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With the tremendous growth of published news articles, a key issue is how to help users find diverse and interesting news stories. To this end, it is crucial to understand and build accurate profiles for both users and news articles. In this paper, we define a user profile based on (1) the set of entities she/he talked about it in her/his comments...
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Citations
... Traditional news recommendation methods include methods based on collaborative filtering [3][4][5][6][7] , contentbased methods [8] [9], and hybrid methods [10] [11], which generate user and item features from interaction matrices. For example, in scoring-related recommender systems, the interaction between users and items usually adopts collaborative filtering [12] [13]. ...
... Traditional news recommendation methods include methods based on collaborative filtering [3][4] [5], content-based methods [8][9] and hybrid methods [10] [11]. But collaborative filtering-based methods often suffer from cold-start problems because news items are often replaced. ...
Existing research usually utilizes side information such as social network or item attributes to improve the performance of collaborative filtering-based recommender systems. In this paper, the knowledge graph with user perception is used to acquire the source of side information. We proposed KGUPN to address the limitations of existing embedding-based and path-based knowledge graph-aware recommendation methods, an end-to-end framework that integrates knowledge graph and user awareness into scientific and technological news recommendation systems. KGUPN contains three main layers, which are the propagation representation layer, the contextual information layer and collaborative relation layer. The propagation representation layer improves the representation of an entity by recursively propagating embeddings from its neighbors (which can be users, news, or relationships) in the knowledge graph. The contextual information layer improves the representation of entities by encoding the behavioral information of entities appearing in the news. The collaborative relation layer complements the relationship between entities in the news knowledge graph. Experimental results on real-world datasets show that KGUPN significantly outperforms state-of-the-art baselines in scientific and technological news recommendation.
... Demartini (2011), Giachanou et al. (2014), Aktolga and Allan (2013), Aktolga (2014), Gamper (2011, 2012), and Meguebli et al. (2014a) considered diversifying the search results according to their sentiments. Aktolga (2014) considered diversification by time and opinionatedness. ...
... Aktolga (2014) considered diversification by time and opinionatedness. Gamper (2011, 2012) and Meguebli et al. (2014a) used the semantic similarity of documents (the Jaccard similarity of topic elements and (or) key concepts of documents) to obtain semantic diversity, which can be considered a form of aspect-based diversity (see Section 5.4.3). Kiritoshi and Ma (2014, 2016 addressed sentiment-based diversification of news, and they also considered a form of aspect-based diversification by using two measures "difference in factor coverage" and "difference in details". ...
... Similarly, Ma and Yoshikawa (2009) employed a clustered presentation of news to deliver diverse news reports on a certain news event. Meguebli et al. (2014a) and Kiritoshi and Ma (2014, 2016 implicitly considered aspects for diversifying news recommendations by exploiting semantic dissimilarity and 17 To frame (Entman 1993, p. 2) is to "select some aspects of a perceived reality and make them more salient in a communicating text, in such a way as to promote a particular problem definition, causal interpretation, moral evaluation, and/or treatment recommendation for the item described". Kiritoshi and Ma (2014, 2016) employed a "difference in details" measure to consider the differences in the amount of details of news articles. ...
Nowadays, individuals heavily rely on search engines for seeking information. The presence of information bubbles (filter bubbles and echo chambers) can threaten the effectiveness of these systems in providing unbiased information and damage healthy civic discourse and open-minded deliberation. In this paper, we propose a new paradigm for search that aims at mitigating the information bubble in the search. The paradigm, which we call perspective-based search (PBS), is based on the intuition that in a fair search the user should not be limited to the results corresponding to a specific perspective of the search topic. Briefly, in PBS, different perspectives of the search topic are identified and presented to the user and the user can select a perspective for the search results. In this paper, we focus on the paradigm itself, why it is an appropriate solution, and how it differs from other solutions. We raise new questions and call for research on the paradigm and on providing solutions for implementing its required components. We do not aim at providing any specific implementation for it, although we provide some hints on implementing it. We also provide a survey of the related concepts and methods and discuss their differences with PBS.
