Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs

Abstract

Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information. Many news articles describe the occurrence of specific events and named entities including people, places or objects. In this paper, we propose a graph traversal algorithm as well as a novel weighting scheme for cold-start content based news recommendation utilizing these named entities. Seeking to create a higher degree of user-specific relevance, our algorithm computes the shortest distance between named entities, across news articles, over a large knowledge graph. Moreover, we have created a new human annotated data set for evaluating content based news recommendation systems. Experimental results show our method is suitable to tackle the hard coldstart problem and it produces stronger Pearson correlation to human similarity scores than other cold-start methods. Our method is also complementary and a combination with the conventional cold-start recommendation methods may yield significant performance gains. The dataset, CNRec, is available at: https://github.com/kevinj22/CNRec

A Survey on Knowledge Graph-Based Recommender Systems

“…serve as edges. After obtaining the embeddings of entities in the graph, the user's preference can be calculated with Equation 1, or by further KDD 2016 entity2rec [66] RecSys 2017 ECFKG [67] Algorithms 2018 SHINE [68] WSDM 2018 DKN [48] WWW 2018 KSR [44] SIGIR 2018 CFKG [13] SIGIR 2018 KTGAN [69] ICDM 2018 KTUP [70] WWW 2019 MKR [45] WWW 2019 DKFM [71] WWW 2019 SED [72] WWW 2019 RCF [73] SIGIR 2019 BEM [74] CIKM 2019 Hete-MF [75] IJCAI 2013 HeteRec [76] RecSys 2013 HeteRec p [77] WSDM 2014 Hete-CF [78] ICDM 2014 SemRec [79] CIKM 2015 ProPPR [80] RecSys 2016 FMG [3] KDD 2017 MCRec [1] KDD 2018 RKGE [81] RecSys 2018 HERec [82] TKDE 2019 KPRN [83] AAAI 2019 RuleRec [84] WWW 2019 PGPR [85] SIGIR 2019 EIUM [86] MM 2019 Ekar [87] arXiv 2019 RippleNet [14] CIKM 2018 RippleNet-agg [88] TOIS 2019 KGCN [89] WWW 2019 KGAT [90] KDD 2019 KGCN-LS [91] KDD 2019 AKUPM [92] KDD 2019 KNI [93] KDD 2019 IntentGC [94] KDD 2019 RCoLM [95] IEEE Access 2019 AKGE [96] arXiv 2019 considering the relation embedding in the graph via…”

“…Embedding-based Methods. Most embedding-based methods [2], [44], [45], [48], [69], [70], [72], [73], [74] build KGs with multiple types of item side information to enrich the representation of items, and such information can be used to model the user representation more precisely. Some models [13], [66], [67], [68], [71] build user-item graphs by introducing users into the graph, which can directly model the user preference.…”

A survey on knowledge-aware news recommender systems

“…In contrast to the previous models, SED, the entity shortest distance over knowledge graphs algorithm proposed by Joseph and Jiang [83], defines item-item similarity as the shortest distance between the subgraphs consisting of named entities extracted from news articles. The approach is threefold [83]. Firstly, all named entities contained in every news article are extracted and linked to the corresponding nodes in a knowledge graph.…”

“…Lastly, the similarity between the two articles is computed as the pair-wise shortest distance over the union of their subgraphs [83], as shown in Eq. (46).…”

Personalized News Recommendation: Methods and Challenges

“…Trevisiol et al [144] proposed to build a browsing graph from the news browsing histories of users on Yahoo News. Joseph et al [63] proposed to represent users by regarding the clicked news as subgraphs of a knowledge graph, which are constructed via entity linking. These methods can consider the high-order information on graphs to help understand user behaviors, which can improve user modeling.…”