HI2Rec: Exploring Knowledge in Heterogeneous Information for Movie Recommendation

Online movies’ recommender systems aim to address the information explosion of movies and make the personalized recommendation for users. Recently, knowledge graphs have been proven to be highly effective to recommender systems, because they are able to fuse various recommendation models and can handle the issues of data sparsity and cold start to improve recommendation performance. However, less consideration is given to the information about the user’s properties than the item’s properties in the existing knowledge graph recommendation methods, which leads to some limitations in the recommendation results. In this paper, we propose HI2Rec, which integrates multiple information to learn the user’s and item’s vector representations for top-N recommendation to address the above-mentioned issues. We extract the movie-related information from the Linked Open Data and then leverage the knowledge representation learning approach to embed this information as well as real-world datasets’ information of recommender systems to a unified vector space. These vector representations are further calculated to generate a preliminary recommendation list. Finally, we utilize a collaborative filter approach to generate a precision recommendation list. The experimental results on the real-world datasets demonstrate that HI2Rec gives substantial performance improvements against the state-of-the-art recommendation models.

[1]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[2]  Zhen Wang,et al.  Knowledge Graph Embedding by Translating on Hyperplanes , 2014, AAAI.

[3]  Danqi Chen,et al.  Reasoning With Neural Tensor Networks for Knowledge Base Completion , 2013, NIPS.

[4]  Jason Weston,et al.  Learning Structured Embeddings of Knowledge Bases , 2011, AAAI.

[5]  Jun Zhao,et al.  Knowledge Graph Embedding via Dynamic Mapping Matrix , 2015, ACL.

[6]  MengChu Zhou,et al.  A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[7]  Minyi Guo,et al.  DKN: Deep Knowledge-Aware Network for News Recommendation , 2018, WWW.

[8]  MengChu Zhou,et al.  Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data , 2018, IEEE Transactions on Cybernetics.

[9]  John Riedl,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[10]  MengChu Zhou,et al.  An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications , 2018, IEEE Transactions on Industrial Informatics.

[11]  William W. Cohen,et al.  Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach , 2016, RecSys.

[12]  Paolo Tomeo,et al.  A SPRank : Semantic Path-based Ranking for Top-N Recommendations using Linked Open Data , 2016 .

[13]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

[14]  Zhiyuan Liu,et al.  Learning Entity and Relation Embeddings for Knowledge Graph Completion , 2015, AAAI.

[15]  Atsuhiro Takasu,et al.  Collaborative Item Embedding Model for Implicit Feedback Data , 2017, ICWE.

[16]  Nicholas Jing Yuan,et al.  Collaborative Knowledge Base Embedding for Recommender Systems , 2016, KDD.

[17]  Minyi Guo,et al.  SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction , 2017, WSDM.

[18]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[19]  MengChu Zhou,et al.  Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[20]  Yueting Zhuang,et al.  User Preference Learning for Online Social Recommendation , 2016, IEEE Transactions on Knowledge and Data Engineering.

[21]  Yueting Zhuang,et al.  Social-Aware Movie Recommendation via Multimodal Network Learning , 2018, IEEE Transactions on Multimedia.

[22]  Nicolas Le Roux,et al.  A latent factor model for highly multi-relational data , 2012, NIPS.

[23]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[24]  Tommaso Di Noia,et al.  Top-N recommendations from implicit feedback leveraging linked open data , 2013, IIR.

[25]  Yizhou Sun,et al.  Personalized entity recommendation: a heterogeneous information network approach , 2014, WSDM.

[26]  Jason Weston,et al.  A semantic matching energy function for learning with multi-relational data , 2013, Machine Learning.

[27]  MengChu Zhou,et al.  An Effective Scheme for QoS Estimation via Alternating Direction Method-Based Matrix Factorization , 2019, IEEE Transactions on Services Computing.