Paper Session 7: Beyond Users and Items

Date: Friday, Oct 5, 2018, 14:00-16:00
Location: Parq D/E/F
Chair: Linas Baltrunas

  • LPRecurrent Knowledge Graph Embedding for Effective Recommendation
    by Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, LongKai Huang, Chi Xu

    Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs, which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation shows the superiority of RKGE against the state-of-the-art. Furthermore, we show that RKGE provides meaningful explanations for recommendation results.

    Full text in ACM Digital Library

  • LPSpectral Collaborative Filtering
    by Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, Philip Yu

    Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start problem, which has a significantly negative impact on users’ experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the spectral domain, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the spectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the cold-start problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the spectral domains of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly outperforms state-of-the-art models. Code and data are available at https://github.com/anonymous121212/SpectralCF.

    Full text in ACM Digital Library

  • LPCategorical-Attributes-Based Item Classification for Recommender Systems
    by Qian Zhao, Jilin Chen, Minmin Chen, Sagar Jain, Alex Beutel, Francois Belletti, Ed Chi

    Many techniques to utilize side information of users and/or items as inputs to recommenders to improve recommendation, especially on cold-start items/users, have been developed over the years. In this work, we test the approach of utilizing item side information, specifically categorical attributes, in the output of recommendation models either through multi-task learning or hierarchical classification. We first demonstrate the efficacy of these approaches for both matrix factorization and neural networks with a medium-size real-word data set. We then show that they improve a neural-network based production model in an industrial-scale recommender system. We demonstrate the robustness of the hierarchical classification approach by introducing noise in building the hierarchy. Lastly, we investigate the generalizability of hierarchical classification on a simulated dataset by building two user models in which we can fully control the generative process of user-item interactions.

    Full text in ACM Digital Library

  • LPEliciting Pairwise Preferences in Recommender Systems
    by Saikishore Kalloori, Francesco Ricci, Rosella Gennari

    Preference data in the form of ratings or likes for items are widely used in many Recommender Systems (RSs). However, previous research has shown that even item comparisons, which generate pairwise preference data, can be used to model user preferences. Moreover, pairwise preferences can be effectively combined with ratings to compute recommendations. In such hybrid approaches, the RS requires to elicit from the user both types of preference data. In this work, we aim at identifying how and when to elicit pairwise preferences, i.e., when this form of user preference data is more meaningful for the user to express and more beneficial for the system. We conducted an online A/B test and compared a rating-only based system variant with another variant that allows the user to enter both types of preferences. Our results demonstrate that pairwise preferences are valuable and useful especially when the user is focusing on a specific type of items and by incorporating pairwise preferences, the system can generate better recommendations than a state of the art rating-only based solution. Additionally, our results indicate that there exists a dependency between the user’s personality and the perceived system usability and the satisfaction for the preference elicitation procedure, which varies if only ratings or a combination of ratings and pairwise preferences are elicited.

    Full text in ACM Digital Library

  • LPAdaptive Collaborative Topic Modeling for Online Recommendation
    by Marie Al-Ghossein, Pierre-Alexandre Murena, Talel Abdessalem, Anthony Barré, Antoine Cornuéjols

    Collaborative filtering (CF) mainly suffers from rating sparsity and from the cold-start problem. Auxiliary information like texts and images has been leveraged to alleviate these problems, resulting in hybrid recommender systems (RS). Due to the abundance of data continuously generated in real-world applications, it has become essential to design online RS that are able to handle user feedback and the availability of new items in real-time. These systems are also required to adapt to drifts when a change in the data distribution is detected. In this paper, we propose an adaptive collaborative topic modeling approach, CoAWILDA, as a hybrid system relying on adaptive online Latent Dirichlet Allocation (AWILDA) to model new available items arriving as a document stream and incremental matrix factorization for CF. The topic model is maintained up-to-date in an online fashion and is retrained in batch when a drift is detected, using documents automatically selected by an adaptive windowing technique. Our experiments on real-world datasets prove the effectiveness of our approach for online recommendation.

    Full text in ACM Digital Library

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