Session: User Modeling

Date: Monday September 19, 11:00 AM – 12:30 PM (PDT)

  • PAExploring the longitudinal effect of nudging on users’ genre exploration behavior and listening preference
    by Yu Liang (’s-Hertogenbosch, Netherlands), Martijn C. Willemsen (Eindhoven University of Technology, Netherlands, Jheronimus Academy of Data Science, Netherlands)

    Previous studies on exploration have shown that users can be nudged to explore further away from their current preferences. However, these effects were shown in a single session study, while it often takes time to explore new tastes and develop new preferences. In this work, we present a longitudinal study on users’ exploration behavior and behavior change over time after they have used a music genre exploration tool for four sessions in six weeks. We test two relevant nudges to help them explore more: the starting point (the personalization of the default initial playlist) and the visualization of users’ previous position(s). Our results show that the personalization level of the default initial playlist in the first session influences the preferred personalization level users set in the second session but fades away in later sessions as users start exploring in different directions. Visualization of users’ previous positions did not nudge users to stay closer to the initial defaults. Over time, users perceive the playlist to be more personalized to their tastes and helpful to explore the genre and helpfulness increases when they explore further away from their current preferences. Apart from differences in self-reported measures, we also find some objective evidence for preference change in users’ top tracks from their Spotify profile, that over the period of 6 weeks move somewhat closer to the genre that users selected to explore with the tool.

    Full text in ACM Digital Library

  • PAModeling User Repeat Consumption Behavior for Online Novel Recommendation
    by Yuncong Li (Tencent, China), Cunxiang Yin (Tencent, China), yancheng he (Tencent, China), Guoqiang Xu (Tencent, China), Jing Cai (tencent, China), leeven luo (technology zone, China), Sheng-hua Zhong (Shenzhen University, China)

    Given a user’s historical interaction sequence, online novel recommendation suggests the next novel the user may be interested in. Online novel recommendation is extremely important but underexplored. In this paper, we concentrate on recommending online novels to new users of an online novel reading platform, whose first visits to the platform occurred in the last seven days. We have two observations about online novel recommendation for new users. First, repeat novel consumption of new users is a common phenomenon. Second, interactions between users and novels are informative. To accurately predict whether a user will reconsume a novel, it is crucial to characterize each interaction at a fine-grained level. Based on these two observations, we propose a neural network for online novel recommendation, called NovelNet. NovelNet can recommend the next novel from both the user’s consumed novels and new novels simultaneously. Specifically, an interaction encoder is used to obtain accurate interaction representation considering fine-grained attributes of interaction, and a pointer network with a pointwise loss is incorporated into NovelNet to recommend previously-consumed novels. Moreover, an online novel recommendation dataset is built from a well-known online novel reading platform. Experimental results on the dataset demonstrate the effectiveness of NovelNet and the dataset will be released for public use as a benchmark.

    Full text in ACM Digital Library

  • PAA User-Centered Investigation of Personal Music Tours
    by Giovanni Gabbolini (University College Cork, Ireland), Derek Bridge (University College Cork, Ireland)

    Streaming services use recommender systems to surface the right music to users. Playlists are a popular way to present music in a list-like fashion, i.e. as a plain list of songs. An alternative are tours, where the songs alternate with segues, which explain the connections between consecutive songs. Tours address the user need of seeking background information about songs, and are found to be superior to playlists, given the right user context. In this work, we provide, for the first time, a user-centered evaluation of two tour-generation algorithms (Greedy and Optimal) using semi-structured interviews. We assess the algorithms, we discuss attributes of the tours that the algorithms produce, we identify which attributes are desirable and which are not, and we enumerate several possible improvements to the algorithms, along with practical suggestions on how to implement the improvements.
    Our main findings are that Greedy generates more likeable tours than Optimal, and that three important attributes of tours are segue diversity, song arrangement and song familiarity. More generally, we provide insights into how to present music to users, which could inform the design of user-centered recommender systems.

    Full text in ACM Digital Library

  • INPersonalizing Benefits Allocation Without Spending Money
    by Dmitri Goldenberg (, Israel)

    Modern e-commerce platforms make use of promotional offers such as discounts and rewards to encourage customers to complete purchases. While offering the promotions has a great effect on the sales, it also generates a monetary loss. By utilizing causal machine learning and optimization, our team at was able to personalize the promotions allocation to customers, while efficiently controlling the spend within a given budget.
    In this talk we’ll share the personalized promotion assignment techniques, such as uplift modeling and constrained optimization, which helped us to predict the outcomes of discounts offering and allocate them efficiently. This solution allowed us to unlock promotional campaigns to bring more value to the customers and grow our business.

    Full text in ACM Digital Library

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