Paper Session 9: Contextual Challenges

Date: Sunday, Sept 18, 2016, 14:00-15:40
Location: Kresge Auditorium
Chair: Gedas Adomavicius

• PPFThe Contextual Turn: from Context-Aware to Context-Driven Recommender Systems
  by Roberto Pagano, Paolo Cremonesi, Martha Larson, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, Massimo Quadrana

  A critical change has occurred in the status of context in recommender systems. In the past, context has been considered `additional evidence’. This past picture is at odds with many present application domains, where user and item information is scarce. Such domains face continuous cold start conditions and must exploit session rather than user information. In this paper, we describe the `Contextual Turn’: the move towards context-driven recommendation algorithms for which context is critical, rather than additional. We cover application domains, algorithms that promise to address the challenges of context-driven recommendation, and the steps that the community has taken to tackle context-driven problems. Our goal is to point out the commonalities of context-driven problems, and urge the community to address the overarching challenges that context-driven recommendation poses.

  Full text in ACM Digital Library

• LPDiscovering What You’re Known For: A Contextual Poisson Factorization Approach
  by Haokai Lu, James Caverlee, Wei Niu

  Discovering what people are known for is valuable to many important applications such as recommender systems. Unlike an individual’s personal interests, what a user is known for is reflected by the views of others, and is often not easily discerned for a long-tail of the vast majority of users. In this paper, we tackle the problem of discovering what users are known for through a probabilistic model called Bayesian Contextual Poisson Factorization. Moving beyond just modeling user’s content, it naturally models and integrates additional contextual factors, concretely, user’s geo-spatial footprints and social influence, to overcome noisy online activities and social relations. Through GPS-tagged social media datasets, we find that the proposed method can improve known-for prediction performance by 17.5% in precision and 20.9% in recall on average, and that it can capture the implicit relationships between a user’s known-for profile and her content, geo-spatial and social influence.

  Full text in ACM Digital Library

• LP BPNTAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation
  by Hancheng Ge, James Caverlee, Haokai Lu

  We address the challenge of personalized recommendation of high quality content producers in social media. While some candidates are easily identifiable (say, by being “favorited” many times), there is a long-tail of potential candidates for whom we have little evidence. Through careful modeling of contextual factors like the geo-spatial, topical, and social preferences of users, we propose a tensor-based personalized expert recommendation framework that integrates these factors for revealing latent connections between homogeneous entities (e.g., users and users) and between heterogeneous entities (e.g., users and experts). Through extensive experiments over geo-tagged Twitter data, we find that the proposed framework can improve the quality of recommendation by over 30% in both precision and recall compared to the state-of-the-art.

  Full text in ACM Digital Library

• SPAre You Influenced by Others When Rating? Improve Rating Prediction by Conformity Modeling
  by Yiming Liu, Xuezhi Cao, Yong Yu

  Conformity has a strong influence to user behaviors, even in online environment. When surfing online, users are usually flooded with others’ opinions. These opinions implicitly contribute to the user’s ongoing behaviors. However, there is no research work modeling online conformity yet. In this paper, we model user’s conformity in online rating sites. We conduct analysis using real data to show the existence and strength of conformity in these scenarios. We propose a matrix-factorization-based conformity modeling technique to improve the accuracy of rating prediction. Experiments show that our model outperforms existing works significantly (with a relative improvement of 11.72% on RMSE). Therefore, we draw the conclusion that conformity modeling is important for understanding user behaviors and can contribute to further improve the online recommender systems.

  Full text in ACM Digital Library

• SPModelling Contextual Information in Session-Aware Recommender Systems with Neural Networks
  by Bartłomiej Twardowski

  Preparing recommendations for unknown users or such that correctly respond to the short-term needs of a particular user is one of the fundamental problems for e-commerce. Most of the common Recommender Systems assume that user identification must be explicit. In this paper a Session-Aware Recommender System approach is presented where no straightforward user information is required. The recommendation process is based only on user activity within a single session, defined as a sequence of events. This information is incorporated in the recommendation process by explicit context modeling with factorization methods and a novel approach with Recurrent Neural Network (RNN). Compared to the session modeling approach, RNN directly models the dependency of user observed sequential behavior throughout its recurrent structure. The evaluation discusses the results based on sessions from real-life system with ephemeral items (identified only by the set of their attributes) for the task of top-n best recommendations.

  Full text in ACM Digital Library

• SPGetting the Timing Right: Leveraging Category Inter-purchase Times to Improve Recommender Systems
  by Denis Vuckovac, Julia Wamsler, Alexander Ilic, Martin Natter

  In the marketing domain, models of grocery buying behavior consider purchase incidence as a key dimension. However, in recommender systems, timing is often subsumed under contextual information and has received little attention yet. For this reason, we analyze the relation between the timing of a recommendation and its acceptance across different product categories. Our study is based on a real-world deployment of an in-store recommendation system in the brick-and-mortar grocery industry. We base our analysis on transaction data of more than 100,000 unique users and more than four million product recommendations. Our findings suggest that the success of a recommendation significantly depends on the inter-purchase time within the respective category. Different sensitivities across product categories further stress the importance of timing and its interplay with category characteristics within the context of recommender systems. The insights gained in this study enable retailers to improve scheduling recommendations and target promotions more efficiently.

  Full text in ACM Digital Library

• SPMAPS: A Multi Aspect Personalized POI Recommender System
  by Ramesh Baral, Tao Li

  The evolution of the World Wide Web (WWW) and the smart-phone technologies have played a key role in the revolution of our daily life. The location-based social networks (LBSN) have emerged and facilitated the users to share the check-in information and multimedia contents. The Point of Interest (POI) recommendation system uses the check-in information to predict the most potential check-in locations. The different aspects of the check-in information, for instance, the geographical distance, the category, and the temporal popularity of a POI; and the temporal check-in trends, and the social (friendship) information of a user play a crucial role in an efficient recommendation. In this paper, we propose a fused recommendation model termed MAPS (Multi Aspect Personalized POI Recommender System) which will be the first in our knowledge to fuse the categorical, the temporal, the social and the spatial aspects in a single model. The major contribution of this paper are: (i) it realizes the problem as a graph of location nodes with constraints on the category and the distance aspects (i.e. the edge between two locations is constrained by a threshold distance and the category of the locations), (ii) it proposes a multi-aspect fused POI recommendation model, and (iii) it extensively evaluates the model with two real-world data sets.

  Full text in ACM Digital Library

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