Paper Session 2: From Browser to Buyer: Online Product Recommendations

Date: Wednesday, Oct 3, 2018, 16:00-17:45
Location: Parq D/E/F
Chair: Bracha Shapira

  • LPMultistakeholder Recommendation with Provider Constraints
    by Ozge Surer, Robin Burke, Edward C. Malthouse

    Recommender systems are typically designed to optimize the utility of the end user. In many settings, however, the end user is not the only stakeholder and this exclusive focus may produce unsatisfactory results for the others. One such setting is found in multisided platforms, which act as middlemen bringing together buyers and sellers. In such platforms, it may be necessary to jointly optimize the value for both buyers and sellers. This paper proposes a constraint-based integer programming optimization model, in which different sets of constraints are used to reflect the goals of multiple stakeholders. This model is applied as a post-processing step, so it can easily be added onto an existing recommendation system to make it multistakeholder aware. For computational tractability with larger data sets, we reformulate the integer problem using the Lagrangian dual and use subgradient optimization. In experiments with two data sets, we evaluate empirically the interaction between the utilities of buyers and sellers and show that our approximation can achieve good upper and lower bounds in practical situations.

    Full text in ACM Digital Library

  • LPTranslation-based Factorization Machines for Sequential Recommendation
    by Rajiv Pasricha, Julian McAuley

    Sequential recommendation algorithms aim to predict users’ future behavior given their historical interactions over time. A recent line of work has achieved state-of-the-art performance on sequential recommendation tasks by adapting ideas from metric learning and knowledge-base completion. These algorithms replace inner products with low-dimensional embeddings and distance functions, employing a simple translation dynamic to model user behavior over time.

    In this paper, we propose TransFM, a model that combines translation and metric-based approaches for sequential recommendation with Factorization Machines (FMs). Doing so allows us to reap the benefits of FMs (in particular, the ability to straightforwardly incorporate content-based features), while enhancing the state-of-the-art performance of translation-based models is sequential settings. Specifically, we learn an embedding and translation space for each feature dimension, replacing the inner product with the squared Euclidean distance to measure interaction strength between features. Like FMs, we show that the model equation for TransFM can be computed in linear time and optimized using classical techniques. As TransFM operates on arbitrary feature vectors, additional content information can be easily incorporated without significant changes to the model itself. Empirically, the performance of TransFM significantly increases when taking content features into account, outperforming state-of-the-art models on the sequential recommendation task for a wide variety of datasets.

    Full text in ACM Digital Library

  • SPOExploring Recommendations Under User-Controlled Data Filtering
    by Hongyi Wen, Longqi Yang, Michael Sobolev, Deborah Estrin

    Traditionally, recommendation systems are built on the assumption that each service provider has full access to all user data generated on its platform. However, with increasing data privacy concerns and personal data protection regulation, service providers, such as Google, Twitter, and Facebook, are enabling their users to revisit, erase, and rectify their historical profiles. Future recommendation systems need to be robust to such profile modifications and user-controlled data filtering.

    In this paper, we explore how recommendation performance may be affected by time-sensitive user data filtering, i.e., users choosing to share only recent “N days” of data. Using the MovieLens dataset as a testbed, we evaluate three state-of-the-art collaborative filtering algorithms. Our experiments demonstrate that filtering out historical user data does not significantly affect the overall recommendation performance, but its impact on individual users may vary. These findings challenge the common belief that more data produces better performance, and suggest a potential win-win solution for services and end users.

    Full text in ACM Digital Library

  • LPQuality-Aware Neural Complementary Item Recommendation
    by Yin Zhang, Haokai Lu, Wei Niu, James Caverlee

    Complementary item recommendation finds products that go well with one another (e.g., a camera and a specific lens). While they are ubiquitous, the dimensions by which items go together can vary by both product and category, making it difficult to detect complementary items at scale. Moreover, in practice, user preferences for complementary items can be complex combinations of item quality and evidence of complementarity. Hence, we propose a new neural complementary recommender Encore that can jointly learn complementary item relationships and user preferences. Specifically, Encore (i) effectively combines and balances both stylistic and functional evidence of complementary items across item categories; (ii) naturally models item latent quality for complementary items through Bayesian inference of customer ratings; and (iii) builds a novel neural network model to learn the complex (non-linear) relationships between items for flexible and scalable complementary product recommendations. Through experiments over large Amazon datasets, we find that Encore effectively learns complementary item relationships, leading to an improvement in accuracy of 15.5% on average versus the next-best alternative.

    Full text in ACM Digital Library

  • LPItem Recommendation on Monotonic Behavior Chains
    by Mengting Wan, Julian McAuley

    ‘Explicit’ and ‘implicit’ feedback in recommender systems have been studied for many years, as two relatively isolated areas. However many real-world systems involve a spectrum of both implicit and explicit signals, ranging from clicks and purchases, to ratings and reviews. A natural question is whether implicit signals (which are dense but noisy) might help to predict explicit signals (which are sparse but reliable), or vice versa. Thus in this paper, we propose an item recommendation framework which jointly models this spectrum of interactions. Our main observation is that in many settings, feedback signals exhibit monotonic dependency structures, i.e., any signal necessarily implies the presence of a weaker (or more implicit) signal (a ‘review’ action implies a ‘purchase’ action, which implies a ‘click’ action, etc.). We refer to these structures as ‘monotonic behavior chains,’ for which we develop new algorithms that exploit these dependencies. Using several new and existing datasets that exhibit a variety of feedback types, we demonstrate the quantitative performance of our approaches. We also perform qualitative analysis to uncover the relationships between different stages of implicit vs. explicit signals.

    Full text in ACM Digital Library

  • LPDeep Reinforcement Learning for Page-wise Recommendations
    by Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang

    Recommender systems can mitigate the information overload problem by suggesting users’ personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is — users are recommended a page of items and provide feedback; and then the system recommends a new page of items. To effectively capture such interaction for recommendations, we need to solve two key problems — (1) how to update recommending strategy according to user’s real-time feedback, and 2) how to generate a page of items with proper display, which pose tremendous challenges to traditional recommender systems. In this paper, we study the problem of page-wise recommendations aiming to address aforementioned two challenges simultaneously. In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.

    Full text in ACM Digital Library

  • LPCausal Embeddings for Recommendation
    by Stephen Bonner, Flavian Vaslie

    Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website. This results in a gap between the final recommendation objective and the classical setup where recommendation candidates are evaluated by their coherence with past user behavior, by predicting either the missing entries in the user-item matrix, or the most likely next event. To bridge this gap, we optimize a recommendation policy for the task of increasing the desired outcome versus the organic user behavior. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy. To this end, we propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization methods and new approaches of causal recommendation and show significant improvements.

    Full text in ACM Digital Library

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