add several top conf papers

cubean · Dec 19, 2019 · 5f77d96 · 5f77d96
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diff --git a/02-Classical RS/2018-Spectral Collaborative Filtering-RecSys.pdf b/02-Classical RS/2018-Spectral Collaborative Filtering-RecSys.pdf
diff --git a/02-Classical RS/2019-Neural Graph Collaborative Filtering-SIGIR.pdf b/02-Classical RS/2019-Neural Graph Collaborative Filtering-SIGIR.pdf
diff --git a/03-Social RS/2013-Exploiting Local and Global Social Context for Recommendation-AAAI.pdf b/03-Social RS/2013-Exploiting Local and Global Social Context for Recommendation-AAAI.pdf
diff --git a/03-Social RS/2016-Recommendation with Social Dimensions-AAAI.pdf b/03-Social RS/2016-Recommendation with Social Dimensions-AAAI.pdf
diff --git a/03-Social RS/2017-Additive co-clustering with social influence for recommendation-RecSys.pdf b/03-Social RS/2017-Additive co-clustering with social influence for recommendation-RecSys.pdf
diff --git a/...ed RS/2019-CosRec-2D Convolutional Neural Networks for Sequential Recommendation-CIKM.pdf b/...ed RS/2019-CosRec-2D Convolutional Neural Networks for Sequential Recommendation-CIKM.pdf
diff --git a/... of Representation Learning and Matching Function Learning in Recommender System-AAAI.pdf b/... of Representation Learning and Matching Function Learning in Recommender System-AAAI.pdf
diff --git a/...d RS/2019-Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation-WWW.pdf b/...d RS/2019-Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation-WWW.pdf
diff --git a/10-CTR Prediction for RS/README.md b/10-CTR Prediction for RS/README.md
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diff --git a/README.md b/README.md
@@ -140,10 +140,14 @@ the user with the recommendations, but also make the user aware why such items a
 
 * Zhang et al. **Metric Factorization: Recommendation beyond Matrix Factorization.** 2018.
 
+* Lei et al. **Spectral Collaborative Filtering.** RecSys, 2018.
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 * Chen et al. **Collaborative Similarity Embedding for Recommender Systems.** arXiv, 2019.
 
 * Chuan et al. **Heterogeneous Information Network Embedding for Recommendation.** TKDE, 2019.
 
+* Xiang et al. **Neural Graph Collaborative Filtering.** SIGIR, 2019.
+
 
 
 ## Social Recommender System
@@ -166,6 +170,8 @@ the user with the recommendations, but also make the user aware why such items a
 
 * Yang et al. **Social collaborative filtering by trust.** IJCAI, 2013.
 
+* Jiliang et al. **Exploiting Local and Global Social Context for Recommendation.** IJCAI, 2013.
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 * Zhao et al. **Leveraging social connections to improve personalized ranking for collaborative filtering.** CIKM, 2014.
 
 * Chen et al. **Context-aware collaborative topic regression with social matrix factorization for recommender systems.** AAAI, 2014.
@@ -174,6 +180,8 @@ the user with the recommendations, but also make the user aware why such items a
 
 * Wang et al. **Social recommendation with strong and weak ties.** CIKM, 2016.
 
+* Jiliang et al. **Recommendation with Social Dimensions.** AAAI, 2016.
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 * Li et al. **Social recommendation using Euclidean embedding.** IJCNN, 2017.
 
 * Zhang et al. **Collaborative User Network Embedding for Social Recommender Systems.** SDM, 2017.
@@ -184,10 +192,14 @@ the user with the recommendations, but also make the user aware why such items a
 
 * Rafailidis et al. **Learning to Rank with Trust and Distrust in Recommender Systems.** RecSys, 2017.
 
+* Xixi et al. **Additive Co-Clustering with Social Influence for Recommendation.** RecSys, 2017.
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 * Zhao et al. **Collaborative Filtering with Social Local Models.** ICDM, 2017.
 
 * Wang et al. **Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation.** AAAI, 2018.
 
+* Wenqi et al. **Deep Modeling of Social Relations for Recommendation.** AAAI, 2018
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 * Xuying et al. **Personalized Privacy-Preserving Social Recommendation.** AAAI,2018.
 
 * Wen et al. **Network embedding based recommendation method in social networks.** WWW Poster, 2018.
@@ -283,6 +295,8 @@ the user with the recommendations, but also make the user aware why such items a
 
 * Wu et al. **Session-based Recommendation with Graph Neural Networks.** AAAI, 2019.
 
+* Zhi-Hong et al. **DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System.** AAAI, 2019.
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 * Zeping et al. **Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation.** IJCAI, 2019.
 
 * Dong Xi et al. **BPAM: Recommendation Based on BP Neural Network with Attention Mechanism.** IJCAI, 2019.
@@ -299,6 +313,11 @@ the user with the recommendations, but also make the user aware why such items a
 
 * Jiani et al. **STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems.** IJCAI, 2019.
 
+* An et al. **CosRec: 2D Convolutional Neural Networks for Sequential Recommendation.** CIKM, 2019.
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+* Hongwei et al. **Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation.** WWW, 2019.
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+
 
 ## Cold Start Problem in Recommender System