T r N ews: Heterogeneous User-Interest Transfer Learning for News Recommendation (2021)

Abstract: We investigate how to solve the cross-corpus news recommendation for unseen users in the future. This is a problem where traditional content-based recommendation techniques often fail. Luckily, in real-world recommendation services, some publisher (e.g., Daily news) may have accumulated a large corpus with lots of consumers which can be used for a newly deployed publisher (e.g., Political news). To take advantage of the existing corpus, we propose a transfer learning model (dubbed as TrNews) for news recommendation to transfer the knowledge from a source corpus to a target corpus. To tackle the heterogeneity of different user interests and of different word distributions across corpora, we design a translator-based transfer-learning strategy to learn a representation mapping between source and target corpora. The learned translator can be used to generate representations for unseen users in the future. We show through experiments on real-world datasets that TrNews is better than various baselines in terms of four metrics. We also show that our translator is effective among existing transfer strategies.

## introduction news recommendation is key to satisfying users' information need for online services. some news articles, such as breaking news, are manually selected by publishers and displayed for all users. a huge number of news articles generated everyday make it impossible for editors and users to read through all of them, raising the issue of information overload. online news platforms provide a service of personalized news recommendation by learning from the past reading history of users, e.g., @xcite , yahoo @xcite , and bing news @xcite . when a new user uses the system (cold-start users) or a new article is just created (cold-start items), there are too few observations for them to train a reliable recommender system. contentbased techniques exploit the content information of news (e.g., words and tags) and hence new articles can be recommended to existing users @xcite . content-based recommendation, however, suffers from the issue of data sparsity since there is no reading history for them to be used to build a profile @xcite . transfer learning is a common technique for alleviating the issues of data sparsity @xcite @xcite . a user may have access to many websites such as twitter.com and youtube.com @xcite , and consume different categories of products such as movies and books @xcite . in this case, transfer learning approaches can recommend articles to a new user in the target domain by exploiting knowledge from the relevant source domains for this new user. a technical challenge for transfer learning approaches is that user interests are quite different across domains (corpora). for example, users do not use twitter for the same purpose. a user may follow up on news about "donald trump" because she supports republican party (in the political news domain), while she may follow up account @tay-lorswift13 ("taylor swift") because she loves music (in the entertainment news domain). another challenge is that the word distribution and feature space are different across domains. for example, vocabularies are different for describing political news and entertainment news. an illustration is depicted in figure 1 . as a result, the user profile computed from her news history is heterogeneous across domains. several strategies have been proposed for heterogeneous transfer learning @xcite . the transferable contextual bandit (tcb) @xcite learns a translation matrix to translate target feature examples to the source feature space. this linear mapping strategy is also used in collaborative cross networks (conet) @xcite and deep dual transfer cross domain recommendation (d-dtcdr) @xcite . to capture complex relations between source and target domains, some nonlinear mapping strategy is considered in the embedding and mapping cross-domain recommendation (emcdr) @xcite which learns a supervised regression between source and target factors using a multilayer perceptron (mlp). since aligned examples between source and target domains are limited, they may face the overfitting issues. to tackle challenges of heterogeneous user interests and limited aligned data between domains, we propose a novel transfer learning model (trnews) for cross-corpora news recommendation. trnews builds a bridge between two base networks (one for each corpus, see section 3.1.1) through the proposed translator-based transfer strategy. the translator in trnews captures the relations between source and target domains by learning a nonlinear mapping between them (section 3.2). the heterogeneity is alleviated by translating user interests across corpora. trnews uses the translator to transfer knowledge between source and target networks. trnews alleviates the limited data in a way of alternating training (section 3.3). the learned translator is used to infer the representations of unseen users in the future (section 3.4). by "translating" the source representation of a user to the target domain, trnews offers an easy solution to create unseen users' target representations. trnews outperforms the state-of-the-art recommendation methods on four real-world datasets in terms of four metrics (section 4.2), while having an explanation advantage by allowing the visualization of the importance of each news article in the history to the future news (section 4.4). ## related work content recommendation content-based recommendation exploits the content information about items (e.g., news title and article body @xcite @xcite @xcite , tag, vlog @xcite ), builds a profile for each user, and then matches users to items @xcite @xcite . it is effective for items with content or auxiliary information but suffers from the issues of data sparsity for users. dct @xcite constructs a user-user similarity matrix from user demographic features including gender, age, occupation, and location @xcite . nt-mf @xcite constructs a useruser similarity matrix from twitter texts. browse-graph @xcite addresses the freshly news recommendation by constructing a graph using url links between web pages. nac (rafailidis and crestani, 2019) transfers from multiple source domains through the attention mechanism. pdms @xcite assumes that there are many recommender models available to select items for a user, and introduces a multi-armed bandit for model selection. llae @xcite ) needs a social network as side information for cold-start users. different from the aforementioned works, we aim to recommending news to unseen users by transferring knowledge from a source domain to a target domain. transfer learning transfer learning aims at improving the performance of a target domain by exploiting knowledge from source domains @xcite . a special setting is domain adaptation where a source domain provides labeled training examples while the target domain provides instances on which the model is meant to be deployed @xcite . the coordinate system transfer (cst) @xcite firstly learns the principle coordinate of users in the source domain, and then transfers it to the target domain in the way of warm-start initialization. this is equivalent to an identity mapping from users' source representations to their corresponding target representations. tcb @xcite learns a linear mapping to translate target feature examples to the source feature space because there are many labelled data in the source domain. this linear strategy is also used in conet @xcite and ddtcdr (li and tuzhilin, 2020) which transforms the source representations to the target domain by a translation matrix. nonlinear mapping strategy @xcite @xcite is to learn a supervised mapping function between source and target latent factors by using neural networks. sscdr @xcite extends them to the semi-supervised mapping setting. our translator is general to accommodate these identity, linear, and nonlinear transfer-learning strategies. ## conclusion we investigate the cross-domain news recommendation via transfer learning. the experiments on real-word datasets demonstrate the necessity of tackling heterogeneity of user interests and word distributions across domains. our trnews model and its translator component are effective to transfer knowledge from the source network to the target network. we also shows that it is beneficial to learn a mapping from the source domain to the target domain even when only a small amount of aligned examples are available. in future works, we will focus on preserving the privacy of the source domain when we transfer its knowledge.