Paper Session 11: Algorithms II

Date: Monday, Sept 19, 2016, 10:40-12:20
Location: Kresge Auditorium
Chair: Alexandros Karatzoglou

• LPPersonalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach
  by Rose Catherine, William Cohen

  Improving the performance of recommender systems using knowledge graphs is an important task. There have been many hybrid systems proposed in the past that use a mix of content-based and collaborative filtering techniques to boost the performance. More recently, some work has focused on recommendations that use external knowledge graphs (KGs) to supplement content-based recommendation. In this paper, we investigate three methods for making KG based recommendations using a general-purpose probabilistic logic system called ProPPR. The simplest of the models, EntitySim, uses only the links of the graph. We then extend the model to TypeSim that also uses the types of the entities to boost its generalization capabilities. Next, we develop a graph based latent factor model, GraphLF, which combines the strengths of latent factorization with graphs. We compare our approaches to a recently proposed state-of-the-art graph recommendation method on two large datasets, Yelp and MovieLens-100K. The experiments illustrate that our approaches can give large performance improvements. Additionally, we demonstrate that knowledge graphs give maximum advantage when the dataset is sparse, and gradually become redundant as more training data becomes available, and hence are most useful in cold-start settings.

  Full text in ACM Digital Library

• LPEfficient Bayesian Methods for Graph-based Recommendation
  by Ramon Lopes, Renato Assunção, Rodrygo L. T. Santos

  Short-length random walks on the bipartite user-item graph have recently been shown to provide accurate and diverse recommendations. Nonetheless, these approaches suffer from severe time and space requirements, which can be alleviated via random walk sampling, at the cost of reduced recommendation quality. In addition, these approaches ignore users’ ratings, which further limits their expressiveness. In this paper, we introduce a computationally efficient graph-based approach for collaborative filtering based on short-path enumeration. Moreover, we propose three scoring functions based on the Bayesian paradigm that effectively exploit distributional aspects of the users’ ratings. We experiment with seven publicly available datasets against state-of-the-art graph-based and matrix factorization approaches. Our empirical results demonstrate the effectiveness of the proposed approach, with significant improvements in most settings. Furthermore, analytical results demonstrate its efficiency compared to other graph-based approaches.

  Full text in ACM Digital Library

• LPUsing Navigation to Improve Recommendations in Real-Time
  by Chao-Yuan Wu, Christopher V Alvino, Alexander J Smola, Justin Basilico

  Implicit feedback is a key source of information for many recommendation and personalization approaches. However, using it typically requires multiple episodes of interaction and roundtrips to a recommendation engine. This adds latency and neglects the opportunity of immediate personalization for a user while the user is navigating recommendations. We propose a novel strategy to address the above problem in a principled manner. The key insight is that as we observe a user’s interactions, it reveals much more information about her desires. We exploit this by inferring the within-session user intent on-the-fly based on navigation interactions, since they offer valuable clues into a user’s current state of mind. Using navigation patterns and adapting recommendations in real-time creates an opportunity to provide more accurate recommendations. By prefetching a larger amount of content, this can be carried out entirely in the client (such as a browser) without added latency. We define a new Bayesian model with an efficient inference algorithm. We demonstrate significant improvements with this novel approach on a real-world, large-scale dataset from Netflix on the problem of adapting the recommendations on a user’s homepage.

  Full text in ACM Digital Library

• LPBayesian Low-Rank Determinantal Point Processes
  by Mike Gartrell, Ulrich Paquet, Noam Koenigstein

  Determinantal point processes (DPPs) are an emerging model for encoding probabilities over subsets, such as shopping baskets, selected from a ground set, such as an item catalog. They have recently proved to be appealing models for a number of machine learning tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. Prior work has shown that using a low-rank factorization of this kernel provides scalability improvements that open the door to training on large-scale datasets and computing online recommendations, both of which are infeasible with standard DPP models that use a full-rank kernel. A low-rank DPP model can be trained using an optimization-based method, such as stochastic gradient ascent, to find a point estimate of the kernel parameters, which can be performed efficiently on large-scale datasets. However, this approach requires careful tuning of regularization parameters to prevent overfitting and provide good predictive performance, which can be computationally expensive. In this paper we present a Bayesian method for learning a low-rank factorization of this kernel, which provides automatic control of regularization. We show that our Bayesian low-rank DPP model can be trained efficiently using stochastic gradient Hamiltonian Monte Carlo (SGHMC). Our Bayesian model generally provides better predictive performance on several real-world product recommendation datasets than optimization-based low-rank DPP models trained using stochastic gradient ascent, and better performance than several state-of-the art recommendation methods in many cases.

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• SPRecommending Repeat Purchases using Product Segment Statistics
  by Suvodip Dey, Pabitra Mitra, Kratika Gupta

  Repeat Purchases have become increasingly important in measuring customer’s satisfaction and loyalty to e-commerce websites in regard to online shopping. In this paper, we first propose a model for estimating repeat purchase frequency in a given time period from a given product category using Poisson/Gamma model. Second, we estimate the purchase probabilities of different product types in a product category for each customer using Dirichlet model. Experimental results on data collected by a real-world e-commerce website show that it can predict a user’s average repeat purchase frequency along with their product types with decent accuracy. We also argue that the output of our models can be used as prior information to enhance the performance of time-sensitive recommendation.

  Full text in ACM Digital Library

• SPBayesian Personalized Ranking with Multi-Channel User Feedback
  by Babak Loni, Roberto Pagano, Martha Larson, Alan Hanjalic

  Pairwise learning-to-rank algorithms have been shown to allow recommender systems to leverage unary user feedback. We propose Multi-feedback Bayesian Personalized Ranking (MF-BPR), a pairwise method that exploits different types of feedback with an extended sampling method. The feedback types are drawn from different “channels”, in which users interact with items (e.g., clicks, likes, listens, follows, and purchases). We build on the insight that different kinds of feedback, e.g., a click versus a like, reflect different levels of commitment or preference. Our approach differs from previous work in that it exploits multiple sources of feedback simultaneously during the training process. The novelty of MF-BPR is an extended sampling method that equates feedback sources with “levels” that reflect the expected contribution of the signal. We demonstrate the effectiveness of our approach with a series of experiments carried out on three datasets containing multiple types of feedback. Our experimental results demonstrate that with a right sampling method, MF-BPR outperforms BPR in terms of accuracy. We find that the advantage of MF-BPR lies in its ability to leverage level information when sampling negative items.

  Full text in ACM Digital Library

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