Paper Session 6: Applications

Date: Sunday, Sept 18, 2016, 09:40-10:40
Location: Stratton Student Center (Sala 202)
Chair: Robin Burke

• LPDomain-Aware Grade Prediction and Top-n Course Recommendation
  by Asmaa Elbadrawy, George Karypis

  Automated course recommendation can help deliver personalized and effective college advising and degree planning. Nearest neighbor and matrix factorization based collaborative filtering approaches have been applied to student-course grade data to help students select suitable courses. However, the student-course enrollment patterns exhibit grouping structures that are tied to the student and course academic features, which lead to grade data that are not missing at random (NMAR). Existing approaches for dealing with NMAR data, such as Response-aware and context-aware matrix factorization, do not model NMAR data in terms of the user and item features and are not designed with the characteristics of grade data in mind. In this work we investigate how the student and course academic features influence the enrollment patterns and we use these features to define student and course groups at various levels of granularity. We show how these groups can be used to design grade prediction and top-n course ranking models for neighborhood-based user collaborative filtering, matrix factorization and popularity-based ranking approaches. These methods give lower grade prediction error and more accurate top-n course rankings than the other methods that do not take domain knowledge into account.

  Full text in ACM Digital Library

• LPDeep Neural Networks for YouTube Recommendations
  by Paul Covington, Jay Adams, Emre Sargin

  YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.

  Full text in ACM Digital Library

• SPOptimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize Conversion
  by Yuri M Brovman, Marie Jacob, Natraj Srinivasan, Stephen Neola, Daniel Galron, Ryan Snyder, Paul Wang

  This paper tackles the problem of recommendations in eBay’s large semi-structured marketplace. eBay’s variable inventory and lack of structured information about listings makes traditional collaborative filtering algorithms difficult to use. We discuss how to overcome these data limitations to produce high quality recommendations in real time with a combination of a customized scalable architecture as well as a widely applicable machine learned ranking model. A pointwise ranking approach is utilized to reduce the ranking problem to a binary classification problem optimized on past user purchase behavior. We present details of a sampling strategy and feature engineering that have been critical to achieve a lift in both purchase through rate (PTR) and revenue.

  Full text in ACM Digital Library

• SPA Package Recommendation Framework for Trip Planning Activities
  by Idir Benouaret, Dominique Lenne

  Classical recommender systems provide users with ranked lists of recommendations, where each one consists of a single item. However, these ranked lists are not suitable for applications such as trip planning, which deal with heterogeneous items. In this paper, we focus on the problem of recommending a set of packages to the user, where each package is constituted with a set of different Points of Interest that may constitute a tour. Given a collection of POIs, our goal is to recommend the most interesting packages for the user, where each package satisfies the budget constraints. We formally define the problem and we present a novel composite recommendation system, inspired from composite retrieval. Experimental evaluation of our proposed system, using a real-world dataset demonstrates its quality and its ability to improve both diversity and relevance of recommendations.

  Full text in ACM Digital Library

Back to Program