Paper Session 3: Learning and Optimization

Date: Thursday, Oct 4, 2018, 11:00-12:30
Location: Parq D/E/F
Chair: Dietmar Jannach

  • LPNeural Gaussian Mixture Model for Review-based Rating Prediction
    by Dong Deng, Liping Jing, Jian Yu, Sun Shaolong, Haofei Zhou

    Reviews has been proven to be an important information in recommendation. Different from the overall user-item rating matrix, it can provide textual information that exhibits why a user likes an item or not. Recently, more and more researchers have paid attention on review-based rating prediction. There are two challenging issues: how to extract representative features to characterize users / items from reviews and how to leverage them for recommendation system. In this paper, we propose a Neural Gaussian Mixture Model for review-based rating prediction task (NGMM). Among it, the textual review information is used to construct two parallel neural networks for users and items respectively, so that the users’ preferences and items’ properties can be sufficiently extracted and written as two latent vectors. A shared layer is introduced on the top to couple these two networks together and model user-item rating based on the features learned from reviews. Specifically, each rating is modeled via a Gaussian mixture model, where each Gaussian component has zero variance, the mean described by the corresponding component in user’s latent vector and the weight indicated by the corresponding component in item’s latent vector. Extensive experiments are conducted on five real-world Amazon review datasets. The experimental results have demonstrated that our proposed NGMM model achieves the state-of-the-art performance in review-based rating prediction task.

    Full text in ACM Digital Library

  • LPInteractive Recommendation via Deep Neural Memory Augmented Contextual Bandits
    by Yilin Shen, Yue Deng, Avik Ray, Hongxia Jin

    Personalized recommendation with user interactions has become increasingly popular nowadays in many applications with dynamic change of contents (news, media, etc.). Existing approaches model user interactive recommendation as a contextual bandit problem to balance the trade-off between exploration and exploitation. However, these solutions require a large number of interactions with each user to provide high quality personalized recommendations. To mitigate this limitation, we design a novel deep neural memory augmented mechanism to model and track the history state for each user based on his previous interactions. As such, the user’s preferences on new items can be quickly learned within a small number of interactions. Moreover, we develop new algorithms to leverage large amount of all users’ history data for offline model training and online model fine tuning for each user with the focus of policy evaluation. Extensive experiments on different synthetic and real-world datasets validate that our proposed approach consistently outperforms a variety of state-of-the-art approaches.

    Full text in ACM Digital Library

  • LPOptimally Balancing Receiver and Recommended Users’ Importance in Reciprocal Recommender Systems
    by Akiva Kleinerman, Rosenfeld Ariel, Francesco Ricci, Sarit Kraus

    Online platforms which assist people in finding a suitable partner or match, such as online dating and job recruiting environments, have become increasingly popular in the last decade. Many of these platforms include recommender systems which aim to help users discover other people who will be also interested in them. These recommender systems benefit from contemplating the interest of both sides of the recommended match, however the question of how to optimally balance the interest and the response of both sides remains open. In this study we present a novel recommendation method for recommending people to people. For each user receiving a recommendation, our method finds the optimal balance of two criteria: a) the user’s likelihood to accept the recommendation; and b) the recommended user’s likelihood to positively respond. We extensively evaluate our recommendation method with a group of active users from an operational online dating site. We find that our method is significantly more effective in increasing the number of successful interactions compared to a current state-of-the-art recommendation method.

    Full text in ACM Digital Library

  • SPOHOP-Rec: High-Order Proximity for Implicit Recommendation
    by Jheng-Hong Yang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai

    Recommender systems are vital ingredients for many e-commerce services. In the literature, two of the most popular approaches are based on factorization and graph-based models; the former approach captures user preferences by factorizing the observed direct interactions between users and items, and the latter extracts indirect preferences from the graphs constructed by user-item interactions. In this paper we present HOP-Rec, a unified and efficient method that incorporates the two approaches. The proposed method involves random surfing on a graph to harvest high-order information among neighborhood items for each user. Instead of factorizing a transition matrix, our method introduces a confidence weighting parameter to simulate all high-order information simultaneously, for which we maintain a sparse user-item interaction matrix and enrich the matrix for each user using random walks. Experimental results show that our approach significantly outperforms the state of the art on a range of large-scale real-world datasets.

    Full text in ACM Digital Library

  • LPGeneration Meets Recommendation: Proposing Novel Items for Groups of Users
    by Thanh Vinh Vo, Harold Soh

    Consider a movie studio aiming to produce a set of new movies for summer release: What types of movies it should produce? Who would the movies appeal to? How many movies should it make? Similar issues are encountered by a variety of organizations, e.g., mobile-phone manufacturers and online magazines, who have to create new (non-existent) items to satisfy groups of users with different preferences. In this paper, we present a joint problem formalization of these interrelated issues, and propose novel generative methods that address these questions simultaneously. Specifically, we leverage on the latent space obtained by training a deep generative model—the Variational Autoencoder (VAE)—via a loss function that incorporates both rating performance and item reconstruction terms. We use a greedy search algorithm that utilize this learned latent space to jointly obtain K plausible new items, and user groups that would find the items appealing. An evaluation of our methods on a synthetic dataset indicates that our approach is able to generate novel items similar to highly-desirable unobserved items. As a case study on real-world data, we applied our method to the MART abstract art and Movielens Tag Genome dataset, which resulted in a promising results: small but diverse sets of proposed items.

    Full text in ACM Digital Library

  • LPCalibrated Recommendations
    by Harald Steck

    When a user has watched, say, 70 romance movies and 30 action movies, then it is reasonable to expect the personalized list of recommended movies to be comprised of about 70% romance and 30% action movies as well. This important property is known as calibration, and recently received renewed attention in the context of fairness in machine learning. In the recommended list of items, calibration ensures that the various (past) areas of interest of a user are reflected with their corresponding proportions. Calibration is especially important in light of the fact that recommender systems optimized toward accuracy (e.g., ranking metrics) in the usual offline-setting can easily lead to recommendations where the lesser interests of a user get crowded out by the user’s main interests-which we show empirically as well as in thought-experiments. This can be prevented by calibrated recommendations. To this end, we outline metrics for quantifying the degree of calibration, as well as a simple yet effective re-ranking algorithm for post-processing the output of any recommender system.

    Full text in ACM Digital Library

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