Paper Session 4: Session-Based RS

Date: Tuesday, Aug 29, 2017, 11:00-12:30
Location: Main Room
Chair: Bracha Shapira

  • LPRecommending Personalised News in Short User Sessions by Elena Viorica Epure, Benjamin Kille, Jon Espen Ingvaldsen, Rebecca Deneckere, Camille Salinesi and Sahin Albayrak

    News organizations employ personalized recommenders to target news articles to specific readers and thus foster engagement. Existing approaches rely on extensive user profiles. However, though often available, authentication is a rare choice of readers consulting news publishers’ websites. This paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency and the dynamics of reading behavior. We extent existing research on the dynamics of news reading behavior by focusing not only on the progress of reading interests over time but also on their relations. Reading interests are considered in three categories: short, medium and long-term. Combinations of these are evaluated in terms of added value to the recommendation’s performance and ensured news variety. The experiments with 17-month data logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Also, recommendations based on short-term with long-term interests result in increased accuracy while recommendations based on short-term with medium-term interests yield a higher news variety.

  • LPPersonalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks by Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi and Paolo Cremonesi

    Session-based recommendations are an important problem in many recommendation settings (e.g. e-commerce). Recurrent Neural Networks have recently been shown to perform very well in this setting. While in many session-based recommendation domains user identifiers are hard to come by there are also settings in which these profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information and devise a hierarchical RNN model that transfers end evolves the hidden states of the RNN’s across user sessions. Results on two industry datasets show large improvements over the session-only RNN’s.

  • LP3D Convolutional Networks for Session-based Recommendation with Content Features by Trinh Xuan Tuan and Tu Minh Phuong

    In many real-life recommendation settings, user profiles and past activities are not available. The recommender system should make predictions based on session data, e.g. session clicks and descriptions of clicked items. Conventional recommendation approaches, which rely on user-item interaction data, cannot deliver accurate results in these situations. In this paper, we describe a method that combines session clicks and content features such as item descriptions and item categories to generate recommendations. To model these data, which are usually of different types and nature, we use 3-dimensional convolutional neural networks with character-level encoding of all input data. While 3D architectures provide a natural way to capture spatio-temporal patterns, character-level networks allow modeling different data types using their raw textual representation, thus reducing feature engineering effort. We applied the proposed method to predict add-to-cart events in e-commerce websites, which is more difficult then just predicting next clicks. On two real data sets, our method outperformed several baselines and a state-of-the-art method based on recurrent neural networks.

  • SPModeling User Session and Intent with an Attention-based Encoder-Decoder Architecture by Pablo Loyola, Chen Liu and Yu Hirate

    We propose an encoder-decoder neural architecture to model user session and intent using browsing and purchasing data from a large e-commerce company.

    We begin by identifying the source-target transition pairs between items within each session. Then, the set of source items are passed through an encoder, whose learned representation is used by the decoder to estimate the sequence of target items. Therefore, as this process is performed pair-wise, we hypothesize that the model could capture the transition regularities in a more fine grained way. Additionally, our model incorporates an attention mechanism to explicitly learn the more expressive portions of the sequences in order to improve performance. Besides modeling the user sessions, we also extended the original architecture by means of attaching a second decoder that is jointly trained to predict, for each user, the intent of her next session. With this, we want to explore to what extent the model can capture inter session dependencies.

    We performed an empirical study comparing against several baselines on a large real world dataset, showing that our approach is competitive in both item and intent prediction.

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