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Personalized News Recommendation Based on Click Behavior Jiahui Liu, Peter Dolan, Elin Rønby Pedersen Google Inc

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Personalized News Recommendation Based on Click Behavior Jiahui Liu, Peter Dolan, Elin Rønby Pedersen Google Inc Personalized News Recommendation Based on Click Behavior Jiahui Liu, Peter Dolan, Elin Rønby Pedersen Google Inc. 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA {jiahui, peterdolan, elinp}@google.com ABSTRACT to thousands of sources via the internet. News aggregation Online news reading has become very popular as the web websites, like Google News and Yahoo! News, collect news provides access to news articles from millions of sources from various sources and provide an aggregate view of around the world. A key challenge of news websites is to news from around the world. A critical problem with news help users find the articles that are interesting to read. In service websites is that the volumes of articles can be this paper, we present our research on developing overwhelming to the users. The challenge is to help users personalized news recommendation system in Google find news articles that are interesting to read. News. For users who are logged in and have explicitly enabled web history, the recommendation system builds Information filtering is a technology in response to this profiles of users’ news interests based on their past click challenge of information overload in general. Based on a behavior. To understand how users’ news interest change profile of user interests and preferences, systems over time, we first conducted a large-scale analysis of recommend items that may be of interest or value to the anonymized Google News users click logs. Based on the user. Information filtering plays a central role in log analysis, we developed a Bayesian framework for recommender systems, as it is able to recommend predicting users’ current news interests from the activities information that has not been rated before and of that particular user and the news trends demonstrated in accommodates the individual differences between users [3, the activity of all users. We combine the information 8]. Information filtering has been applied in various filtering mechanism using learned user profiles with an domains, such as email [16], news [4, 5, 20], and web existing collaborative filtering mechanism to generate search [15, 18]. In the domain of news, this technology personalized news recommendations. The combined particularly aims at aggregating news articles according to method was deployed in Google News. Experiments on the user interests and creating a “personal newspaper” for each live traffic of Google News website demonstrated that the user. combined method improves the quality of news An accurate profile of users' current interests is critical for recommendation and increases traffic to the site. the success of information filtering systems. Some systems [1, 19] require users to manually create and update profiles. Author Keywords This approach places an extra burden on users, something Personalization, user modeling, news trend. very few are willing to take on. Instead, systems can construct profiles automatically from users' interaction with ACM Classification Keywords the system. H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous. In this paper, we describe our research on developing a personalized news recommendation system based on INTRODUCTION profiles learned from user activity in Google News. The News reading has changed with the advance of the World Google News website, available at http://news.google.com, Wide Web, from the traditional model of news is one of the most popular news websites in the world, consumption via physical newspaper subscription to access receiving millions of page views and clicks from users around the world The nature of news reading makes news information Permission to make digital or hard copies of all or part of this work for filtering distinctive from information filtering in other personal or classroom use is granted without fee provided that copies are domains. When visiting a news website, the user is looking not made or distributed for profit or commercial advantage and that copies for new information, information that she did not know bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior before, that may even surprise her. Since user profiles are specific permission and/or a fee. inferred from past user activity, it is important to know how CHI 2009, April 4–9, 2009, Boston, MA, USA. users’ news interests change over time and how effective it Copyright 2009 ACM 978-1-60558-246-7/08/04…$5.00 1 would be to use the past user activities to predict their “Recommended for [account]”, containing stories future behavior. recommended based on her click history in Google News. For our analysis, the recorded click histories were fully To understand this issue, we conducted a large scale log anonymized and kept secure according to the Google analysis of Google News users to measure the stability of Privacy Policy. users’ news interests. We found that their interests do vary over time but follow the aggregate trend of news events. A previous Google News recommendation system was Based on these findings, we develop a Bayesian model to developed using a collaborative filtering method [7]. It predict the news interests of an individual user from the recommends news stories that were read by users with activities of that particular user and the news trend similar click history. This method has two major drawbacks demonstrated in activities of a group of users. To in recommending news stories. First, the system cannot recommend news stories to users, the system takes into recommend stories that have not yet been read by other account of the genuine interests of individual user and the users, a problem that is often referred to as the first-rater current news trend. Therefore, the user will receive news problem [7, 8]. For news recommendations, this is a serious tailored to her interests without missing the important news problem, as news service websites strive to present the most events, even when those events do not strictly match the updated information to users in a timely manner. News user’s particular interests. articles presented in Google News are usually published within one hour. However, the collaborative filtering We combined the news information filtering method with method has to wait several hours to collect enough clicks to the collaborative filtering method previously developed for recommend the news story to users, resulting in undesirable Google News [7] to generate personalized time lags between break-out news and recommendations. recommendations for news access. The combined method Second, not all users are equal to each other, and the was evaluated in a live experiment: a subset of the live collaborative filtering method may not account for the traffic at Google News used the combined method; the individual variability between users [3]. For example, we result showed significant improvement over the existing observed that entertainment news stories are constantly collaborative filtering method. The experiment on live recommended to most of the users, even for those users traffic also revealed a number of interesting issues related who never clicked on entertainment stories. The reason is to recommendations, serendipitous exploration and user that entertainment news stories are generally very popular, satisfaction. We will discuss these issues later in this paper. thus there are always enough clicks on entertainment stories The contribution of this paper is three-fold. First, we report from a user’s “neighbors” to make the recommendation. a large-scale log analysis of the consistency of users' news A solution to these two problems would be to build profiles interest. Second, we propose a novel method for predicting of user’s genuine interests and use them to make news a user’s news interests based on click behavior which recommendations. The profiles would help the system filter combines the genuine interests of the user and the current out the stories that are not of interest to the user, such as the news trend. Third, we combined information filtering and entertainment news mentioned above. A news story may collaborative filtering for personalized news also be recommended to the user if it matches her interest, recommendation and ran an experiment on the live traffic, even if the story has not been clicked on by other users. showing improved results. In this paper, we describe an information filter method to PERSONALIZATION IN GOOGLE NEWS recommend news articles based on their topic categories, Google News is a computer-generated news website that which is assigned by text classifiers. Based on a user’s aggregates headlines from news sources worldwide. It news reading history the information filtering component classifies news articles into different topic categories (e.g. predicts the topic categories of interest to her each time she “world”, “sport”, “entertainment”, etc.) and displays them visit Google News. News articles in those categories are in corresponding sections, as do standard news websites, ranked higher in the candidate list and will be but with fully automated text-based classification. Google recommended to the user. We chose to recommend news News serves millions of users around the world, and stories at the general level of topic categories instead of fine provides numerous editions for different countries and grained topics because of the nature of news reading: most languages. users visit news websites with the attitude of “show me something interesting,” rather than having any specific Users usually visit Google News starting from the information goals [7]. Over-specializing the user profile homepage. The homepage of the standard edition has the may limit the recommendations to news that the user Top Stories section on the top of the homepage, followed already knew, which is obviously undesirable for news by topic based sections of news articles, like “world” and reading. “sport”. The user activities that Google News records are the user’s If a user signs in to her Google Account and explicitly clicks on the Google News website.
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