Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation (2024)

Abstract: Recent neural news recommenders (NNRs) extend content-based recommendation (1) by aligning additional aspects (e.g., topic, sentiment) between candidate news and user history or (2) by diversifying recommendations w.r.t. these aspects. This customization is achieved by ”hardcoding” additional constraints into the NNR’s architecture and/or training objectives: any change in the desired recommendation behavior thus requires retraining the model with a modified objective. This impedes widespread adoption of multi-aspect news recommenders. In this work, we introduce MANNeR, a modular framework for multi-aspect neural news recommendation that supports on-the-fly customization over individual aspects at inference time. With metric-based learning as its backbone, MANNeR learns aspect-specialized news encoders and then flexibly and linearly combines the resulting aspect-specific similarity scores into different ranking functions, alleviating the need for ranking function-specific retraining of the model. Extensive experimental results show that MANNeR consistently outperforms state-of-the-art NNRs on both standard content-based recommendation and single- and multi-aspect customization. Lastly, we validate that MANNeR’s aspect-customization module is robust to language and domain transfer.

## introduction neural content-based recommenders, trained to infer users' preferences from their click history, represent the state of the art in news recommendation @xcite . while previously consumed content clearly indicates users' preferences, aspects other than content alone, namely categorical features of the news such as topical category, sentiment, news outlet, or stance, contribute to their news consumption decisions. accordingly, some neural news recommenders (nnrs) leverage information on these aspects in addition to text content, be it (i) directly as model input @xcite or (ii) indirectly, as auxiliary training tasks @xcite . increased personalization is often at odds with diversity @xcite . nnrs optimized to maximize congruity to users' preferences tend to produce suggestions highly similar in content to previously consumed news @xcite @xcite . another strand of work thus focuses on increasing diversity of recommendations w.r.t. aspects other than content (e.g., sentiment). to this effect, prior work either (i) re-ranks content-based recommendations to decrease the aspectual similarity between them @xcite , or (ii) trains the nnr model by combining a contentbased personalization objective with an aspectbased diversification objective @xcite @xcite . different users assign different importance to various news aspects (e.g., following developing events requires maximization of content-based overlap with the user's recent history; in another use-case, a user may prefer content-wise diversification of recommendations, but within the same topic of interest). moreover, with personalization and diversification as mutually conflicting goals, users should be able to seamlessly define their own optimal trade-offs between the two. the existing body of work is ill-equipped for such multi-aspect customization, because each set of preferencesi.e., to personalize or diversify for each aspectrequires a different nnr model to be trained from scratch. put differently, forcing global assumptions on personalization and diversification preferences (i.e., same for all users) into the model design and training prevents customization at inference time. contributions. we propose a modular framework for multi-aspect neural news recommendation (manner) to address this limitation. it leverages metric-based contrastive learning to induce a dedicated news encoder for each aspect, starting from a pretrained language model (plm). this way, we obtain linearly-combinable aspect-specific similarity scores for pairs of news, allowing us to define ad-hoc at inference a custom ranking function for each user, reflecting their preferences across all aspects. manner's modular design allows customization for any recommendation objective specified over (i) standard (i.e., content-based) personalization, (ii) aspect-based diversification, and (iii) aspect-based personalization. it also makes man-ner easily extendable: to support personalization and diversification over a new aspect (e.g., news outlet), one only needs to train the aspect-specific news encoder for that aspect. through extensive experiments with topical categories and sentiment as additional aspects next to content itself, we find that manner outperforms state-of-the-art nnrs on standard content-based recommendation. thanks to its module-specific outputs being linearly composable between objectives, we show -without training numerous models with different objectives -that depending on the recommendation goals, one can either (i) vastly increase aspect diversity (e.g., over topics and sentiment) of recommendations or (ii) improve aspect-based personalization, while retaining much of the content-based personalization performance. finally, we demonstrate that manner with a multilingual plm is robust to the (cross-lingual) transfer of aspect-based encoders. ## methodology personalized news recommendation produces for each candidate news n c and user u with corresponding click history @xmath0, n u 2 , ..., n u n }, a relevance score s(n c , u) that quantifies the candidate's relevance for the user. we define an aspect a p as a categorical variable that encodes a news attribute (e.g. its category, stance, sentiment, provider), where each news n i can belong only to one value of a p (e.g. if a p is the topic, then n i may take exactly one value from {politics, sports, ...}). as discussed in §2, aspects are additional dimensions next to content over which to tailor recommendations, whether by (i) personalizing or (ii) diversifying over them. in line with earlier work, we define aspect-based personalization as the level of homogeneity between a user's recommendations and clicked news w.r.t. the distribution of aspect a p . in contrast, we define aspect-based diversity as the level of uniformity of aspect a p 's distribution among the news in the recommendation list. we next introduce our proposed modular framework manner, illustrated in fig. 1 . starting from a plm, during (1) training, we reshape the plm's representation space via contrastive learning, independently for each aspect; this results in one specialized ne for each aspect; at (2) inference, we can, depending on the recommendation task, aggregate the resulting aspect-specific similarity scores to produce a final ranking function. ## experimental setup we compare manner against state-of-the-art nnrs on a range of single-and multi-aspect recommendation tasks. we experiment with two aspects: topical categories (ctg) and news sentiment (snt). baselines. we evaluate several nnrs trained on classification objectives. we follow @xcite and replace the original nes of all baselines that do not use plms (instead, contextualizing word embeddings with convolution or selfattention layers) with the same plm used in man-ner. 1 we include two models optimized purely for content personalization: (1) nrms @xcite , and (2) miner @xcite . we further evaluate seven nnrs that inject aspect information. thereof, five incorporate topical categories, i.e., (3) naml @xcite , (4) lstur @xcite , ( 5 ) mins @xcite , ( 6 ) caum @xcite , ( 7 ) tanr @xcite , and two the news sentiment: (8) sen-tirec @xcite , and ( 9 ) sentidebias @xcite . ## results and discussion we first discuss manner's content personalization performance. we then analyze its capability for single-and multi-aspect (i) diversification and (ii) personalization. in the aspect customization setups, we treat manner's cr-module as a baseline. lastly, we evaluate its ability to re-use pretrained aspect-specific modules in cross-lingual transfer. ## conclusion we proposed manner, a modular framework for multi-aspect neural news recommendation. it learns aspect-specialized nes with supervised contrastive learning, and linearly combines the corresponding aspect-specific similarity scores for final ranking. its modular design allows defining adhoc multi-aspect ranking functions at inference. our experiments show that manner consistently outperforms state-of-the-art nnrs on both (i) standard content-based recommendation, and on singleand multi-aspect (ii) diversification and (iii) personalization of recommendations. moreover, we can identify on-the-fly optimal trade-offs between content-based recommendation performance and aspect-based customization. equipped with a multilingual plm, manner can successfully crosslingually transfer aspect-specific nes. this supports use cases where aspect-specific labels (e.g., sentiment) are not available for news in the target languages of interest. we hope that our work stimulates more research towards modular, easily extendable, and reusable news recommenders.