACM RecSys 2018 参会总结:



1. Loss Aversion in Recommender Systems: Utilizing Negative User Preference to Improve Recommendation Quality

Bibek Paudel, Sandro Luck, Abraham Bernstein

Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems. This context is especially useful in scenarios where the cost of negative items is high for the users. We build upon existing machine-learning model to incorporate the contextual information provided by negative user preference.

2. Deep Item-based Collaborative Filtering for Sparse Implicit Feedback

Daniel A. Galron, Yuri M. Brovman, Jin Chung, Michal Wieja, Paul Wang

Techniques such as item-based collaborative filtering are used to model users' behavioral interactions with items and make recommendations from items that have similar behavioral patterns. We propose an objective function that optimizes a similarity measure between binary implicit feedback vectors between two items. Finally, we discuss the results of an A/B test that show marked improvement of the proposed technique over eBay's existing collaborative filtering recommender system.

3. Knowledge Representation Learning: A Quantitative Review

Yankai Lin, Xu Han, Ruobing Xie, Zhiyuan Liu, Maosong Sun

Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks. Afterwards, we extensively conduct and quantitative comparison and analysis of several typical KRL methods on three evaluation tasks of knowledge acquisition including knowledge graph completion, triple classification, and relation extraction. We also review the real-world applications of KRL, such as language modeling, question answering, information retrieval, and recommender systems.

4. Towards Finding Non-obvious Papers: An Analysis of Citation Recommender Systems

Haofeng Jia, Erik Saule

Our analysis shows the degrees of cited papers in the subgraph induced by the citations of a paper, called projection graph, follow a power law distribution. Existing popular methods are only good at finding the long tail papers, the ones that are highly connected to others. Results show that different methods are finding cited papers with widely different properties.

5. On hybrid modular recommendation systems for video streaming

Evripidis Tzamousis, Maria Papadopouli

The recommendation systems aim to improve the user engagement by recommending appropriate personalized content to users, exploiting information about their preferences. The enabler incorporates a variety of recommendation algorithms that span from collaborative filtering and content-based techniques to ones based on neural networks. A pilot web-based recommendation system was developed and tested in the production environment of a large telecom operator in Greece.

6. A Neural Network Based Explainable Recommender System

Jionghao Lin, Yiren Liu

Recommendation system could help the companies to persuade users to visit or consume at a particular place, which was based on many traditional methods such as the set of collaborative filtering algorithms. This paper proposed an integrated neural network based model which integrates rating scores prediction and explainable words generation. Based on the experimental results, this model presented lower RMSE compared with traditional methods, and generate the explanation of recommendation to convince customers to visit the recommended place.

7. Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation

Yun He, Haochen Chen, Ziwei Zhu, James Caverlee

We propose PsiRec, a novel user preference propagation recommender that incorporates pseudo-implicit feedback for enriching the original sparse implicit feedback dataset. Three of the unique characteristics of PsiRec are: (i) it views user-item interactions as a bipartite graph and models pseudo-implicit feedback from this perspective; (ii) its random walks-based approach extracts graph structure information from this bipartite graph, toward estimating pseudo-implicit feedback; and (iii) it adopts a Skip-gram inspired measure of confidence in pseudo-implicit feedback that captures the pointwise mutual information between users and items. This pseudo-implicit feedback is ultimately incorporated into a new latent factor model to estimate user preference in cases of extreme sparsity.







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