Accuracy meets Diversity in a News Recommender System (2022)

Abstract: News recommender systems face certain challenges. These challenges arise due to evolving users’ preferences over dynamically created news articles. The diversity is necessary for a news recommender system to expose users to a variety of information. We propose a deep neural network based on a two-tower architecture that learns news representation through a news item tower and users’ representations through a query tower. We customize an augmented vector for each query and news item to introduce information interaction between the two towers. We introduce diversity in the proposed architecture by considering a category loss function that aligns items’ representation of uneven news categories. Experimental results on two news datasets reveal that our proposed architecture is more effective compared to the state-of-the-art methods and achieves a balance between accuracy and diversity.

## introduction reading the news has never been more common in people's daily lives than it is now. big names like yahoo!, google, and cnn have launched online news portals that users can access from anywhere to browse various news categories and find up-todate information. however, finding the right content is a challenge. with so much information available online, selecting relevant news has become a time-consuming and challenging task. a news recommender system (nrs) offers solutions to the information overload problem and provides relevant and interesting news recommendations to users @xcite . in the state-of-the-art of nrs, the news stories that users have read in the past are used to infer their interests and preferences. however, there is usually frequent content updating in a news domain. we show the news reading behaviour of a typical user in figure 1 as an example. we see in figure 1 that this user reads about the 'covid-19 cases in ontario' during time t1, and then read about 'canadian children 5 to 11 could become eligible for covid-19 vaccine…" during t2, and then his following action (read/click) is reading the news 'covid-19 travel ban on tourists' during t3. we also observe that there is a transition in user preferences to other topics, for example, the elections, and politics in the later time. this shows that a user in an nrs generally changes his/her interests over time. some of the user interests are long-term which shows a habit or personality, while some of the user interests are short-term, which may erupt due to some trending news, a sudden interest in an event and so. it is important to consider the accuracy as well as the diversity of news items while providing recommendations to users in an nrs @xcite . this is a motivation for this research. a common practice in the recommender systems is to design a two-tower architecture @xcite , where, first, a retrieval model retrieves a subset of related items from a large corpus in response to a user's query, and, then a ranking model ranks the retrieved items based on users' actions (clicks or ratings). the quality of retrieved items plays a critical role in the retrieval stage. the retrieval and ranking mechanism is also used in two-tower architectures @xcite . in a typical two-tower architecture @xcite @xcite , there is usually no mechanism for the information interaction between the two towers. usually, the accuracy meets diversity in a news recommender system shaina raza 1* , syed raza bashir 2 , usman naseem 3 items or users are represented by their ids or titles (for example, movie id, news id, user id) to represent each tower. by missing the chance to include rich information related to news items (e.g., news body, categories, etc.) or user contexts (e.g., a situation when the user interacts with the news, such as time, and place) in the two-tower architecture, we are providing recommendations that are not quite relevant to users' preferences. an issue with a typical recommender system is that they only focus on the relevancy of users' preferences and there is usually no consideration of diversity aspects while making recommendations @xcite . for example, if we consider a real-time news recommendation scenario, we find that there are different categories of news (for example, sports, entertainment, politics, weather, and such), and the number of news items in each category varies (i.e., are imbalanced). as a result, the items in a single news category may account for most news recommendations. consequently, the news recommendations that are produced may be too narrow and users' diversified interests are not addressed. to address the above issues (incorporating rich user-item interactions and diversity of recommendations), we present a novel two-tower architecture for an nrs. we summarize our contributions as:  we present a two-tower architecture for nrs and supplement the item tower with rich side information (meta-data) related to news items. we also consider the user context(s) in the query tower. we represent each user query by an augmented vector, which consists of the user's query and the news item features. the augmented vector is then updated based on the output representation vector of the other tower for a positive sample. in this way, the augmented vector implicitly models the information interaction between the two towers. extensive experiments on two news datasets show that our proposed approach has two major advantages: (i) it provides deeper insights into the information interaction of two-tower models in an nrs, and (ii) it provides diversified news recommendations (along with relevant recommendations) from a variety of news categories. our goal is to provide news recommendations that are relevant to users' past preferences (interests) and are diversified at the same time ## related work deep learning has demonstrated great success in recommender systems, such as in movie recommendations (wearenetflix, 2018), social networks @xcite and many other application domains. recommending news is particularly challenging @xcite . this is because of the dynamic nature of the news domain and changing users' preferences. the stateof-the-art nrss @xcite @xcite has shown tremendous performance in both academia and industry, however, a few challenges need to be addressed. first, these models do not extract enough news data from a reader's history. there are many pieces of information, other than news id or title, that may be more descriptive (e.g., the news story) or better reflect a reader's interests (e.g., topics, categories) than titles or ids. second, the focus in these methods is usually on the relevancy and not on the diversity aspect. some works consider the two-tower deep neural networks to learn representation from content features in language models @xcite . these twotower models are also used in recommender systems to leverage content features on the item side @xcite . nevertheless, these models are usually focused on the relevancy of retrieved items, which is appreciated, however, they are not used to address the diversity aspect. in this work, we use a two-tower architecture for an nrs and try to incorporate both accuracy (relevancy) and diversity of news items while making recommendations to the users. maximal marginal relevance (mmr) is a classical technique to increase the diversity of documents retrieved against a query in an information retrieval system. mrr is also used in recommender systems to include diversity during the re-ranking phase of recommended items @xcite . some work @xcite considers diversity in the recommendation process by using the regularization terms on items' feature space. dueling bandit gradient descent (dbgd) @xcite is an online learningto-rank algorithm based on multi-arm bandit algorithms and is used to model the explorationversus-exploitation trade-off for relative feedback. dbgd is recently used in a state-of-the-art nrs @xcite to improve recommendation diversity. however, the learning efficiency of this model is limited in high-dimensional parameter space. second, this method assumes only binary feedback because there is no way of directly observing the reward of users' actions. in this work, we also consider diversity during the model training time, but we use a category loss function for this purpose. our intuition is that news items under different news categories are highly imbalanced and the recommendations are usually produced considering one such major category. we try to provide recommendations by considering news items from a diverse set of news categories. ## conclusion this paper proposes a two-tower architecture to model the information interaction between the two towers (query and news items). extensive experiments on two datasets show the better performance of the proposed approach in achieving a balance between accuracy and diversity. in future, we like to conduct experiments on more real-world news data and make a deeper neural network. we like to evaluate our approach using more diversity metrics, such as normalized topic coverage and novelty. we also like to include more users' feedback like click-through rate.